diff --git a/reports/templates/cairo_run_results.ipynb b/reports/templates/cairo_run_results.ipynb new file mode 100644 index 0000000..3f4e7af --- /dev/null +++ b/reports/templates/cairo_run_results.ipynb @@ -0,0 +1,2032 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a0000001", + "metadata": {}, + "source": [ + "# CAIRO run results review\n", + "\n", + "This **state-agnostic** notebook reviews the outputs from the first four CAIRO simulation\n", + "runs for any given state and batch. Run it after a new batch is complete to sanity-check\n", + "inputs, verify bill arithmetic, and get an initial read on cross-subsidization and\n", + "bill-change patterns.\n", + "\n", + "The first four runs (covering two upgrade levels \u00d7 two supply configurations) produce\n", + "the master tables this notebook reads:\n", + "\n", + "| Run pair | Upgrade | What it represents |\n", + "|----------|---------|--------------------|\n", + "| 1+2 | Upgrade 00 (baseline) | Current HVAC; delivery-only + delivery+supply combined |\n", + "| 3+4 | Upgrade 02 (HP upgrade) | All buildings given heat pumps; same two supply configs |\n", + "\n", + "> **Demonstration state**: This notebook currently uses NY batch\n", + "> `ny_20260417a_r1-36` as a worked example. \n", + "> **To review a different state, change `STATE` and `BATCH` in the Parameters cell\n", + "> and restart the kernel.**\n", + "\n", + "---\n", + "\n", + "## How to use for a new state\n", + "\n", + "1. Change `STATE` (lowercase 2-letter abbreviation, e.g. `\"ri\"`, `\"ct\"`) and `BATCH`\n", + " in the *Parameters* cell below.\n", + "2. Verify that `run_1+2` and `run_3+4` exist under\n", + " `s3://data.sb/switchbox/cairo/outputs/hp_rates//all_utilities//`.\n", + "3. Restart the kernel and run all cells (`Kernel \u2192 Restart & Run All`).\n", + "4. No other code changes are needed \u2014 all paths and column references are derived\n", + " from `STATE` and `BATCH`.\n", + "\n", + "---\n", + "\n", + "## Notebook sections\n", + "\n", + "| Section | What it covers |\n", + "|---------|---------------|\n", + "| **1 \u2014 Load data** | Read master bills and BAT tables from S3 for both run pairs |\n", + "| **2 \u2014 Input data quality checks (EDA)** | Utility assignments, heating-type composition, gas and electric spending distributions |\n", + "| **3 \u2014 Structural quality checks** | Schema validation, row counts, bill arithmetic identities |\n", + "| **4 \u2014 Baseline analysis (upgrade 00)** | Cross-subsidy table, BAT distribution, monthly bill pattern |\n", + "| **5 \u2014 Bill change analysis (upgrade 00 \u2192 02)** | Quadrant bar chart, savings breakdown by heating type |" + ] + }, + { + "cell_type": "markdown", + "id": "a0000002", + "metadata": {}, + "source": [ + "## Parameters\n", + "\n", + "Change `STATE` and `BATCH` here to switch states. Everything else derives from these\n", + "two values." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "a0000003", + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'coord_polar' from 'plotnine' (/ebs/home/lee_switch_box/reports2/.venv/lib/python3.13/site-packages/plotnine/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[45]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpolars\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpl\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIPython\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m display\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplotnine\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 9\u001b[39m aes,\n\u001b[32m 10\u001b[39m coord_flip,\n\u001b[32m 11\u001b[39m coord_polar,\n\u001b[32m 12\u001b[39m element_text,\n\u001b[32m 13\u001b[39m facet_wrap,\n\u001b[32m 14\u001b[39m geom_bar,\n\u001b[32m 15\u001b[39m geom_col,\n\u001b[32m 16\u001b[39m geom_hline,\n\u001b[32m 17\u001b[39m geom_text,\n\u001b[32m 18\u001b[39m ggplot,\n\u001b[32m 19\u001b[39m guides,\n\u001b[32m 20\u001b[39m labs,\n\u001b[32m 21\u001b[39m position_dodge,\n\u001b[32m 22\u001b[39m position_stack,\n\u001b[32m 23\u001b[39m scale_fill_manual,\n\u001b[32m 24\u001b[39m scale_x_discrete,\n\u001b[32m 25\u001b[39m scale_y_continuous,\n\u001b[32m 26\u001b[39m theme,\n\u001b[32m 27\u001b[39m )\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mplotnine\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SB_COLORS, theme_switchbox\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'coord_polar' from 'plotnine' (/ebs/home/lee_switch_box/reports2/.venv/lib/python3.13/site-packages/plotnine/__init__.py)" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "import math\n", + "from typing import cast\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import polars as pl\n", + "from IPython.display import display\n", + "from plotnine import (\n", + " aes,\n", + " coord_flip,\n", + " facet_wrap,\n", + " geom_col,\n", + " geom_hline,\n", + " geom_text,\n", + " ggplot,\n", + " guides,\n", + " labs,\n", + " position_dodge,\n", + " position_stack,\n", + " scale_fill_manual,\n", + " scale_x_discrete,\n", + " scale_y_continuous,\n", + " theme,\n", + ")\n", + "\n", + "from lib.plotnine import SB_COLORS, theme_switchbox" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000004", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reviewing NY batch: ny_20260417a_r1-36\n" + ] + } + ], + "source": [ + "# \u2500\u2500 Change these two values to review a different state or batch \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", + "STATE = \"ny\" # lowercase state abbreviation, e.g. \"ri\", \"ct\", \"ma\"\n", + "BATCH = \"ny_20260417a_r1-36\"\n", + "# \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", + "\n", + "S3_BASE = \"s3://data.sb/switchbox/cairo/outputs/hp_rates\"\n", + "RUN_PAIRS: dict[str, str] = {\n", + " \"Upgrade 00\": \"run_1+2\",\n", + " \"Upgrade 02\": \"run_3+4\",\n", + "}\n", + "DATASETS: dict[str, str] = {\n", + " \"bills\": \"comb_bills_year_target\",\n", + " \"bat\": \"cross_subsidization_BAT_values\",\n", + "}\n", + "\n", + "BLDG_ID = \"bldg_id\"\n", + "UTILITY_COL = \"sb.electric_utility\"\n", + "GAS_UTILITY_COL = \"sb.gas_utility\"\n", + "HEATING_TYPE_COL = \"postprocess_group.heating_type\"\n", + "MONTH_ORDER = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", + "SCENARIO_ORDER = list(RUN_PAIRS)\n", + "\n", + "# Human-readable labels for postprocess_group.heating_type values\n", + "HEATING_TYPE_LABELS: dict[str, str] = {\n", + " \"fossil_fuel\": \"Fossil fuel\",\n", + " \"electrical_resistance\": \"Electric resistance\",\n", + " \"heat_pump\": \"Existing heat pump\",\n", + "}\n", + "# Display order for heating groups in charts and tables\n", + "HEATING_ORDER = [\"Fossil fuel\", \"Electric resistance\", \"Existing heat pump\", \"Other\"]\n", + "\n", + "print(f\"Reviewing {STATE.upper()} batch: {BATCH}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000005", + "metadata": {}, + "source": [ + "## Section 1: Load data\n", + "\n", + "We load `comb_bills_year_target/` (monthly bills per building) and\n", + "`cross_subsidization_BAT_values/` (annual BAT metrics per building) for both run pairs.\n", + "Both datasets are Hive-partitioned on `sb.electric_utility`.\n", + "\n", + "Each row in the **bills** table represents one building in one month (plus an `\"Annual\"`\n", + "summary row). Each row in the **BAT** table represents one building (annual only).\n", + "Schema details are in the AGENTS.md master-table documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000006", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded both scenarios.\n", + " Upgrade 00: bills (439270, 27) | BAT (33790, 35) | months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n", + " Upgrade 02: bills (439270, 27) | BAT (33790, 35) | months: ['Apr', 'Aug', 'Dec', 'Feb', 'Jan', 'Jul', 'Jun', 'Mar', 'May', 'Nov', 'Oct', 'Sep']\n" + ] + } + ], + "source": [ + "def master_path(run_pair: str, dataset: str) -> str:\n", + " return f\"{S3_BASE}/{STATE}/all_utilities/{BATCH}/{run_pair}/{dataset}/\"\n", + "\n", + "\n", + "def load_master_table(run_pair: str, dataset: str) -> pl.DataFrame:\n", + " return cast(\n", + " pl.DataFrame,\n", + " pl.scan_parquet(master_path(run_pair, dataset), hive_partitioning=True).collect(),\n", + " )\n", + "\n", + "\n", + "bills_by_scenario: dict[str, pl.DataFrame] = {}\n", + "bat_by_scenario: dict[str, pl.DataFrame] = {}\n", + "\n", + "for scenario, run_pair in RUN_PAIRS.items():\n", + " bills_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bills\"])\n", + " bat_by_scenario[scenario] = load_master_table(run_pair, DATASETS[\"bat\"])\n", + "\n", + "print(\"Loaded both scenarios.\")\n", + "for scenario in SCENARIO_ORDER:\n", + " b = bills_by_scenario[scenario]\n", + " bt = bat_by_scenario[scenario]\n", + " months_present = sorted(b.filter(pl.col(\"month\") != \"Annual\")[\"month\"].unique().to_list())\n", + " print(f\" {scenario}: bills {b.shape} | BAT {bt.shape} | months: {months_present}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000007", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bills schema:\n", + "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'in.representative_income': Float64, 'in.hvac_cooling_partial_space_conditioning': String, 'month': String, 'weight': Float64, 'elec_fixed_charge': Float64, 'elec_delivery_bill': Float64, 'elec_supply_bill': Float64, 'elec_total_bill': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'gas_fixed_charge': Float64, 'gas_volumetric_bill': Float64, 'gas_total_bill': Float64, 'propane_total_bill': Float64, 'oil_total_bill': Float64, 'energy_total_bill': Float64, 'sb.electric_utility': String})\n", + "\n", + "BAT schema:\n", + "Schema({'bldg_id': Int64, 'sb.gas_utility': String, 'upgrade': Int32, 'postprocess_group.has_hp': Boolean, 'postprocess_group.heating_type': String, 'postprocess_group.heating_type_v2': String, 'heats_with_electricity': Boolean, 'heats_with_natgas': Boolean, 'heats_with_oil': Boolean, 'heats_with_propane': Boolean, 'weight': Float64, 'BAT_vol_delivery': Float64, 'BAT_vol_supply': Float64, 'BAT_vol_total': Float64, 'BAT_percustomer_delivery': Float64, 'BAT_percustomer_supply': Float64, 'BAT_percustomer_total': Float64, 'BAT_epmc_delivery': Float64, 'BAT_epmc_supply': Float64, 'BAT_epmc_total': Float64, 'annual_bill_delivery': Float64, 'annual_bill_supply': Float64, 'annual_bill_total': Float64, 'economic_burden_delivery': Float64, 'economic_burden_supply': Float64, 'economic_burden_total': Float64, 'residual_share_delivery': Float64, 'residual_share_supply': Float64, 'residual_share_total': Float64, 'residual_share_epmc_delivery': Float64, 'residual_share_epmc_supply': Float64, 'residual_share_epmc_total': Float64, 'passthrough_delivery': Float64, 'passthrough_supply': Float64, 'sb.electric_utility': String})\n" + ] + } + ], + "source": [ + "# Quick schema peek \u2014 useful when working with an unfamiliar batch\n", + "print(\"Bills schema:\")\n", + "print(bills_by_scenario[SCENARIO_ORDER[0]].schema)\n", + "print(\"\\nBAT schema:\")\n", + "print(bat_by_scenario[SCENARIO_ORDER[0]].schema)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000008", + "metadata": {}, + "source": [ + "## Section 2: Input data quality checks (EDA)\n", + "\n", + "Before looking at bills and the BAT, we examine the raw inputs: how buildings are\n", + "distributed across utility territories, what their baseline heating systems are, and\n", + "what their gas and electric spending looks like. These checks catch assignment errors\n", + "and calibration anomalies early.\n", + "\n", + "All EDA in this section uses upgrade 00 (`\"Annual\"` rows)." + ] + }, + { + "cell_type": "markdown", + "id": "a0000009", + "metadata": {}, + "source": [ + "### 2a: Utility assignment\n", + "\n", + "What fraction of buildings (and weighted households) are assigned to each electric and\n", + "gas utility? We expect the partition to align with each utility's approximate share of\n", + "the state's residential customers.\n", + "\n", + "A large fraction of **null gas assignments** is normal \u2014 buildings that heat with\n", + "electricity, propane, or oil are not assigned a gas utility. Investigate if the null\n", + "fraction is surprisingly low in a territory with extensive gas infrastructure, or\n", + "surprisingly high in a gas-dense market." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000010", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Upgrade 00 \u2014 Annual rows: 33,790 sample buildings, 7,251,938 weighted households\n", + "\n", + "Electric utility assignment:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (8, 4)
electric_utilitybuildingsweighted_householdspct_of_total
stru32f64f64
"coned"154353.0640e642.3
"nimo"67911.532294e621.1
"psegli"44271.0300e614.2
"nyseg"3682792300.010.9
"rge"1444351481.04.8
"cenhud"1182270859.03.7
"or"829211011.02.9
"**TOTAL**"337907.251938e699.9
" + ], + "text/plain": [ + "shape: (8, 4)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 electric_utility \u2506 buildings \u2506 weighted_households \u2506 pct_of_total \u2502\n", + "\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 str \u2506 u32 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 coned \u2506 15435 \u2506 3.0640e6 \u2506 42.3 \u2502\n", + "\u2502 nimo \u2506 6791 \u2506 1.532294e6 \u2506 21.1 \u2502\n", + "\u2502 psegli \u2506 4427 \u2506 1.0300e6 \u2506 14.2 \u2502\n", + "\u2502 nyseg \u2506 3682 \u2506 792300.0 \u2506 10.9 \u2502\n", + "\u2502 rge \u2506 1444 \u2506 351481.0 \u2506 4.8 \u2502\n", + "\u2502 cenhud \u2506 1182 \u2506 270859.0 \u2506 3.7 \u2502\n", + "\u2502 or \u2506 829 \u2506 211011.0 \u2506 2.9 \u2502\n", + "\u2502 **TOTAL** \u2506 33790 \u2506 7.251938e6 \u2506 99.9 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gas utility assignment (null = no gas service / unassigned):\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (11, 4)
gas_utilitybuildingsweighted_householdspct_of_total
stru32f64f64
"(null \u2014 no gas service)"82121.8038e624.9
"coned"69911.3887e619.1
"kedny"64181.2747e617.6
"kedli"2874667484.0480479.2
"nimo"2746617985.7079918.5
…………
"nyseg"1581348155.1222574.8
"rge"1240297109.9641564.1
"cenhud"713162382.6882032.2
"or"594150773.0838762.1
"**TOTAL**"337907.2519e6100.0
" + ], + "text/plain": [ + "shape: (11, 4)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 gas_utility \u2506 buildings \u2506 weighted_households \u2506 pct_of_total \u2502\n", + "\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 str \u2506 u32 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 (null \u2014 no gas service) \u2506 8212 \u2506 1.8038e6 \u2506 24.9 \u2502\n", + "\u2502 coned \u2506 6991 \u2506 1.3887e6 \u2506 19.1 \u2502\n", + "\u2502 kedny \u2506 6418 \u2506 1.2747e6 \u2506 17.6 \u2502\n", + "\u2502 kedli \u2506 2874 \u2506 667484.048047 \u2506 9.2 \u2502\n", + "\u2502 nimo \u2506 2746 \u2506 617985.707991 \u2506 8.5 \u2502\n", + "\u2502 \u2026 \u2506 \u2026 \u2506 \u2026 \u2506 \u2026 \u2502\n", + "\u2502 nyseg \u2506 1581 \u2506 348155.122257 \u2506 4.8 \u2502\n", + "\u2502 rge \u2506 1240 \u2506 297109.964156 \u2506 4.1 \u2502\n", + "\u2502 cenhud \u2506 713 \u2506 162382.688203 \u2506 2.2 \u2502\n", + "\u2502 or \u2506 594 \u2506 150773.083876 \u2506 2.1 \u2502\n", + "\u2502 **TOTAL** \u2506 33790 \u2506 7.2519e6 \u2506 100.0 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "annual_u0 = bills_by_scenario[\"Upgrade 00\"].filter(pl.col(\"month\") == \"Annual\")\n", + "total_bldgs = annual_u0.height\n", + "total_weighted = cast(float, annual_u0[\"weight\"].sum())\n", + "\n", + "print(f\"Upgrade 00 \u2014 Annual rows: {total_bldgs:,} sample buildings, {total_weighted:,.0f} weighted households\\n\")\n", + "\n", + "# Electric utility assignment\n", + "elec_assign = (\n", + " annual_u0.with_columns(pl.col(UTILITY_COL).fill_null(\"(null)\"))\n", + " .group_by(UTILITY_COL)\n", + " .agg(\n", + " pl.len().alias(\"buildings\"),\n", + " pl.col(\"weight\").sum().alias(\"weighted_households\"),\n", + " )\n", + " .with_columns(\n", + " (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n", + " )\n", + " .sort(\"weighted_households\", descending=True)\n", + " .rename({UTILITY_COL: \"electric_utility\"})\n", + ")\n", + "# Add summary row for electric (cast to match schema)\n", + "elec_assign_with_total = pl.concat(\n", + " [\n", + " elec_assign,\n", + " pl.DataFrame(\n", + " {\n", + " \"electric_utility\": [\"**TOTAL**\"],\n", + " \"buildings\": [elec_assign[\"buildings\"].sum()],\n", + " \"weighted_households\": [elec_assign[\"weighted_households\"].sum()],\n", + " \"pct_of_total\": [elec_assign[\"pct_of_total\"].sum()],\n", + " },\n", + " schema=elec_assign.schema,\n", + " ),\n", + " ]\n", + ")\n", + "print(\"Electric utility assignment:\")\n", + "display(elec_assign_with_total)\n", + "\n", + "# Gas utility assignment\n", + "gas_assign = (\n", + " annual_u0.with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null \u2014 no gas service)\"))\n", + " .group_by(GAS_UTILITY_COL)\n", + " .agg(\n", + " pl.len().alias(\"buildings\"),\n", + " pl.col(\"weight\").sum().alias(\"weighted_households\"),\n", + " )\n", + " .with_columns(\n", + " (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n", + " )\n", + " .sort(\"weighted_households\", descending=True)\n", + " .rename({GAS_UTILITY_COL: \"gas_utility\"})\n", + ")\n", + "# Add summary row for gas (cast to match schema)\n", + "gas_assign_with_total = pl.concat(\n", + " [\n", + " gas_assign,\n", + " pl.DataFrame(\n", + " {\n", + " \"gas_utility\": [\"**TOTAL**\"],\n", + " \"buildings\": [gas_assign[\"buildings\"].sum()],\n", + " \"weighted_households\": [gas_assign[\"weighted_households\"].sum()],\n", + " \"pct_of_total\": [gas_assign[\"pct_of_total\"].sum()],\n", + " },\n", + " schema=gas_assign.schema,\n", + " ),\n", + " ]\n", + ")\n", + "print(\"\\nGas utility assignment (null = no gas service / unassigned):\")\n", + "display(gas_assign_with_total)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000010a", + "metadata": {}, + "source": [ + "### Zero gas usage verification\n", + "\n", + "Cross-check the gas utility assignment against ResStock annual gas consumption.\n", + "Buildings with zero annual gas consumption should align closely with the\n", + "\"(null \u2014 no gas service)\" category above, since gas utility assignment is gated\n", + "on `has_natgas_connection` (derived from nonzero gas consumption in\n", + "`load_curve_annual`).\n", + "\n", + "Both metrics below are **unweighted building counts** for apples-to-apples\n", + "comparison. Small differences can arise from sampling variance or buildings with\n", + "minimal gas usage that round to zero in annual totals but are flagged as connected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000010b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ResStock annual load curves from:\n", + " s3://data.sb/nrel/resstock/res_2024_amy2018_2/load_curve_annual/state=NY/upgrade=00/NY_upgrade00_metadata_and_annual_results.parquet\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Buildings with zero annual gas consumption: 8,212 / 33,790 (24.3%)\n", + "Buildings with nonzero annual gas consumption: 25,578 / 33,790 (75.7%)\n", + "\n", + "For comparison, unweighted null gas utility assignment: 8,212 / 33,790 (24.3%)\n", + "Difference (zero gas usage - null assignment): 0.0 percentage points\n" + ] + } + ], + "source": [ + "# Load ResStock metadata to check gas consumption\n", + "RESSTOCK_RELEASE = \"res_2024_amy2018_2\"\n", + "RESSTOCK_S3_BASE = \"s3://data.sb/nrel/resstock\"\n", + "GAS_CONSUMPTION_COL = \"out.natural_gas.total.energy_consumption.kwh\"\n", + "\n", + "# Construct path to load_curve_annual for this state and upgrade 00\n", + "# Filename pattern: {STATE}_upgrade{UPGRADE}_metadata_and_annual_results.parquet\n", + "upgrade_padded = \"00\"\n", + "annual_filename = f\"{STATE.upper()}_upgrade{upgrade_padded}_metadata_and_annual_results.parquet\"\n", + "annual_path = f\"{RESSTOCK_S3_BASE}/{RESSTOCK_RELEASE}/load_curve_annual/state={STATE.upper()}/upgrade={upgrade_padded}/{annual_filename}\"\n", + "\n", + "print(f\"Loading ResStock annual load curves from:\\n {annual_path}\\n\")\n", + "\n", + "# Load annual data and check for gas consumption column\n", + "annual_lf = pl.scan_parquet(annual_path)\n", + "\n", + "# Select just bldg_id and gas consumption\n", + "gas_check = cast(pl.DataFrame, annual_lf.select([BLDG_ID, GAS_CONSUMPTION_COL]).collect())\n", + "\n", + "# Count buildings with zero gas consumption\n", + "zero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) == 0).height\n", + "nonzero_gas = gas_check.filter(pl.col(GAS_CONSUMPTION_COL) > 0).height\n", + "total_bldgs_annual = gas_check.height\n", + "\n", + "pct_zero_gas = zero_gas / total_bldgs_annual * 100\n", + "pct_nonzero_gas = nonzero_gas / total_bldgs_annual * 100\n", + "\n", + "print(f\"Buildings with zero annual gas consumption: {zero_gas:,} / {total_bldgs_annual:,} ({pct_zero_gas:.1f}%)\")\n", + "print(\n", + " f\"Buildings with nonzero annual gas consumption: {nonzero_gas:,} / {total_bldgs_annual:,} ({pct_nonzero_gas:.1f}%)\"\n", + ")\n", + "\n", + "# Compare to null gas utility assignment (unweighted building count)\n", + "try:\n", + " null_gas_bldgs = gas_assign.filter(pl.col(\"gas_utility\") == \"(null \u2014 no gas service)\")[\"buildings\"][0]\n", + " pct_null_gas_unweighted = null_gas_bldgs / total_bldgs * 100\n", + " print(\n", + " f\"\\nFor comparison, unweighted null gas utility assignment: {null_gas_bldgs:,} / {total_bldgs:,} ({pct_null_gas_unweighted:.1f}%)\"\n", + " )\n", + " print(\n", + " f\"Difference (zero gas usage - null assignment): {pct_zero_gas - pct_null_gas_unweighted:.1f} percentage points\"\n", + " )\n", + "except (NameError, IndexError):\n", + " print(\"\\n(Run the utility assignment cell above first to compare with null gas assignment)\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000011", + "metadata": {}, + "source": [ + "### 2b: Heating type and fuel composition\n", + "\n", + "What fraction of buildings heat with each fuel or technology under upgrade 00\n", + "(baseline)? This breakdown drives cross-subsidy and bill-change patterns throughout\n", + "the analysis.\n", + "\n", + "The `postprocess_group.heating_type` column classifies buildings into three groups\n", + "used by CAIRO's post-processing:\n", + "\n", + "- **Fossil fuel** \u2014 primary heat source is natural gas, oil, or propane\n", + "- **Electric resistance** \u2014 primary heat source is electric resistance\n", + "- **Existing heat pump** \u2014 already has a heat pump in the baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000012", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline heating-type breakdown (upgrade 00):\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n", + "(Deprecated in version 1.0.0)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (4, 8)
heating_labelbuildingsweighted_householdsheats_natgasheats_oilheats_propaneheats_electricitypct_of_total
stru32f64i32i32i32i32f64
"Fossil fuel"295546.3495e62020170671280087.6
"Electric resistance"3070654504.11857300030709.0
"Existing heat pump"918198368.0275540009182.7
null24849529.86488700000.7
" + ], + "text/plain": [ + "shape: (4, 8)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 heating_la \u2506 buildings \u2506 weighted_h \u2506 heats_natg \u2506 heats_oil \u2506 heats_pro \u2506 heats_ele \u2506 pct_of_to \u2502\n", + "\u2502 bel \u2506 --- \u2506 ouseholds \u2506 as \u2506 --- \u2506 pane \u2506 ctricity \u2506 tal \u2502\n", + "\u2502 --- \u2506 u32 \u2506 --- \u2506 --- \u2506 i32 \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 str \u2506 \u2506 f64 \u2506 i32 \u2506 \u2506 i32 \u2506 i32 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Fossil \u2506 29554 \u2506 6.3495e6 \u2506 20201 \u2506 7067 \u2506 1280 \u2506 0 \u2506 87.6 \u2502\n", + "\u2502 fuel \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Electric \u2506 3070 \u2506 654504.118 \u2506 0 \u2506 0 \u2506 0 \u2506 3070 \u2506 9.0 \u2502\n", + "\u2502 resistance \u2506 \u2506 573 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Existing \u2506 918 \u2506 198368.027 \u2506 0 \u2506 0 \u2506 0 \u2506 918 \u2506 2.7 \u2502\n", + "\u2502 heat pump \u2506 \u2506 554 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 null \u2506 248 \u2506 49529.8648 \u2506 0 \u2506 0 \u2506 0 \u2506 0 \u2506 0.7 \u2502\n", + "\u2502 \u2506 \u2506 87 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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z2NBE8+bN873vfS9jxozJjjvuWDo/atSovPjii0mSr33ta4v9nTdq1CgnnXRSXnrppcyYMSOPPvpojjvuuCU+DwDK7/PMvxo2bJizzz47Z555ZqZMmZJbbrklP/zhD/P666/n/vvvT5KcfPLJi92edGkq8f2gHj161AgzVGvUqFG+9rWv5fLLL09VVVVGjhy5SKBh2LBhSZKWLVtmk002WWSMNdZYI1tvvXU+/PDDDB8+PDNnzlyp1T+33XbbGmGGak2bNs2hhx6aX//615k1a1bGjh1bIyxQG6/xrVu3zhVXXJHp06cvdguQRo0aZf/998/NN9+cqVOnZuLEiTU+2K8rRx111GJ/lg0bNszxxx+fl156KePGjcs///nPOv9gf88991zsc+7Zs2c23XTTvP3225k/f34OOeSQRVZNaNCgQQ455JDSCrgffPDBUgMNlfS8gcpXP5sAAUAdGTlyZMaPH58k2WmnnZbatvp6sVgsTa6rVf8j6Y033kixWFzu+7dq1aqU7F94f+AvmuqfZ+PGjZcYKkmS9dZbr/SzHjx4cL3UBgCrgqlTp2bIkCFJFryRuLRvCFa/oTdx4sTSPChJ3nrrrcybNy/J0udFDRs2zMEHH1wrYYYkK7xawsLqq+Zl1Vgbc5nq88OGDcvMmTNXqs7Fqc/5ZsOGDRcbZqjWqVOn0vGYMWOWOtbSPhjq2rVrkgWBlhkzZtS49tprryVJNthgg6y33npLHKNbt27Zeeeda+xvvfAcf5999lli39atW5c+CHnnnXeW2A6A1UPHjh1zyimnJEmefvrp9O/fv7TCwK677povf/nLKzxmJb4ftDyvvcn/hTAXNmfOnCQLPtRf0vL+jRot+H7s51n+f2mv7QvXOGrUqBrXaus1fo011ljqvHBF5jq1ZWk/kyZNmmTbbbdNkrz77rt1XsvS/g6tv/76peOFvyS2pDajR49e6r0q6XkDlc8KDQBUjAMOOCB9+/ZdapumTZvWeFydIE+Wvcxbu3bt0rx588ycOXORyfBee+2VYcOG5bnnnssll1ySvfbaKz169EjHjh2X+o3DQqGQvfbaK/fdd18eeOCBTJo0Kbvuums23HDDtG/fvsYbrKuz6p9n586dl/kNzc6dO2fkyJGL/Qc0AFS65ZmvJP+3/dPyev/990vHi1sdYGELz3k++uij0huvC3/bfUW/ZVgulVJzbcxldttttzz44IOZMGFCvv/976dv377p1atX1ltvvcWu2LC8yjnfnDNnTiZNmpS5c+cmSWkp6uWxtA87Fv4gYfr06TVWcqj+O7Eyfx+qf48tWrRYZoil+k30Zb3ZDkD51cb8a88998yLL76YgQMHpl+/fkkWrJ70ne98Z6U+oK/E94NW5LX3s7p165bnnnsukydPzujRoxd5Ha6qqsqgQYOSLJgLrczqDMuycI2fDTzW1Wv87NmzM3HixMyfPz/JgsBwtRX5wtPnsay/f9UhgcmTJ2fWrFl1un3r0mqpDvAsrV2bNm1Kx5/9Ha7IvZL6fd5A5RNoAKBitG7deoX3nps2bVrpuGXLlkttWygU0rJly8ycOTNTpkypce2AAw7IxIkTc9999+XVV1/Nq6++mmRBcrtXr1750pe+lP3333+x3wr81re+lalTp+aJJ57If//73/z3v/9NkjRr1iybbbZZttxyy+y3336f643sSlf9exg+fHgOPvjg5eoj0ADAqmhl5ivLY+E5zYpsS/DRRx9lm222WWSMVWXeUSk118ZcZqONNsq5556bm2++OaNGjcott9ySZMEctHv37tliiy2yzz771Pjm2vKqr/nm/Pnz8/LLL+eRRx7JsGHDavx+atPSQiPV91yZ51Ldd8aMGTnkkEOWq0/1KhGL2x4DgMpQG/OvQqGQ73znO3nnnXdKAb3TTz99me8lLc2q9H7QsgKb++23X/7xj39k1qxZueKKK/Ltb387vXr1SpMmTTJ69Oj84x//KK1Ote+++9ZJjUsLgdTWa/zcuXPzzDPPpH///hk5cuQyP3SvBAt/uevjjz9e7JYd9WF5Qjoruw3d4lTK8wYqg0ADAGTBP2yPPfbY7LvvvnnyySczePDgDB8+PBMnTswbb7yRN954Iw888EB+8IMf5Etf+lKNvg0bNsyZZ56ZQw45JAMHDszgwYMzcuTITJkyJS+99FJeeumlPPjgg/nxj39cY/m8L7pV4R+NAFDpFvcNO+rH4uYyu+yyS3r37p0nn3wygwYNyrBhwzJu3Li89957ee+99/LQQw/luOOOW+434qvVx3xzzpw5ueyyyypm2ez69NlVIgBYPc2cOTOffvpp6fFLL72U3r17r/R4q9P7QW3bts2FF16Yq666KqNGjcoVV1yx2Ha77rrrcoc/K8HCr/FTp07Nj3/844wYMaK8RQGwwgQaAFilLbxiwvTp09OxY8clti0Wi6VEd+vWrRfbpmPHjjnyyCNLj6dNm5aXXnopt99+eyZOnJjrr78+/fr1W+wyZ127dq3xD9SJEyfm2WefzZ/+9KeMGzcuN9xwQ2688cbPtddgXauqqlqpftW/h4022ijnnHNObZYEAF8IC89pzjzzzPTs2XO5+i08p1n4G4azZ8+uiG8DLkul1Fybc5nmzZvngAMOyAEHHJBkQVBg0KBBueuuuzJkyJD8/ve/z6abbpoePXqs8Nh1Od98+OGHS2GGzTffPIcddli6d++e1q1bp3HjxkmSsWPH5qSTTlrhsVdEq1atMnHixMyePXul+iYLPpS58sorl7vfWmuttcL3AmDVMn/+/Fx33XWZMWNGWrZsmenTp+fBBx/M9ttvny233PJzjb06vB+UJF/60pdy5ZVX5sc//nGN4GaTJk3SpUuXfPWrX80OO+xQludRG6/xf/7zn0thhh133DF9+/bN+uuvn9atW5dWFnjzzTdz4YUX1l7htWDhEM6S3vdc2ffzKtnyPG/gi0OgAYBV2kYbbVQ6HjVqVDbccMMltp04cWJmzZqVJMv9BnKrVq3Sp0+fdOzYMeeff34mTpyY999/P5ttttky+7Zr1y4HHnhgmjdvnhtuuCHDhw/PhAkTVnhP7bq28AcJC+8V+FlL2ztwww03zDvvvJPJkyfXyTLcALC66969e+l4/vz5K/V6uvAyrKNHj14llmWtlJrrci6zxhprpHfv3unZs2dOPPHETJ8+PS+//PJKBRo+qzbnm2+99VaSBfsjX3rppaUQQ33r2rVrJk6cuFz7Xn/WhhtumKeeeipTpkxJhw4dssYaa9RBhQCsiu6555688847WWONNfLTn/40t912W15//fXcdNNN+cUvflGrK/WsKu8Hfdb999+fP/zhD9lhhx1y3HHHpVmzZpk3b17atWu3XNsN1KXaeI2vnutsuOGGOf/888v+nKotK4zw4YcfJlkwR2vevHmNa9Xv6S3t/bxk6e/plcvned7AF09l/B8bAFZS165d0759+yTJs88+u9S21dcLhUK23nrrGtcmT5681DdNF07aT506tca1MWPGZNKkSSvVtxKss846pePhw4cvsV31PyQWp3rv7gkTJix1jCQZNmyY5bEB4DNat26djTfeOP+vvXsPirr6/zj+UlCBQllFbiLlJaHMqBBNxFJCbDQ17Q+zmZomtVEz8JI0gzqYyh/1xVshYebUaGMz1lheUEiRQRRUDDVFMTF1XRVQuQ4QcvH3h8P+QBZvIG7xfPzF7J7PZ8+ZzzLnvZ/P+7yPdLv88d1UV1ebb8jW9/zzz5tXl90rLtq+fbuOHj36kL1tOdbS55aIZaqrq3X27Nkmj3NwcJCLi4uk/98H+n61RryZl5cnSfLw8GgymaE1Ytm60t9Go1GXL19usl1lZaXWrl3b4AZ+3XWsra2953fFaDSa91AHAPy3ZWVlafPmzZKkDz74QF5eXvr444/VqVMnXb9+XevWrXuo8/7b7wfVl5OTo/Xr18vT01Ph4eFyd3eXk5OTnJ2dreLBf0vM8XWxjqenZ5NjehzX6eLFi02+V1lZaf5tYGkRl4eHhyTp2rVrTcaX5eXlVhnzNGfcANqexz8TAQDQDO3bt9eECRMkSWlpadq3b5/FdiaTST/++KMkaciQIeaAX5Kys7M1e/ZsRURE6Ny5cxaP/+OPP8x/119BefjwYc2dO1eLFi3S1atXLR6bmZkpSbK1tZWXl9cDjK512NnZqUePHpKkxMREiz9yrl27ppSUlCbP8dJLL5nHtmLFChUXF1tsd+7cOUVGRmrGjBkyGo0t0HsAAP473nrrLUnSwYMHlZCQYHElVXV1teLi4hQREaG4uLgG7xkMBgUGBkqStmzZ0uTD9UOHDmn9+vWKjIzU6dOnW3YQD8ha+tzcWKagoEAREREKDw9XcnKyxWuXn59vThCtH0/eS2vFm3WVKYxGY4My03Vqamr066+/PtS5H0RQUJDs7e1VU1Oj2NhYlZeXN2pTXV2tmJgYxcfHa8GCBeYVfj179tTAgQMlSd98841yc3MtfkZubq6WLl2qmTNn6vjx449uMACAx660tFTLly9XbW2t/Pz8NGbMGEmSm5ub3n//fUnS3r1775lYeaf/wv2g+rKysiTdLu1vDQkMd2qJOb4u1snJyVFVVVWjYysrK7V9+/YW7vm9bd682VxRtr6amhp9//33ys/PlySFhIQ0alNX3aympsactHOnLVu2WOWWFM0ZN4C2hy0nAAD/eqNHj9bBgwd14sQJRUdH69ChQxoyZIjc3NxUWFioU6dOadu2baqqqlKXLl300UcfNTjewcFBlZWVKisr07x58zRixAi98sorcnZ2VkVFhf7880/99ttvkqTnnnvOvLJOup0MUFlZKaPRqNDQUAUHB2vgwIEyGAwqLS1VRkaGduzYIUkaOnToA5furVuxWFNTY84kd3Z2Nr/eUt59913973//U0VFhebMmaNJkyapV69eqq6uVnZ2tnbs2CFXV9cmS9i1b99es2fPVnh4uC5evKjp06dr3Lh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+ "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": { + "image/png": { + "height": 400, + "width": 1050 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "def add_heating_label(df: pl.DataFrame) -> pl.DataFrame:\n", + " \"\"\"Map postprocess_group.heating_type codes to human-readable labels.\"\"\"\n", + " return df.with_columns(\n", + " pl.col(HEATING_TYPE_COL)\n", + " .fill_null(\"Other\")\n", + " .replace(HEATING_TYPE_LABELS, default=pl.col(HEATING_TYPE_COL))\n", + " .alias(\"heating_label\")\n", + " )\n", + "\n", + "\n", + "heating_df = add_heating_label(annual_u0)\n", + "heating_stats = (\n", + " heating_df.group_by(\"heating_label\")\n", + " .agg(\n", + " pl.len().alias(\"buildings\"),\n", + " pl.col(\"weight\").sum().alias(\"weighted_households\"),\n", + " pl.col(\"heats_with_natgas\").cast(pl.Int32).sum().alias(\"heats_natgas\"),\n", + " pl.col(\"heats_with_oil\").cast(pl.Int32).sum().alias(\"heats_oil\"),\n", + " pl.col(\"heats_with_propane\").cast(pl.Int32).sum().alias(\"heats_propane\"),\n", + " pl.col(\"heats_with_electricity\").cast(pl.Int32).sum().alias(\"heats_electricity\"),\n", + " )\n", + " .with_columns(\n", + " (pl.col(\"weighted_households\") / total_weighted * 100).round(1).alias(\"pct_of_total\"),\n", + " )\n", + " .sort(\"weighted_households\", descending=True)\n", + ")\n", + "print(\"Baseline heating-type breakdown (upgrade 00):\")\n", + "display(heating_stats)\n", + "\n", + "avail_order = [h for h in HEATING_ORDER if h in heating_df[\"heating_label\"].unique().to_list()]\n", + "chart_data = (\n", + " heating_stats.filter(pl.col(\"heating_label\").is_in(avail_order))\n", + " .with_columns(pl.col(\"heating_label\").cast(pl.Enum(avail_order)))\n", + " .sort(\"heating_label\")\n", + ")\n", + "\n", + "# Create pie chart\n", + "colors = {\n", + " \"Fossil fuel\": SB_COLORS[\"carrot\"],\n", + " \"Electric resistance\": SB_COLORS[\"saffron\"],\n", + " \"Existing heat pump\": SB_COLORS[\"sky\"],\n", + " \"Other\": \"#CCCCCC\",\n", + "}\n", + "labels = chart_data[\"heating_label\"].to_list()\n", + "sizes = chart_data[\"pct_of_total\"].to_list()\n", + "pie_colors = [colors.get(label, \"#CCCCCC\") for label in labels]\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "result = ax.pie(\n", + " sizes,\n", + " labels=labels,\n", + " colors=pie_colors,\n", + " autopct=\"%.1f%%\",\n", + " textprops={\"fontsize\": 11},\n", + " pctdistance=0.7,\n", + ")\n", + "autotexts = result[2] # type: ignore[index-out-of-bounds]\n", + "for t in autotexts:\n", + " t.set_color(\"white\")\n", + " t.set_fontweight(\"bold\")\n", + "ax.set_title(\"Baseline heating type distribution (upgrade 00)\", fontsize=15, fontweight=\"bold\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a0000013", + "metadata": {}, + "source": [ + "### 2c: Annual gas spending by gas utility\n", + "\n", + "Summary of annual gas bills (`gas_total_bill`, in dollars) for buildings with non-zero\n", + "gas bills, grouped by assigned gas utility.\n", + "\n", + "> **Note**: `gas_total_bill` is a bill amount (dollars), not volumetric consumption\n", + "> (kWh or therms). For actual consumption data, cross-reference the ResStock monthly\n", + "> load curves column `out.natural_gas.total.energy_consumption`.\n", + "\n", + "Implausibly wide min/max ranges, or extreme mean-to-median ratios, often trace to\n", + "incorrect tariff calibration or misassigned buildings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000014", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual gas spending stats by gas utility\n", + "(buildings with gas_total_bill > 0; unweighted mean/median):\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (9, 7)
gas_utilitybuildings_with_gas_billweighted_householdsmin_gas_billmax_gas_billmean_gas_billmedian_gas_bill
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" + ], + "text/plain": [ + "shape: (9, 7)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 gas_utility \u2506 buildings_wi \u2506 weighted_ho \u2506 min_gas_bil \u2506 max_gas_bil \u2506 mean_gas_bi \u2506 median_gas_ \u2502\n", + "\u2502 --- \u2506 th_gas_bill \u2506 useholds \u2506 l \u2506 l \u2506 ll \u2506 bill \u2502\n", + "\u2502 str \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 u32 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 coned \u2506 6991 \u2506 1.3887e6 \u2506 400.0 \u2506 12166.0 \u2506 1362.0 \u2506 927.0 \u2502\n", + "\u2502 kedny \u2506 6418 \u2506 1.2747e6 \u2506 251.0 \u2506 7240.0 \u2506 1462.0 \u2506 1257.0 \u2502\n", + "\u2502 kedli \u2506 2874 \u2506 667484.0480 \u2506 302.0 \u2506 6189.0 \u2506 1792.0 \u2506 1756.0 \u2502\n", + "\u2502 \u2506 \u2506 47 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 nimo \u2506 2746 \u2506 617985.7079 \u2506 265.0 \u2506 4005.0 \u2506 1063.0 \u2506 998.0 \u2502\n", + "\u2502 \u2506 \u2506 91 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 nfg \u2506 2421 \u2506 540904.9569 \u2506 238.0 \u2506 3337.0 \u2506 1007.0 \u2506 957.0 \u2502\n", + "\u2502 \u2506 \u2506 26 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 nyseg \u2506 1581 \u2506 348155.1222 \u2506 201.0 \u2506 4667.0 \u2506 1091.0 \u2506 1005.0 \u2502\n", + "\u2502 \u2506 \u2506 57 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 rge \u2506 1240 \u2506 297109.9641 \u2506 249.0 \u2506 3639.0 \u2506 1164.0 \u2506 1098.0 \u2502\n", + "\u2502 \u2506 \u2506 56 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 cenhud \u2506 713 \u2506 162382.6882 \u2506 325.0 \u2506 7328.0 \u2506 1374.0 \u2506 968.0 \u2502\n", + "\u2502 \u2506 \u2506 03 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 or \u2506 594 \u2506 150773.0838 \u2506 278.0 \u2506 5743.0 \u2506 1658.0 \u2506 1449.0 \u2502\n", + "\u2502 \u2506 \u2506 76 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gas_bill_stats = (\n", + " annual_u0.filter(pl.col(\"gas_total_bill\") > 0)\n", + " .with_columns(pl.col(GAS_UTILITY_COL).fill_null(\"(null)\"))\n", + " .group_by(GAS_UTILITY_COL)\n", + " .agg(\n", + " pl.len().alias(\"buildings_with_gas_bill\"),\n", + " pl.col(\"weight\").sum().alias(\"weighted_households\"),\n", + " pl.col(\"gas_total_bill\").min().round(0).alias(\"min_gas_bill\"),\n", + " pl.col(\"gas_total_bill\").max().round(0).alias(\"max_gas_bill\"),\n", + " pl.col(\"gas_total_bill\").mean().round(0).alias(\"mean_gas_bill\"),\n", + " pl.col(\"gas_total_bill\").median().round(0).alias(\"median_gas_bill\"),\n", + " )\n", + " .sort(\"buildings_with_gas_bill\", descending=True)\n", + " .rename({GAS_UTILITY_COL: \"gas_utility\"})\n", + ")\n", + "print(\"Annual gas spending stats by gas utility\\n(buildings with gas_total_bill > 0; unweighted mean/median):\")\n", + "display(gas_bill_stats)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000015", + "metadata": {}, + "source": [ + "### 2d: Annual electric bill by electric utility\n", + "\n", + "Summary of annual total electric bills (`elec_total_bill = fixed + delivery volumetric\n", + "+ supply`), grouped by electric utility. The decomposition into fixed charge, delivery,\n", + "and supply components helps identify which tariff component is driving outliers.\n", + "\n", + "Cross-check median values against published tariff rates for a rough sanity check." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000016", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (7, 10)
electric_utilitybuildingsweighted_householdsmin_elec_totalmax_elec_totalmean_elec_totalmedian_elec_totalmedian_fixedmedian_deliverymedian_supply
stru32f64f64f64f64f64f64f64f64
"coned"154353.0640e6266.026915.01597.01251.0255.0608.0388.0
"nimo"67911.532294e6259.017161.01679.01408.0215.0578.0615.0
"psegli"44271.0300e6212.014787.02090.01900.0197.0841.0858.0
"nyseg"3682792300.0286.019078.02042.01714.0239.0755.0721.0
"rge"1444351481.0332.011209.01710.01399.0288.0510.0604.0
"cenhud"1182270859.0368.017252.02388.02031.0264.01064.0702.0
"or"829211011.0349.014370.02045.01872.0289.0863.0716.0
" + ], + "text/plain": [ + "shape: (7, 10)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 electric_ \u2506 buildings \u2506 weighted_ \u2506 min_elec_ \u2506 \u2026 \u2506 median_el \u2506 median_fi \u2506 median_de \u2506 median_s \u2502\n", + "\u2502 utility \u2506 --- \u2506 household \u2506 total \u2506 \u2506 ec_total \u2506 xed \u2506 livery \u2506 upply \u2502\n", + "\u2502 --- \u2506 u32 \u2506 s \u2506 --- \u2506 \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 str \u2506 \u2506 --- \u2506 f64 \u2506 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n", + "\u2502 \u2506 \u2506 f64 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 coned \u2506 15435 \u2506 3.0640e6 \u2506 266.0 \u2506 \u2026 \u2506 1251.0 \u2506 255.0 \u2506 608.0 \u2506 388.0 \u2502\n", + "\u2502 nimo \u2506 6791 \u2506 1.532294e \u2506 259.0 \u2506 \u2026 \u2506 1408.0 \u2506 215.0 \u2506 578.0 \u2506 615.0 \u2502\n", + "\u2502 \u2506 \u2506 6 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 psegli \u2506 4427 \u2506 1.0300e6 \u2506 212.0 \u2506 \u2026 \u2506 1900.0 \u2506 197.0 \u2506 841.0 \u2506 858.0 \u2502\n", + "\u2502 nyseg \u2506 3682 \u2506 792300.0 \u2506 286.0 \u2506 \u2026 \u2506 1714.0 \u2506 239.0 \u2506 755.0 \u2506 721.0 \u2502\n", + "\u2502 rge \u2506 1444 \u2506 351481.0 \u2506 332.0 \u2506 \u2026 \u2506 1399.0 \u2506 288.0 \u2506 510.0 \u2506 604.0 \u2502\n", + "\u2502 cenhud \u2506 1182 \u2506 270859.0 \u2506 368.0 \u2506 \u2026 \u2506 2031.0 \u2506 264.0 \u2506 1064.0 \u2506 702.0 \u2502\n", + "\u2502 or \u2506 829 \u2506 211011.0 \u2506 349.0 \u2506 \u2026 \u2506 1872.0 \u2506 289.0 \u2506 863.0 \u2506 716.0 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "elec_bill_stats = (\n", + " annual_u0.group_by(UTILITY_COL)\n", + " .agg(\n", + " pl.len().alias(\"buildings\"),\n", + " pl.col(\"weight\").sum().alias(\"weighted_households\"),\n", + " pl.col(\"elec_total_bill\").min().round(0).alias(\"min_elec_total\"),\n", + " pl.col(\"elec_total_bill\").max().round(0).alias(\"max_elec_total\"),\n", + " pl.col(\"elec_total_bill\").mean().round(0).alias(\"mean_elec_total\"),\n", + " pl.col(\"elec_total_bill\").median().round(0).alias(\"median_elec_total\"),\n", + " pl.col(\"elec_fixed_charge\").median().round(0).alias(\"median_fixed\"),\n", + " pl.col(\"elec_delivery_bill\").median().round(0).alias(\"median_delivery\"),\n", + " pl.col(\"elec_supply_bill\").median().round(0).alias(\"median_supply\"),\n", + " )\n", + " .sort(\"buildings\", descending=True)\n", + " .rename({UTILITY_COL: \"electric_utility\"})\n", + ")\n", + "print(\"Annual electric bill stats by electric utility (upgrade 00, unweighted mean/median):\")\n", + "display(elec_bill_stats)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000017", + "metadata": {}, + "source": [ + "## Section 3: Structural quality checks\n", + "\n", + "These checks catch common post-processing errors before any analysis proceeds:\n", + "\n", + "- **Duplicate building IDs** \u2014 indicate a failed post-processing merge or join.\n", + "- **ID mismatches** \u2014 buildings that made it into one table but not the other.\n", + "- **Electric component arithmetic**:\n", + " `elec_total_bill \u2248 elec_fixed_charge + elec_delivery_bill + elec_supply_bill`\n", + "- **Energy total arithmetic**:\n", + " `energy_total_bill \u2248 elec_total_bill + gas_total_bill + propane_total_bill + oil_total_bill`\n", + "- **BAT identity**:\n", + " `BAT_percustomer_total \u2248 annual_bill_total \u2212 economic_burden_total \u2212 residual_share_total`\n", + "\n", + "All assertions must pass before continuing. A failed assertion points to a specific\n", + "post-processing step to investigate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000018", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (2, 12)
scenariobill rowsBAT rowsannual bill bldgsBAT bldgsduplicate annual IDsduplicate BAT IDsbill IDs missing from BATBAT IDs missing from billsmax elec component errormax energy component errormax BAT identity error
stri64i64i64i64i64i64i64i64f64f64f64
"Upgrade 00"43927033790337903379000009.0949e-131.8190e-121.1369e-12
"Upgrade 02"43927033790337903379000009.0949e-131.7053e-124.6566e-10
" + ], + "text/plain": [ + "shape: (2, 12)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 scenario \u2506 bill rows \u2506 BAT rows \u2506 annual \u2506 \u2026 \u2506 BAT IDs \u2506 max elec \u2506 max \u2506 max BAT \u2502\n", + "\u2502 --- \u2506 --- \u2506 --- \u2506 bill \u2506 \u2506 missing \u2506 component \u2506 energy \u2506 identity \u2502\n", + "\u2502 str \u2506 i64 \u2506 i64 \u2506 bldgs \u2506 \u2506 from bills \u2506 error \u2506 component \u2506 error \u2502\n", + "\u2502 \u2506 \u2506 \u2506 --- \u2506 \u2506 --- \u2506 --- \u2506 error \u2506 --- \u2502\n", + "\u2502 \u2506 \u2506 \u2506 i64 \u2506 \u2506 i64 \u2506 f64 \u2506 --- \u2506 f64 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 f64 \u2506 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Upgrade 00 \u2506 439270 \u2506 33790 \u2506 33790 \u2506 \u2026 \u2506 0 \u2506 9.0949e-13 \u2506 1.8190e-1 \u2506 1.1369e-1 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 2 \u2506 2 \u2502\n", + "\u2502 Upgrade 02 \u2506 439270 \u2506 33790 \u2506 33790 \u2506 \u2026 \u2506 0 \u2506 9.0949e-13 \u2506 1.7053e-1 \u2506 4.6566e-1 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 2 \u2506 0 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All structural checks passed.\n" + ] + } + ], + "source": [ + "qa_rows: list[dict[str, object]] = []\n", + "\n", + "for scenario in SCENARIO_ORDER:\n", + " bills = bills_by_scenario[scenario]\n", + " bat = bat_by_scenario[scenario]\n", + " annual = bills.filter(pl.col(\"month\") == \"Annual\")\n", + "\n", + " elec_err = cast(\n", + " float,\n", + " annual.select(\n", + " (\n", + " pl.col(\"elec_total_bill\")\n", + " - pl.col(\"elec_fixed_charge\")\n", + " - pl.col(\"elec_delivery_bill\")\n", + " - pl.col(\"elec_supply_bill\")\n", + " )\n", + " .abs()\n", + " .max()\n", + " ).item()\n", + " or 0,\n", + " )\n", + " energy_err = cast(\n", + " float,\n", + " annual.select(\n", + " (\n", + " pl.col(\"energy_total_bill\")\n", + " - pl.col(\"elec_total_bill\")\n", + " - pl.col(\"gas_total_bill\")\n", + " - pl.col(\"propane_total_bill\")\n", + " - pl.col(\"oil_total_bill\")\n", + " )\n", + " .abs()\n", + " .max()\n", + " ).item()\n", + " or 0,\n", + " )\n", + " bat_err = cast(\n", + " float,\n", + " bat.select(\n", + " (\n", + " pl.col(\"BAT_percustomer_total\")\n", + " - pl.col(\"annual_bill_total\")\n", + " + pl.col(\"economic_burden_total\")\n", + " + pl.col(\"residual_share_total\")\n", + " )\n", + " .abs()\n", + " .max()\n", + " ).item()\n", + " or 0,\n", + " )\n", + "\n", + " qa_rows.append(\n", + " {\n", + " \"scenario\": scenario,\n", + " \"bill rows\": bills.height,\n", + " \"BAT rows\": bat.height,\n", + " \"annual bill bldgs\": annual[BLDG_ID].n_unique(),\n", + " \"BAT bldgs\": bat[BLDG_ID].n_unique(),\n", + " \"duplicate annual IDs\": annual.height - annual[BLDG_ID].n_unique(),\n", + " \"duplicate BAT IDs\": bat.height - bat[BLDG_ID].n_unique(),\n", + " \"bill IDs missing from BAT\": annual.join(bat.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n", + " \"BAT IDs missing from bills\": bat.join(annual.select(BLDG_ID), on=BLDG_ID, how=\"anti\").height,\n", + " \"max elec component error\": elec_err,\n", + " \"max energy component error\": energy_err,\n", + " \"max BAT identity error\": bat_err,\n", + " }\n", + " )\n", + "\n", + "qa = pl.DataFrame(qa_rows)\n", + "display(qa)\n", + "\n", + "assert qa[\"duplicate annual IDs\"].sum() == 0, \"Duplicate annual bill IDs found\"\n", + "assert qa[\"duplicate BAT IDs\"].sum() == 0, \"Duplicate BAT IDs found\"\n", + "assert qa[\"bill IDs missing from BAT\"].sum() == 0, \"Bill IDs missing from BAT table\"\n", + "assert qa[\"BAT IDs missing from bills\"].sum() == 0, \"BAT IDs missing from bills table\"\n", + "assert cast(float, qa[\"max elec component error\"].max()) < 0.01, \"Electric component arithmetic mismatch\"\n", + "assert cast(float, qa[\"max energy component error\"].max()) < 0.01, \"Energy total arithmetic mismatch\"\n", + "assert cast(float, qa[\"max BAT identity error\"].max()) < 0.01, \"BAT identity check failed\"\n", + "print(\"All structural checks passed.\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000019", + "metadata": {}, + "source": [ + "## Section 4: Baseline analysis (upgrade 00, runs 1+2)\n", + "\n", + "This section uses the upgrade 00 run pair (runs 1+2), representing the current state:\n", + "each building is on its existing heating system with today's electric and gas rates." + ] + }, + { + "cell_type": "markdown", + "id": "a0000020", + "metadata": {}, + "source": [ + "### 4a: Weighted statistics helpers\n", + "\n", + "We define weighted mean and weighted quantile here so they are available to all\n", + "subsequent cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000021", + "metadata": {}, + "outputs": [], + "source": [ + "def weighted_mean(df: pl.DataFrame, value: str, weight: str = \"weight\") -> float:\n", + " \"\"\"Weighted arithmetic mean.\"\"\"\n", + " valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null())\n", + " wsum = cast(float, valid[weight].sum())\n", + " if valid.is_empty() or wsum == 0:\n", + " return float(\"nan\")\n", + " return cast(float, (valid[value] * valid[weight]).sum()) / wsum\n", + "\n", + "\n", + "def weighted_quantile(\n", + " df: pl.DataFrame,\n", + " value: str,\n", + " q: float,\n", + " weight: str = \"weight\",\n", + ") -> float:\n", + " \"\"\"Weighted quantile via cumulative weight sort.\"\"\"\n", + " valid = df.filter(pl.col(value).is_not_null() & pl.col(weight).is_not_null()).sort(value)\n", + " wsum = cast(float, valid[weight].sum())\n", + " if valid.is_empty() or wsum == 0:\n", + " return float(\"nan\")\n", + " return float(valid.filter(pl.col(weight).cum_sum() >= wsum * q)[value][0])" + ] + }, + { + "cell_type": "markdown", + "id": "a0000022", + "metadata": {}, + "source": [ + "### 4b: Cross-subsidization table\n", + "\n", + "The **Bill Alignment Test (BAT)** measures whether each customer pays more or less than\n", + "their cost of service. A positive BAT means the customer **overpays** (they\n", + "cross-subsidize others). A negative BAT means they **underpay** (they receive a\n", + "cross-subsidy from others).\n", + "\n", + "The table below shows each heating-type group's share of customers, electric delivery\n", + "revenue, cost of service, and total overpayment. Under flat volumetric rates, heat pump\n", + "and electric resistance customers typically overpay because they use more electricity\n", + "(and therefore pay more delivery cost per unit) than fossil fuel customers, whose\n", + "delivery cost does not scale with their gas consumption.\n", + "\n", + "Column definitions:\n", + "\n", + "- **% of delivery revenue** \u2014 group's weighted share of annual delivery bill revenue\n", + "- **% of cost of service** \u2014 group's weighted share of annual cost of service\n", + " (`economic_burden_total + residual_share_total`)\n", + "- **Net cross-subsidy** \u2014 sum of weighted BAT for all customers in the group.\n", + " Positive = group overpays (cross-subsidizes others); negative = group underpays\n", + " (receives a cross-subsidy). By definition, the net across all groups sums to ~$0.\n", + "- **Mean BAT** \u2014 weighted mean BAT per customer (positive = overpaying)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000023", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-subsidization by customer group (upgrade 00, annual, weighted):\n", + "System-wide check: sum of net cross-subsidy = $13.4M (should be ~$0M)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n", + "(Deprecated in version 1.0.0)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Customer group% of customers% of delivery revenue% of cost of serviceNet cross-subsidy ($M/yr)Mean BAT ($/yr)
strf64f64f64f64f64
"Fossil fuel"87.677.083.3-807.3-127.0
"Electric resistance"9.017.212.3635.1970.0
"Existing heat pump"2.75.33.9185.6935.0
" + ], + "text/plain": [ + "shape: (3, 6)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 Customer group \u2506 % of \u2506 % of delivery \u2506 % of cost of \u2506 Net cross-subsidy \u2506 Mean BAT \u2502\n", + "\u2502 --- \u2506 customers \u2506 revenue \u2506 service \u2506 ($M/yr) \u2506 ($/yr) \u2502\n", + "\u2502 str \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Fossil fuel \u2506 87.6 \u2506 77.0 \u2506 83.3 \u2506 -807.3 \u2506 -127.0 \u2502\n", + "\u2502 Electric resistance \u2506 9.0 \u2506 17.2 \u2506 12.3 \u2506 635.1 \u2506 970.0 \u2502\n", + "\u2502 Existing heat pump \u2506 2.7 \u2506 5.3 \u2506 3.9 \u2506 185.6 \u2506 935.0 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bat_u0 = add_heating_label(bat_by_scenario[\"Upgrade 00\"]).with_columns(\n", + " (pl.col(\"economic_burden_total\") + pl.col(\"residual_share_total\")).alias(\"cost_of_service\"),\n", + ")\n", + "\n", + "_total_customers = cast(float, bat_u0[\"weight\"].sum())\n", + "_total_revenue = cast(float, (bat_u0[\"annual_bill_total\"] * bat_u0[\"weight\"]).sum())\n", + "_total_cos = cast(float, (bat_u0[\"cost_of_service\"] * bat_u0[\"weight\"]).sum())\n", + "\n", + "_avail_groups = [h for h in HEATING_ORDER if h in bat_u0[\"heating_label\"].unique().to_list()]\n", + "_groups: dict[str, pl.DataFrame] = {label: bat_u0.filter(pl.col(\"heating_label\") == label) for label in _avail_groups}\n", + "\n", + "cs_rows: list[dict[str, object]] = []\n", + "for name, gdf in _groups.items():\n", + " g_cust = cast(float, gdf[\"weight\"].sum())\n", + " g_rev = cast(float, (gdf[\"annual_bill_total\"] * gdf[\"weight\"]).sum())\n", + " g_cos = cast(float, (gdf[\"cost_of_service\"] * gdf[\"weight\"]).sum())\n", + "\n", + " # Net cross-subsidy: sum of weighted BAT for the group.\n", + " # Positive = group overpays (cross-subsidizes others).\n", + " # Negative = group underpays (receives a cross-subsidy).\n", + " g_net = cast(float, (gdf[\"BAT_percustomer_total\"] * gdf[\"weight\"]).sum())\n", + "\n", + " cs_rows.append(\n", + " {\n", + " \"Customer group\": name,\n", + " \"% of customers\": round(g_cust / _total_customers * 100, 1),\n", + " \"% of delivery revenue\": round(g_rev / _total_revenue * 100, 1),\n", + " \"% of cost of service\": round(g_cos / _total_cos * 100, 1),\n", + " \"Net cross-subsidy ($M/yr)\": round(g_net / 1e6, 1),\n", + " \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n", + " }\n", + " )\n", + "\n", + "cs_table = pl.DataFrame(cs_rows)\n", + "print(\"Cross-subsidization by customer group (upgrade 00, annual, weighted):\")\n", + "print(\n", + " f\"System-wide check: sum of net cross-subsidy = ${sum(r['Net cross-subsidy ($M/yr)'] for r in cs_rows):.1f}M (should be ~$0M)\"\n", + ")\n", + "display(cs_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a0000023b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Per-customer cross-subsidization by fuel type (upgrade 00, annual, weighted):\n" + ] + }, + { + "data": { + "text/html": [ + "
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Customer groupWeighted households% of customersMean annual bill ($/yr)Mean cost of service ($/yr)Mean BAT ($/yr)Median BAT ($/yr)% overpaying
stri64f64f64f64f64f64f64
"Natural gas"432180459.61496.01642.0-146.0-236.029.3
"Oil"152530621.01736.01816.0-80.0-180.035.5
"Propane"2821993.91716.01798.0-81.0-128.039.1
"Electric resistance"6545049.03404.02433.0970.0488.073.6
"Existing heat pump"1983682.73450.02514.0935.0466.067.9
" + ], + "text/plain": [ + "shape: (5, 8)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 Customer \u2506 Weighted \u2506 % of \u2506 Mean \u2506 Mean cost \u2506 Mean BAT \u2506 Median \u2506 % \u2502\n", + "\u2502 group \u2506 households \u2506 customers \u2506 annual \u2506 of service \u2506 ($/yr) \u2506 BAT \u2506 overpaying \u2502\n", + "\u2502 --- \u2506 --- \u2506 --- \u2506 bill \u2506 ($/yr) \u2506 --- \u2506 ($/yr) \u2506 --- \u2502\n", + "\u2502 str \u2506 i64 \u2506 f64 \u2506 ($/yr) \u2506 --- \u2506 f64 \u2506 --- \u2506 f64 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 --- \u2506 f64 \u2506 \u2506 f64 \u2506 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 f64 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Natural gas \u2506 4321804 \u2506 59.6 \u2506 1496.0 \u2506 1642.0 \u2506 -146.0 \u2506 -236.0 \u2506 29.3 \u2502\n", + "\u2502 Oil \u2506 1525306 \u2506 21.0 \u2506 1736.0 \u2506 1816.0 \u2506 -80.0 \u2506 -180.0 \u2506 35.5 \u2502\n", + "\u2502 Propane \u2506 282199 \u2506 3.9 \u2506 1716.0 \u2506 1798.0 \u2506 -81.0 \u2506 -128.0 \u2506 39.1 \u2502\n", + "\u2502 Electric \u2506 654504 \u2506 9.0 \u2506 3404.0 \u2506 2433.0 \u2506 970.0 \u2506 488.0 \u2506 73.6 \u2502\n", + "\u2502 resistance \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Existing \u2506 198368 \u2506 2.7 \u2506 3450.0 \u2506 2514.0 \u2506 935.0 \u2506 466.0 \u2506 67.9 \u2502\n", + "\u2502 heat pump \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Per-customer cross-subsidy table, with fossil fuel broken out by fuel type\n", + "FUEL_GROUPS: list[tuple[str, pl.Expr]] = [\n", + " (\"Natural gas\", pl.col(\"heats_with_natgas\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n", + " (\"Oil\", pl.col(\"heats_with_oil\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n", + " (\"Propane\", pl.col(\"heats_with_propane\") & (pl.col(\"heating_label\") == \"Fossil fuel\")),\n", + " (\"Electric resistance\", pl.col(\"heating_label\") == \"Electric resistance\"),\n", + " (\"Existing heat pump\", pl.col(\"heating_label\") == \"Existing heat pump\"),\n", + "]\n", + "\n", + "per_cust_rows: list[dict[str, object]] = []\n", + "for name, filt_expr in FUEL_GROUPS:\n", + " gdf = bat_u0.filter(filt_expr)\n", + " if gdf.is_empty():\n", + " continue\n", + " g_cust = cast(float, gdf[\"weight\"].sum())\n", + "\n", + " per_cust_rows.append(\n", + " {\n", + " \"Customer group\": name,\n", + " \"Weighted households\": round(g_cust),\n", + " \"% of customers\": round(g_cust / _total_customers * 100, 1),\n", + " \"Mean annual bill ($/yr)\": round(weighted_mean(gdf, \"annual_bill_total\"), 0),\n", + " \"Mean cost of service ($/yr)\": round(weighted_mean(gdf, \"cost_of_service\"), 0),\n", + " \"Mean BAT ($/yr)\": round(weighted_mean(gdf, \"BAT_percustomer_total\"), 0),\n", + " \"Median BAT ($/yr)\": round(weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5), 0),\n", + " \"% overpaying\": round(\n", + " cast(float, gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum()) / g_cust * 100, 1\n", + " ),\n", + " }\n", + " )\n", + "\n", + "per_cust_table = pl.DataFrame(per_cust_rows)\n", + "print(\"Per-customer cross-subsidization by fuel type (upgrade 00, annual, weighted):\")\n", + "display(per_cust_table)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000024", + "metadata": {}, + "source": [ + "### 4c: Delivery BAT by heating type (upgrade 00 vs. upgrade 02)\n", + "\n", + "The grouped bar chart shows weighted mean total BAT across both upgrade scenarios.\n", + "Under upgrade 00 (current rates, baseline HVAC), the direction and magnitude of the\n", + "BAT for each heating group set the baseline. Under upgrade 02 (same rates, all\n", + "buildings now have heat pumps), the BAT distribution shifts as load profiles change." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "a0000025", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted mean total BAT ($/yr) by heating type (Upgrade 00 baseline only):\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n", + "(Deprecated in version 1.0.0)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 6)
heating_labelmean_bat_totalmean_bat_deliverymean_bat_supplymedian_bat_totalpct_overpaying
strf64f64f64f64f64
"Fossil fuel"-127.147981-114.601156-12.546825-216.61406131.387175
"Electric resistance"970.407277872.59311697.814161488.41838873.5572
"Existing heat pump"935.496001854.70450280.7915465.81597667.941825
" + ], + "text/plain": [ + "shape: (3, 6)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 heating_label \u2506 mean_bat_total \u2506 mean_bat_deliv \u2506 mean_bat_supp \u2506 median_bat_to \u2506 pct_overpayin \u2502\n", + "\u2502 --- \u2506 --- \u2506 ery \u2506 ly \u2506 tal \u2506 g \u2502\n", + "\u2502 str \u2506 f64 \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Fossil fuel \u2506 -127.147981 \u2506 -114.601156 \u2506 -12.546825 \u2506 -216.614061 \u2506 31.387175 \u2502\n", + "\u2502 Electric \u2506 970.407277 \u2506 872.593116 \u2506 97.814161 \u2506 488.418388 \u2506 73.5572 \u2502\n", + "\u2502 resistance \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Existing heat \u2506 935.496001 \u2506 854.704502 \u2506 80.7915 \u2506 465.815976 \u2506 67.941825 \u2502\n", + "\u2502 pump \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": { + "image/png": { + "height": 450, + "width": 1050 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "bat_summary_rows: list[dict[str, object]] = []\n", + "# Only analyze BAT for baseline (Upgrade 00) - cross-subsidization only applies to current rates\n", + "bat_s = add_heating_label(bat_by_scenario[\"Upgrade 00\"])\n", + "avail = [h for h in HEATING_ORDER if h in bat_s[\"heating_label\"].unique().to_list()]\n", + "for group in avail:\n", + " gdf = bat_s.filter(pl.col(\"heating_label\") == group)\n", + " total_w = cast(float, gdf[\"weight\"].sum())\n", + " bat_summary_rows.append(\n", + " {\n", + " \"heating_label\": group,\n", + " \"mean_bat_total\": weighted_mean(gdf, \"BAT_percustomer_total\"),\n", + " \"mean_bat_delivery\": weighted_mean(gdf, \"BAT_percustomer_delivery\"),\n", + " \"mean_bat_supply\": weighted_mean(gdf, \"BAT_percustomer_supply\"),\n", + " \"median_bat_total\": weighted_quantile(gdf, \"BAT_percustomer_total\", 0.5),\n", + " \"pct_overpaying\": (\n", + " cast(\n", + " float,\n", + " gdf.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n", + " )\n", + " / total_w\n", + " * 100\n", + " ),\n", + " }\n", + " )\n", + "\n", + "bat_summary = pl.DataFrame(bat_summary_rows)\n", + "print(\"Weighted mean total BAT ($/yr) by heating type (Upgrade 00 baseline only):\")\n", + "display(\n", + " bat_summary.select(\n", + " \"heating_label\",\n", + " \"mean_bat_total\",\n", + " \"mean_bat_delivery\",\n", + " \"mean_bat_supply\",\n", + " \"median_bat_total\",\n", + " \"pct_overpaying\",\n", + " )\n", + ")\n", + "\n", + "avail_bat = [h for h in HEATING_ORDER if h in bat_summary[\"heating_label\"].unique().to_list()]\n", + "bat_plot_data = bat_summary.with_columns(\n", + " pl.col(\"heating_label\").cast(pl.Enum(avail_bat)),\n", + ")\n", + "\n", + "(\n", + " ggplot(bat_plot_data, aes(x=\"heating_label\", y=\"mean_bat_total\"))\n", + " + geom_col(fill=SB_COLORS[\"sky\"], width=0.6)\n", + " + geom_hline(yintercept=0, color=\"#666666\", size=0.7)\n", + " + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n", + " + labs(\n", + " x=\"Baseline heating type\",\n", + " y=\"Weighted mean total BAT ($/year)\",\n", + " title=\"Mean total BAT by baseline heating type (Upgrade 00)\",\n", + " )\n", + " + theme_switchbox()\n", + " + theme(figure_size=(10.5, 4.5))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000026", + "metadata": {}, + "source": [ + "### 4d: Monthly bill pattern\n", + "\n", + "Weighted-mean household energy bills by month, before (upgrade 00) and after (upgrade 02)\n", + "the heat pump retrofit. Winter months show the largest divergence: gas-heated homes have\n", + "high gas bills under upgrade 00, while after the retrofit (upgrade 02) gas bills\n", + "effectively drop to zero and electric bills increase due to heat pump load." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000027", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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x8ccfGxUrVnTZ/rp16xrffPNNmueu1feIYRjGqVOnjMGDBxuVKlUygoKCHNLMnz/ftLwNGzYYnTp1cnkMNm7c2FiwYIE9z+LFi41KlSo55OnZs6exbNkyyzK+/fbbNPfVuHHjTPMGBwcbcXFx7u90F/sqpa2HDx82unXrZrndISEhxuOPP+7y/iOFq2uHGXe/+1Lr2bOnaZ6VK1em2b5ffvnFuOuuu1x+xpKMBg0aGHPmzEnzWvlfZPWZpSeP2edsdl/80EMPGQkJCca0adMsv38kGUWLFjXefPNN48qVK25vz7Vr14y3337b5fdnaGio8eyzz9rPvevXrxuPPfaYkTdvXsvtsdoOwzCMXbt2GQ8++KARGRnpdCyeP3/etJ2LFy822rZtm+ax26JFC2P58uVub79hGMbVq1eN0aNHG8WLF7cst0aNGsbChQstryVW53hq3r5XcdfLL79sWtfQoUPdLuP8+fOm98/+/v7G9evXHdIuXbrUtL6oqCiP2l2nTh3Tcjz97jUMwxgzZoxpWXny5DGuXbuWZv4dO3ZYfma//fabU/pt27YZnTt3NgIDA10er1WrVjUmT55sJCQkeLxNGZWe+zZPvuNc3WNt3brVaNeuneX5HBQUZDzwwAPGvn37PNqmxYsXGy1btrTc335+fsZdd91lrF692p5nxowZRmRkpGGz2Uy3x9V2XLx40XjjjTeMatWqGblz53ZY//7775u28dixY8bTTz9tFChQwOWxUaJECWP06NHGjRs3PNoHy5YtM5o0aeKwPalf+fLlM1577TXjypUr6fr7zzBu/U23ePFio2nTpi6vyaVKlTLGjh1rXL582aNtsHLgwAHTesLDwz36e7pLly6m5UyfPt0r7TQMw+jRo4dpHe+++67X6gAAAAD+ywhoQLawCmj47LPPTP/InjBhgmVZzz77rFP65s2bW9bhTkfb7NmzjTx58rj8weH2V7ly5Yzt27e7LNeqI+q1115zq44cOXKY/tGdlQENhQsXzlBbU/Pkx/4PPvjANK3NZjP++usvl/WMHDnSNG+RIkWcfpB1l1WncUREhFv7p0OHDm51PiQnJxvvv/9+mj+O3v6qW7eucfjwYY+3wZsBDVeuXDE6duzo8bHYt29fIzEx0WU71qxZYxQpUsSjcu+77z7j4sWLlmVanUfufqYhISHGokWLXLY7RXR0tFGvXj2P2h8YGGiMHz8+zc6OjAQ0LFu2LM0fW1Ne/v7+RqtWrUzXeSug4dixY0b9+vU92k+VKlUy9u/fb1mm1Y/UH330kdt1jBw50q32//7770aFChU8an/jxo2NkydPWpZp9T1y4sQJlwF4twc0JCcnGyNHjrT88dvs1bt3byMhIcH0GOnZs6eRnJxsGRDSokULl/sqKSnJstN20KBBbu1vd/aVJKNWrVpGaGioW9scFBSU5vHsqwENCQkJxlNPPeXR8SfJ6Nixo1udff8l2RHQ0L59e+Ouu+5y+3MrXry4W8Gkhw4d8ui6FBERYaxdu9aYOHFimttjFdCwcuVKI0eOHJZ13B7QkJCQYPTu3dvjY/ell15yKyBg+/btLgMMbn+1bdvWo3M8RWbcq7irXbt2puXPmTPHo3KsgvFuP9bGjh1rmq5fv34e1WcVkD5+/HiPyjEMw4iNjbW8f549e3aa+QcNGmSat3Llyg7HWXJysjFq1CiPvk8lGfXr1zfOnj3r8XZlRHYFNHzwwQdpBialvPz8/Iy33347zXM5MTHR6NOnj9v722azGa+88opx9uxZ07a4E9Bw5swZl4GvZgEN8+fPN0JCQjw6NqpXr27Exsam+XlevXrVePDBB90ut3bt2qb3P2n9/Xf+/Hmjffv2Hm1D0aJFjQ0bNqS5DWn55ptvTMu/++67PSrHKsDp2WefzXAbDeNWUHHBggXTfb0BAAAAkLasH8MacMFms6lr165Oy7/77jvT9MnJyfr++++dlpuV4a5XX31VjzzyiC5fvuxRvkOHDqlhw4b68ccfPcoXGxurN954w620CQkJ6tOnj5YtW+ZRHd7k7jQe3m7rE088YTrUrGEY+uSTTyzzJSYmaurUqabrBgwYoJw5c3qlfSncHVJy0aJF6tu3r9OwuqklJyerV69eGjRokOLj4z1qx9atW1W7dm1t2rTJo3zeYhiGevToYTl/syuffPKJy3Pixx9/1J133qlTp055VO6CBQvUrFkzXb161aN87n6mV65cUdeuXbV7926X6bZs2aLatWt7PJ9qfHy8XnjhBfXo0UPJycke5XXHd999p3bt2un8+fNupU9KStKKFSu83o4U0dHRqlu3rsfH8B9//KH69evr999/9yjfM88843bakSNHavr06S7TbN68WfXr19fBgwc9ase6desUFRXl8fE9ZMgQ/f33326n/+CDDzRy5EiX16Dbff755+rXr5/lMWKz2fTSSy+Zrvvtt9+0b98+y7KXLl2q6Ohop+UBAQEaOHCg2210x44dOxQXF+dW2ps3b6p3796aMGGCV9uQFYYNG2b5/efKwoUL1a9fv0xoETzx008/WQ6zbebYsWNq2rSp1qxZY5nm4sWLatmypUfXpZMnT6pLly5avny523lSS0pKUo8ePUynP7Dy7LPP6vPPP/e4rnfeeSfNfOvWrVOTJk109OhRt8tNz71sVt+r3M7seirJ46H3raaiOXz4cLbW525ZDzzwgOm6r776ymXexMREff3116br+vfv7zCk/qRJk/T666979H0qSZs2bVKXLl0y5Z7O1wwYMMDt7UxOTtaIESM0YMAAl+mGDBmiadOmud0GwzD01ltv6cUXX0z3Ph89erR27Njhdvo1a9bogQce0JUrVzyqZ/fu3eratavLY+r69etq1aqVvv32W7fL3b59u9v3PynOnTunhg0b6qeffvIo34kTJ9S8eXP9+uuvHuW7nS9eW263e/du3XXXXTp37pzp+ubNm2e4DgAAAAASAQ3wOWbBCBs2bNCxY8eclq9fv96ps9Fms6lz587pqvvTTz/VW2+9la680q2Oj0ceeUQ7d+5MdxlpMQxD/fv3V2JiYqbV4S3ebGtwcLCee+4503Wffvqpbty4Ybruxx9/1IkTJ5yWh4SEZHuHzddff61Vq1ZZrn/99dc1Y8aMdJd/4cIFderUyaMf7b1l5cqVmj9/frrzjxkzxjR4Zv/+/Xr00UfTfUzt2rVLffv2TXe70nL9+nU9//zzluuPHj2qe++91+2gATNff/21Xn311XTnN7N//3717t1bSUlJXi03va5du6b777/f446gFBcvXlTnzp09/gHZE0OGDNGFCxdM150+fTpD9R87dkwPPvig2z+4X7161aPz7fTp0xoxYoTpugYNGmjgwIF6/PHHVaRIEaf1n376qcuyu3TporJly5qu+/jjjy3zTZ482XT5I488opIlS7qsMysMGzbM5fXa1xw6dEjvv/9+uvPPmDFD27dv92KLkBWuX7+uhx56SKdPnzZd/9Zbb5kGPuXNm1ePPvqohgwZotatWzutj42N9bgzK8Xq1atN7+GtbNy40WWgalpeeeUVy2vn6dOn9eCDD+ratWvpLt8dvnCvcunSJdPlYWFhHpVj1Ql4e/lZXZ+7nn76adPlS5cudRmkvWzZMtPzKDg4WD169HBo18svv5yutknSqlWr9MMPP6Q7/7/ZpEmTLINKdu/erQ8//NB0XevWrTV48GD16NFD+fLlc1qf1n2MK1btMWMYhvr27Zvue+vVq1e7DGp7/vnntXHjxnSV7a7k5GR169ZN+/fvT1f++Ph4Pfzww6Z/i7vLV68tx48f1/Tp09WyZUvVqFFDu3btMk3XoUMHRUREpKsOAAAAAI4IaIDPiYqKUqlSpZyWm43EYDZyQ9OmTdP1R+PJkydddkRKUqlSpdS0aVMVLFjQMs21a9f0xBNPpPvJj1KlSql58+am+yDF4cOH0/Xku7dldVufffZZ5c6d22l5XFyc5dMpVh1offv2Vf78+b3SLjP58uVTo0aNVL16dQUGBlqms+pw2rlzZ5rBNeXLl1fjxo2VJ08eyzSxsbFpPuGUGaxGVcmZM6c6d+6sYcOGaeDAgWrTpo38/f2d0iUkJJj+wDtkyBDT0VNsNptatGihfv36adCgQerUqZPpj5jSrR8jt2zZ4uEW3aqjfPnyat68uctrzKpVqyyf3howYIDLTvo8efKocePGKl++vMu2jB492quBU0OGDHHZ+Z4rVy7Vr19flStXlp9f5t86fPTRR5Y/DFarVk09e/bU0KFD9fDDDysyMtI03cGDBy07ydMSERGh5s2bq1y5cg5PYaZ26dIlyyeBR40apePHj5uuq1+/vp544gm98MILeuCBByyfMFuzZo3p956ZK1euWAZ1mZk3b55ph967776r9evX6/3339enn36qgwcPqkGDBm6XK0n+/v4aOnSo6boZM2aYnr9HjhzRokWLTPO88MILHtWfHqVLl1aTJk0srxnSrR/1n3vuuX/Mk7Tz5s0zXR4QEKB77rlHQ4YM0ZAhQ9SxY0cFBQWZprW6jiN7+Pv7q3r16qpbt67lZybdup998803nZYbhqGZM2c6LS9durQOHjyor776SuPHj9fy5cs1d+5cr7XbKrjCiqsnritVqqRWrVqpYcOGyps3r2makydPatu2babrRo0a5bJjLTAwULVr11atWrWUI0cOj9qdWnbeq6Sw6qSz6tSzktGAhsyqz10p9+K3S0pK0uzZsy3zffnll6bLu3fv7vCZLVmyxDJApkWLFho4cKCGDh2qrl27Wt6ve/N8+6coVqyYmjZtmman9KBBg0z376xZs0xHL/j222+1fPlyTZgwQTNmzNDBgwdVunRpr7Xbk+vZ2rVr9ccff5iuCwsLU9OmTdWyZUuX7bP6G3rLli1pjhQWGRmZ5u8Wafn+++8tgyrKlSunbt26aejQoXr00UdVqVIl03RnzpzR22+/ne42+Mq1JTk5WatXr9aLL76oGjVqqHjx4urTp49WrlxpmScoKEijR4/2qJ0AAAAAXMiemS7wX2c2x65Szd/5wgsvOK1r0qSJQxlJSUlG8eLFndL973//c6uO2z3zzDOWc0C2b9/eOHbsmD1tcnKysWHDBpdzEM+dO9epDqv5vPX/565cu3atQ/pFixYZefPmNU3fv39/e7ozZ84YW7ZscXhNmTLFcltuT7tlyxanOeczq62ppWce8QEDBpjmqV+/vlPa/fv3m6YNCAgwjh49almHO0qVKmVats1mM0aPHm0kJCTY0x49etRyW/PkyeOQNoWreUp79eplnDt3zp42KSnJ+Omnn1zO07x582a3tiGtOVSttvt2d999t2m6nTt3OqWdP3++adohQ4Y4pNu+fbtpuooVKxq7d+92KjcuLs545JFHTPN07drVKb3VHMGSjBo1ahh79+61p01OTjY+//xzw9/f3zT92LFjncrfvHmzZfkRERHGzz//bCQlJTm0v2fPni6vSxn5jFLs2rXLso68efMaX331lZGYmGhPf+XKFct5pVNeruZiTsuNGzeMsLAwpzJz5cplLFiwwGlO5fj4eGP8+PGm7ShcuLDDPjUM63mRJRmhoaHGDz/84JB+/fr1pt81kox27do5tf/kyZOmc3aHhYUZa9ascUp/9epVY/DgwablR0VFOaV3dW0uUKCAMWbMGGPp0qVO1/jU14znnnvO9Bg0m696yZIlpnX16NHD2Lx5s738w4cP2/Ncv37dKFy4sGm+yZMnO9Xx8ssvu71/PeFqX6WcQ8ePH7enT05ONpYvX275eUsyFi5c6FSPJ/OLG0b6vvusrgUrV640Td+vXz/T9D/++KNT2m3btpmm7dKli8v9+19i9ZmlJ4/Z52x1zyrduq946aWXjGvXrtnTx8fHG5MmTTJy5sxpmidnzpzG6dOnHeqIi4szTfvJJ5+Ytr9BgwZOaYOCgoxly5Y5XFtu3Ljh1nbccccdxtSpU41Vq1Y5XZ9S3weVK1fOKW+OHDmMdevWObTv8uXLRseOHU3r+vbbb522JzY21ggKCjJNHxgYaEyYMMG4efOmPf2NGzeMd955x8iRI4flNpmd45l9r+IuPz8/0zLj4+M9Ksfq+vzmm286pGvZsqVpumXLlnlU39KlS03LadWqlUflpDZ58mTTMmvXrm2a/vz585bHyo4dOxzSvvPOO6bpPvzwQ6dyjxw5YoSEhDilrVOnTrq3zVNW31eu7ts8+Y5zdY8l3fp77cCBA/b0ycnJxtatW43KlStb5pk0aZJTPWbnfsOGDU3bb/U38bBhwxyuQ6nvB1xtR65cuYzhw4cbP/30k8N90JYtW4xTp07Zy3jrrbdM8w8YMMDhvjo5Odn48ssvTdPec889ptvUuXNny/bdc889Tr9brF271ihbtqxlHqu//6pXr+6U1mazGVOnTnXYBsO49ffo119/bfq3UVBQkBEXF2daR1oef/xx0zZPnTrVo3IOHDhgWk7ZsmVd5jtx4oQxevRoo0yZMi6P7dtfAQEBxsyZM9O1zQAAAADMBQjwQV27dtX48eMdlq1bt04nTpxQ0aJFJd2ad/T2IWzTO91EUlKS5syZY7ru4Ycf1qxZsxye0rXZbGrQoIG2bdumatWqKSYmxinfrFmzLOdtNfP111+rcePGDsvat2+vV155RcOGDXNKv2fPHvv/CxUqpEKFCjmst3raulChQqpbt67b7fJ2WzNq8ODB+uijj5yG8d20aZO2bdumOnXq2JdNmTLFtIzu3burePHiXmtTak8++aTT0LPFixfXjBkzVKFCBd28edNh3eXLl3XkyBGHIdrPnTunxYsXm5b/4osv6p133nFY5ufnp3vuuUfbtm1TpUqVTJ80mT17tqKiotK7WR4ze+o8ODjY9Omdjh07mj7dcvtw9wsWLDCta968eapcubLT8oIFC+qzzz7T5s2b9ddffzmsW758uZKSkkxHh7hdUFCQ5s2bpzJlytiX2Ww29erVS1u2bDEdBcTsmJ81a5Zp+fny5dO2bducRn0oWLCgvvjiC4WHh+vdd991yrd48WKdO3cuQ09eSbKcqsDf319r1qxxerIxODhY7733nooXL64hQ4ZkqG4z69ev15kzZ5yWT5gwQZ06dXJaniNHDg0ZMkQ7d+50evo4NjZWu3btUq1atdyq+8MPP9S9997rsKxhw4Z677339OCDDzqlN/ucFy9erPj4eKflX375pZo0aeK0PHfu3Hr33Xe1ZcsWrVmzxmHdli1bFBcXp9DQ0DTb7ufnp19//VU1a9ZMM63ZKAOFCxc2HY3CagSJli1bWl5TcubMqQEDBmj48OFO6z766CP17dvXXtfNmzcth382+z7xli5dumju3LlO3+2tW7fWtm3bdMcdd5hODTN//nx16NAh09rlLVafm9n3f+3atbV27VolJCQ4LPd0FKObN2969fs+sxQtWtR+L/lP8MEHHzhNuZUjRw49++yzuuOOO9SmTRunPDdu3NCSJUschsa3Gl3E6lgxW16kSBHT+tJSokQJbd26VcHBwWmmHTVqlFNbQ0ND1ahRI4dlISEhevHFF02fYL569arTssWLFzvdg6VYsGCB2rVr57AsKChIL774osqXL68uXbqk2e7UZZnJ7HuV2+XOndv0b4ELFy54NFS71dRKt3+WZiOoucqf0fo80b17dw0bNsxp1Izt27fr999/V5UqVRyWz5071/RYadSokdN3rCfX2pIlS+q3335zakfOnDnd2Yx/vHr16mnNmjUOo5/YbDbVqVNHW7duVfXq1XXo0CGnfPPnz9ezzz7rsMzqPsaM1fJ27dql62/ib7/91q37gNatW5uOYtixY0eHc9pms+mxxx7Tm2++6bT9ZteyGzdu6Oeffzat86GHHtLs2bOd7m0aN26s7du3q3LlypYjiN0uOjpau3fvdlo+dOhQ9enTx2m5n5+funXrpj/++MNplMGbN29q1apVuv/++92qO7XsurYYhqGPP/5Yw4YN83iaotDQUH333Xdq0aKFR/kAAAAApCG7Iyrw35TW6AnJycmmTxmnfkLD7InWZs2auV1Hahs2bDBNmzt3boenNczMmzfPNG9ISIjTU1BWT4tWrVrVsnyrUQYqVqzosl1WdfXs2dNlvqxsa3qeUjUMw3jsscdM8z3++OP2NFevXjXy5ctnmm7Pnj1u7QNXrJ6C37p1q2Ues6cdJRkbNmxwSDd79mzTdMWKFTOuXLnisl3vvfeead477rjDrW3w1ggNH3zwgWm6xo0bG1OmTDEOHDjg8FSnOxo3buxUXrly5dLM16dPH9O23P6kpNUTaB06dLAs2+rp9bvuusspbcWKFU3TTpw40WX7L1++bERERJjmnTNnjlN6T0dosDoPBwwY4LJd8fHxRqVKldy+zrprxIgRpmWmftrMzNdff22a7/anJK2eusuTJ4/pCAWGYRjXrl0zzRMUFOSUtnv37k7pcubMmeZTsW+//bZpHbc/UW91bW7UqJHL8lMze1o1MDDQiImJcUpr9fTpokWLXNZx/vx5I0+ePKZ5V69ebU83a9Ys0zRRUVGWn4e7rPaV1bamZnUNi4yMdErriyM0/PDDD6bpq1WrZrz//vvGnj17jKtXr7rcB55K68lcX3lZfS6uZNcIDVWqVDEdxSm1+++/3zRvr169HNIlJyebjpzSt29fpzIvXLhghIeHO6WtW7euy7ZYbcfw4cNd5nPH9evXjcuXLzu89u7da1qf2XeQ1TnUqVMnl/UmJycbrVu3dvtYyux7FXcVLVrUtLzUo025o0uXLqblTJ8+3SFdt27dTNOljJznrkmTJpmW8+ijj3pUzu2efvpp03JffPFFp7RNmjQxTfvVV185pbUa5SoyMtIYPXq0sXXrVuPy5csZars3ZecIDRs3bnTZNqvvraCgIKe/GV588UWndOHh4caFCxecyrUascjVuWC1HUWLFnW5De5ITEx0upZdvnzZ9NrRvHlzp/xW9za5c+dO81555syZpnnN/v6bNm2aadrbR2m83bp160zzDR482JPdZDd8+HDT8l544QWPylm4cKFpObePAppi6NChlsey1StnzpzGk08+meZvGQAAAADSJ/MnwgbSwWazqWvXrk7LU+ZzNgzDdG5nszzuOHjwoOny+vXrp/kUX/v27RUQ4DzYyZUrVxQbG+tW/RUrVrRcZzU/vCfzpXuTL7TVan722bNn25+onT17ti5evOiUpn379qpatapX25OaN/aP1fHYtm3bNJ9Qs3ry5dChQ1k693vXrl1NRw5Yt26d+vXrp4oVKypnzpwKCwtTzZo19fjjj+uLL75w+dTQkSNHnJYdOnRINpvN5ctqPm5X82in5o3PNDk52fSpM8n6M0sREhKitm3bmq77888/XeZ1x+HDh02XpzXaTY4cOdS+ffsM1387s89ZujXSiavPuXv37qb53P2cK1SoYDpCgSTlypXLacQQSaZPcJq1/8aNGwoMDHTZ/hEjRmSo/WZPAVoxm8s7Pj5e99xzj5YvX65Lly7pxIkTmjhxol5//XWn/DabLc2RIPLnz69+/fqZrks9qonZCCfSrdEZrD6PjKpVq1aa+8tsNBDp1ueblJSUGc3yqlatWqlcuXJOy/fs2aNBgwapWrVqCg4OVsGCBVW1alV169ZNU6ZMsbxOIXt06NDB9B4zNatj9fZru81m0xNPPOGU7pNPPtGIESMUHR2tq1evauPGjerYsaPpXPGpR8HyhCfXJ+nWd+aSJUvUu3dvNWnSRBEREcqVK5fy5Mnj8PLkfi6933U2m0333Xef2/Vk173K7fLmzWu63GwEJFfMjgOz8rO6Pk/179/fdPnMmTMdrul//fWX1q5d65SuUKFCpiPvValSxWnUOkmKiYnR8OHDVbduXeXJk0f58uVTpUqV1LlzZ33wwQf/iNFsvKlw4cKqX7++yzTt2rUzvd7dvHnT6Tzo2bOn08glp0+fVvv27bVhwwZduXJFMTExevXVV01H7MuXL5/D6Hju8vRaJt269gwfPlzt2rVThQoVTK9lefLk0bp169wuz0z9+vVVrFgxl3mtvi/MWN2PN2nSxOW1zOx8kP451zLp1m9OZqPjmbHZbKpfv77GjBmjv//+W9OmTbP8+xAAAABAxhDQAJ9lNrz36tWrderUKW3ZskV///23wzqbzebRkLCpWQUemA2Pf7vAwECVL1/edN2pU6fcqj8kJMRyXVBQkFtlZBVfaGvVqlXVsWNHp+XXr1/X559/LsMwNHnyZNO8mTmEueSd/ZOR47FkyZKmQ3MmJibq3LlzbtXvDREREZo6dWqa6c6ePatdu3bp888/V+/evVWuXDm98847TkOfS57/cOVO3e7wxmcaFxdn2gkaHBzs1vQnZsNUS+5fY1yx2g/uHG9W7coIX/ycJfc/6+xqf2BgoNtlhoaGatKkSU7L9+3bp7Zt2ypfvnwqVqyYBg0aZBq00aVLlzR/NJekgQMHmrbr+++/16lTp7Rnzx7TjqNy5cqla1hid91xxx1ppilRooRpAJlhGIqLi8uMZnlVcHCwvvrqK4ehvc2cP39ev//+u2bPnq3+/furYsWKeuGFF0yHuUbWc+dYtboOm12LRowYYZp+9OjRKlOmjEJCQtSwYUOn6W+kW9MQWXUKp8WT61NsbKzatGmjdu3a6YsvvtC6dev+Ud912fUdcDurDjWrTj0rVttTunTpbK3PU1WrVlWzZs2clh8/fly//fab/f1XX31lmv/xxx83nRrC399fM2bMUL58+VzWf+nSJe3fv1/z58/XwIEDVb16dfXq1Svdn+8/jTvXshw5cqhChQqm624/LipVqqRXX33VKd26devUqFEj5cmTR6VLl3aa+iBFnz590jXVhyfXMsMwNGbMGFWsWFFjxozRkiVL9Oeff5r+jeOJjFzLQkJCVLJkSbfq+a9ey5KSktL8vSBfvnzq2rWrvvzyS8XGxmrjxo166aWXPJrOBwAAAIDnCGiAz6pbt67TUxCGYWj+/PmmozM0bdrUaQ56/Hu9+OKLpssnT56szZs3a9u2bU7r6tevr6ZNm2Z20/D/denSRWvXrvWoI+DGjRt6+eWX9dhjjzmtS+spVU/5+fnGV2BmPYX+T/VP/5z/Ke3v2bNnugK8ypYtq//9739upS1atKjpuZyQkKDp06dbBp698MIL6ZozHo4aNGigHTt2qFGjRm7nSU5O1oQJE3TXXXf9I0aigGdy586tpUuXmo6glJZx48apRo0amdCq/3Pz5k3dfffd+vXXXzO1nszkK98BtWrVMl1udn9s5eLFi6YjQfn7+zuNjuGN+iRp69atpsutyveEVUBOShCDYRiaMWOG03qbzaa+fftallumTBnt2bPHNNjalS+//FINGjTQlStXPMqHW1577TXTByDSUq9ePb3xxhuZ0CJH48eP1/Dhw5WYmJjpdWUGX7+Wbd++3aPRB929tvz666+Kjo42TVuhQgV98803On36tL799ls99thjBDEAAAAAWcg3enMAE1bTTsydO1dz5851Wp7e6SYkKTw83HT5H3/8kWbe+Ph4y2HfCxcunO42wbXGjRurSZMmTssPHTqkXr16meZ58cUX/xGdxxk5Hv/++29du3bNabm/v79bHRhmT2On5umPro0bN9bOnTu1atUqvfXWW+rYsaNq1qypwoULu/xha86cOfrmm28clnn7B6NcuXJ5tTxXChYsaLq9V69e1dGjR9PMv2/fPtPl3rjGFCpUyHS5O8ebVbsy4p/8OUv/jPZfvnxZDzzwgMaNG+d2nhw5cujxxx/XunXrPDruhg4danrdnTx5sumTsOHh4aZBEN60f//+NNMcPXrUdJQCm82m0NDQzGhWpqhSpYrWrFmjTZs2afz48ercubPq1KmjYsWKueysWLduncaPH5+FLf1ncvWd6Y1OSneOVavrsNm1aP78+YqKivJoxKaKFSvqm2++0eDBg93Ok15TpkzRzp07TdcVKlRIrVq10sMPP6z+/ftr+PDhevnll90uO6u+63zlO8CqE3DRokUyDMOtMpYsWWIa2FSpUiWnp9ut6tuyZYvbI2ycPHnSMgAirWmO3NG5c2fTe+zvvvtOV69e1bp160w7Mtu1a6cyZcq4LLtEiRL64YcftGvXLk2aNEkPP/yw6tWrp5IlS7p8qv+vv/6ynEovK/nCtSwhIcFy2r3bz6uTJ0+qZcuW+vbbb91uQ3BwsAYNGqQVK1Zk+r3hyZMn9dprr5muCwwMVN26dXX//ffriSee0AsvvKC3337b7SkwMnItu3LlitMol1Z85VpWrlw501HUYmNjLYMUbnfjxg398ssvputuv7asXr3aNF2pUqW0ZcsWPfjggx6N1AEAAADAewhogE8ze+pi5cqViomJcViWkekmJFlOGbFp06Y053v86aefTJ+8CA4ONp1zHd5jNUqD2Y9mFSpU0L333pvZTfIKq+Nx2bJlaQ4DPn/+fNPl5cqVc+pQNxtCPzY21vJHzXPnzqVrqPUcOXKoWbNmGjFihH788Uft2LFDp06d0vXr17Vy5UrLzsvbOzvNhqEtU6aMkpOTZRiGxy9P5sTOKH9/f8sfKhcsWOAy75UrV7R8+XLTdVbHiieshnK1OpZSJCQk6Keffspw/bezGm74yJEj6fqcJ06c6PU2etr+4OBgXbt2LV3tHzhwoFfbZxiG7r33Xn3//fcu0+XKlUuVKlXSPffco5deekl//fWXPv30U4+DaCpUqGA6R/2JEydMO0mef/75TO9o2Llzp+Xc0Cl++OEH0+UlS5b8x40e4efnp3r16mnIkCH6/vvvtXXrVh07dkw3btzQxo0bNWjQINN8VkOvW4mMjEzXMZ7Vr5EjR3q8D62mnLHqGDIMw7JjzhOLFi1K8+leq2P19mG0V6xYoa5du6bZuRwREaFGjRqpe/fu+u6777Rv3750PQWdHnPmzHFaZrPZNHXqVJ06dUq//PKLZs+erY8//lhvv/226bXFSnq/6wzDsNzHZnzlXqVly5amU87s27dPP//8c5r5DcOwDGpq166d07KwsDDVrl3btJwJEya40WLpvffeMw22iIqK8kogWWBgoJ588kmn5VevXtWCBQv05ZdfmuZzd6oVm82m6tWr69lnn9Xs2bO1adMmHTlyRDdu3NCuXbv0xhtvmAaSff311x49aZ4Rnl7LJHnlWhYbG6tNmza5TLN48WLT611gYKDDKIw3b97UnXfe6TBViJk8efKoevXq6tSpk9566y0dOXJE7733XppTjHnDjz/+qBs3bjgtv/fee3X8+HFt2bJF8+bN0/Tp0/Xuu+9q+PDhbk+BYXUt27Rpk44fP55mu9xldT++evXqdF3L0vp7x4qfn5/atm1ruu7dd991q4xPP/1UFy5ccFpepEgRp5GHrI73xx9/XHnz5nWrPgAAAACZw7vjyAFeljLtRFodD02aNMnQdBP16tVTgQIFdP78eYfl165d05AhQzRr1izTJ0yvXLmiIUOGmJbZpk2bNOeuzg6XL1/O7iak6fr1626lu+eee1SlShX9/vvvaab9Jw1h3rZtW9lsNqcfdY8fP64333xT77zzjmm+EydOWHbUmP34bPbjsGEYWrx4sekP6FOnTk278ZKOHTtm+iNj2bJl1bBhQ/v7wMBAtWjRQs2bN9f8+fOdjs0dO3Y4vG/durWWLVvmsOzw4cNasmSJ6fZJt0ZQMRu62maz2fdzVmnXrp3paC6vv/66unbtankNe+ONN0wDq/z8/NSmTRu3679+/bppR3HLli1N50v/3//+p969e6t69eqm5U2aNMmtp8E81bp1aw0fPtxp+UcffaSxY8da5lu7dq1pB3n9+vVVoEABr7bRldatWzudK1evXtUXX3xh2SliGIZWrFhh+kN+ixYt0jXPs5WVK1c6nZ/lypXTwoUL3ZrjOj1efPHFNAMopFuBH+52HGXEzZs3NWTIEM2dO9f0GnD69GnLa2nLli0zrV3ufvellefChQtatGiR0/IiRYqodevW9vf+/v6qX7++6tevr+XLl2vv3r0O6ffv32953fivsepM/emnn0yDjr755huv3G/9/vvvmjx5sp577jnT9b/88otlh/ztx+qoUaOcnrZ/6623NGTIEK9eYzLCbCSEatWqqU+fPqbpzY5zKy1btjQN0lmwYIEWL15seR8xf/58y6BCM75yrxIaGqoOHTqYHh/9+/fX5s2bXQZejxkzxvLp5549e1ou3759u9PyiRMnqkOHDmrevLllfatWrbIMQLSqLz2eeuopvfPOO04BBNOmTXO675RuPZVt9bklJCQ4jSYmSSEhIQ730SmBDtWrV9fmzZudjtvLly/rr7/+cghS7dWrl2WARXR0tGWndlqsrmU///yz3nzzTafg54sXL5oGGqXH888/r7Vr15r+fXz9+nW98MILpvkaN27scI36+uuvdeDAAYc0DRs21DfffKMSJUp4pa0ZZTWqy8iRI01HWDh8+LBbf89Kt7Y1KCjIKQA95XeL2bNnm14zLl265NFUY1b3Ox999JGaNGlieV3atm2bzpw547S8evXqKlq0qNv1p9azZ0/NmzfPafl3332nWbNmqVu3bpZ5//jjD9O/KyTp0UcfdQoyMgt8kKTixYu732AAAAAAmcMAssHrr79uSHJ6ff75505phw4dapo29evDDz/MUB2GYRj9+vWzLL99+/bGsWPH7GmTk5ONjRs3GhUqVLDMM2fOHKc6Vq5caZq2Z8+eLveXWZ5SpUq5zGNVV86cOY1PP/3U2Lhxo7Flyxb7a//+/Vne1ubNm1vWsXbtWmPLli3G4cOHXdY3Y8aMNI+PwoULG9evX3dZjqdKlSplWpcrPXv2NM2zcuVKp7R333235fY8/vjjxrlz5+xpk5KSjJ9//tmIiIiwzLNx40anOp588knLz2vbtm32dMnJycYXX3xh5MqVy7L81NauXWuapkSJEsbx48ed2vHbb7+Zps+TJ49DukOHDhl+fn5O6YKDg41PP/3U6TM+efKkcd9995mW3alTJ6d2fP7556ZpX3/9dbOP0zAMw4iOjjbN07x5c6e0GzZssNx/RYsWNRYvXmwkJyfb0587d87o3bu3ZZ67777btE1Wx+bQoUONDRs2GFu2bHH4HHbs2GFZR758+YyZM2caiYmJ9vRXrlwxhgwZ4vKcs7rOuiMxMdEoU6aMabnDhg0zYmNjHdJfu3bNGDVqlGn68PBwp+PCk8/Mnf16uwsXLhh58+Z1Sufv72+MGzfOuHjxokP68+fPG3379jUtu0aNGg7HhGGk/9qc4t1333XK+/TTT7uVNyNatmzp8piRZAwcONCrdVrtq5RX+/btjRMnTtjTJycnGytWrDCKFy9umWfBggVO9Xh67UjPd5/V98c999xjrFy50uF7/NixY6Zpg4ODjT/++MOpPfv27TNy5sxpmicuLi4Dn8C/x1tvvWW6fwoUKGD88ssvDml/+ukno1ChQpbHUHR0tFP5VveskgybzWa8/PLLxrVr1+zp4+Pjjf/973+Wn1tgYKBx6tQphzqCg4Od0p0+fdqr+8nTe+/bBQUFOeUNCgoy1qxZ45Du6tWrxvvvv296TyDJGD9+vFPZp06dMgIDA03TBwUFGe+//74RHx9vT3/z5k1j3LhxRo4cOSw/G7NzPLPvVTyxePFiy7aXKlXK+PHHH42EhASHPMeOHXP5N1GjRo0s6ztz5ozpcSbJyJ07t/H+++8bly5dcshz+fJlY+LEiZb3mCEhIcbZs2cztB9ud++996b5fZTyGj16tGU5ycnJlsfH6tWrndKfOHHC8l499X23YVhf862uIe46evSoZbkvvPCCcfPmTXva48ePG61bt/bo+Le6x0p5NWjQwDhw4IDDPty+fbtRpUoVyzwTJ050qOOZZ55xSjNu3Lh07xMz6b1XTGF1Xzdw4ECH60xycrKxZs0ao3z58qbpa9So4XSOGoZhdOrUyXJ/tW/f3uE+Pzk52Vi3bp1RtmxZl9cDMy1atDBN36tXLyMmJsYhbXx8vDFlyhQjICDAKX1gYKBx8uRJt/admfj4eMt7M39/f+PVV191uk5cv37dmDFjhhEaGmqaz8/Pz9i3b59TXVb3aK+99prDbyeevFJ/fwMAAABIPwIakC08+cFz8+bNLn8Ysdlspp2knv6oevToUcsfhlNekZGRRrNmzVz+UJ3y40NSUpJTHVkZ0PDHH3+4bGNaP9BkRVutfiTxpL74+HijZMmSLstw9WNkemV2QMPWrVvTPO4rVKhgNGnSxLTzNPXr3nvvNW3PnDlzLPP4+fkZlSpVMpo3b26Eh4en+TmldvbsWcPf3980XZ48eYwHHnjAGDp0qPHCCy8Y7du3t0wbFRXl1OZHH33Usg158uQxatasabRp08aoWbOmZUeHJGPt2rVOZWd2QINhGEbHjh1d7sd8+fIZTZo0MSpUqGDYbDaXabdu3WpaR2RkZJqf1+3bdNddd7lMnzt3bqNBgwZG1apVXe7XlFdGAhoMwzCmT59uWXaOHDmMSpUqGa1atTIaNGhg5M6d2zLtW2+9leHPLIUn5/wrr7xi2aZcuXIZ1apVM1q3bm1ERUW57CybOXOmU9kZDWj49ttvnfKWLVvWtKPbm5YuXerymPH393f6cTyj0gpoSL39TZo0MfLnz+8yXaVKlUy/2z29dqTnu69Xr15p5kk5hhMTEy1/wA8KCjI6dOhgDB482HjxxReNBx54wPLeJywszCmg5r9q48aNaR5DLVq0MEqUKJHm5+RpQEPKKyAgwKhRo4YRFRVl2vGf+tWvXz+nOipXruyU7s033/RqwGdGAxqsOvUkGXfccYfRunVro27dukaePHncOrdvv65bdTKmvAIDA406deoYtWvXdnltTuscz8x7FU8kJyen2XkfGhpqNGrUyGjdurVRpUoVl/cefn5+Th3vt3v//fdd1pczZ06jbt26Rps2bYy6deum+bfXBx98kKF9YMZVoEfqV44cOZyCKG9Xs2ZN07z+/v5G69atjYEDBxovvfSS0b17d8v79YCAAOPChQsO5WZWQINhGMYdd9xhWXbBggWNxo0bG9WrV0/zHEhPQEPKq3jx4kazZs3S/BujYMGCxuXLlx3qGDdunFO6Bg0aGH///XeG9os72+FuQMPbb79tuU2FChUymjVrZjRr1syt7wyzz339+vVppi9durTRrFkzy/uB1C+rv9V/+eUXl9eD8uXLG3feeafRuHFjl/dQTz75ZIY/k3nz5rnchoCAAPu1tX79+ml+TwwaNMi0HquAhoy89uzZk+HtBwAAAEBAA7KJJz94JicnW3YkSTKaNm2a4TpSfPTRRxn+gzVnzpzG5s2bTcvPyoCGGzdupNkhmvqVHQEN3bp1S7Nd7nTSffDBB5b5Q0JCHEYz8JbMDmgwDMMYNmxYho/HsLAwy1Eubty4YRQtWtQrP9Tcrn379hku0+xH7GPHjhmFCxfOULmtWrUy3R9ZEdAQHR1thIWFZXjfDB061LJNjRo1SjP/7du0Z88elyNwePrKaEBDQkKCW52+rl6FChUyfbo8KwIaLl26ZFSqVClD7a9YsaLDU3wpMhrQcPLkSdOn59J6hYaGGnXq1DG6dOliLFiwwLRj35Xk5GSjVq1aluU/+uijHpXnDncDGtx52Ww2Y/ny5ab1eHrtSM933/Dhw9PMk/oYNnuC1dOX1Y/9/0XJyclGVFSUV46l9AY0uPsqXLiww8gjKZ577jmPy8qRI4dRtmxZo2XLlsaLL75oGkDszna4+50wePBgr+2H288Jw7j1hHxG7yHcOccz817FU0ePHjUKFizole196aWX0qwvMTHRaNiwoVfqa9SokcMIUd6SlJRkORJU6le3bt3SLMts1CNPX/fff79TuZkZ0OCNv3etjn93AxrcfX322WdOdaQV9G31Kly4sFG/fn2jW7duxsqVK10G7GU0oGHXrl1e3Q9mn7urY8TTl9Xf6snJyUaPHj0yVHbOnDkdRuXIiAcffNAr21u2bFmnQJkUBDQAAAAAvstxkkTAB9lsNj344IOW67t27eq1up5++mm9+OKL6c4fGBior7/+WlFRUV5rU3oFBQWpbdu22d0MlypVquSVcp544gkVLFjQdN1TTz2lAgUKeKWerDZmzBg98sgj6c6fN29eLViwQKVLlzZdHxQUpEmTJqW7fFfee+895c6dO935o6Ki1L9/f6flxYoV0/fff6+QkJB0lVuyZEnNnDkz3e3KqMjISP3444/Kly9fust48MEHNWbMGMv16Tmvqlatqs8++8xp7uTsEhAQoG+//VYVKlRIV/6goCDNnTvX8rqQ2fLkyaMFCxaocOHC6cqfP39+zZs3z3Se6YwqUqSIpk+f7nG+uLg4bdu2Td9//73uu+8+VatWTXv37nU7v81mc/n9OnToUI/blJXGjBmj1q1be6Ws9JyjnuYZNWqUihQp4nE9KUqVKqXXX3893fn/bWw2mz788MNMOSe9KWfOnJo9e7YiIiKc1r311luqXr26R+UlJCTor7/+0q+//qqxY8eqdOnSevnll2UYhrea7GDo0KHKnz+/x/ny5s3rVrqIiAh9++23ypUrl8d1eMKX7lWKFy+uX3/9VWFhYRkq55lnntHbb7+dZjp/f3/9/PPPql+/fobqa9iwoX7++Wf5+/tnqBwzfn5+6tevX5rpzO5Db/fcc8+pSpUq6W5Lvnz59P7776c7f3o88cQTqlu3bpbWmR79+vVT7969nZbXqVNHo0aN8ri82NhYbdq0SbNmzdKdd96pJk2a6NixY95oqpPq1aun6284d69lkvTxxx9n+udos9n0ySefqHHjxunOP3369HTfz99uxowZuvfeezNURoUKFfTbb7+l+/oMAAAAIPv4Rs8FkAaroAWbzaYuXbp4ta533nlHX375pYKDgz3KV6pUKa1Zs0adO3f2ansy4qOPPvLoh5Gs9uyzz2aowyXFzZs3FR8f77Q8ICBAAwcOzHD52cXPz09ff/213nnnHQUEBHiUt0aNGtq6dasaNWrkMl3nzp01YcIE2Ww2t8p1N0imQoUK+uWXXxQeHu5W+tRatmypJUuWWHYcNW7cWBs2bFC5cuU8KrdevXpav369V465jGjQoIG2bt2qmjVrepQvICBAo0eP1uzZs13+wD98+PB0ddY8/PDDWrhwoUfXjPbt26tdu3Ye1+WOsLAwbdy40ePyixQpouXLl6tFixaZ0i53VahQQVu2bPE4wK18+fJat26dKleunEktkypWrJjhYI99+/apbdu2unDhgtt5Ll++bLr87rvv9rijNb3Cw8M1YsQIt9PnyJFDU6dOzVCw4+3S89330EMPqVq1am6nDw0N1apVqzy+Tkq3vj9Wr16docCrf6MGDRpoxowZCgwMdCu9N4JK77zzTrcDzQoXLqyVK1fqzjvvNF0fGBiY4c6v+Ph4vfPOO3r33XczVI6VIkWK6KeffvIoqOGNN95Qt27d3E7frFkzrVq1yjTow0pUVJQee+wxt9NLvnWvUqNGDa1Zs0bNmzf3OG+ePHk0YcIETZo0ye1jMX/+/Fq2bJmeeOIJt+8vU9hsNj355JNaunRppl6DevfuraCgIMv11apVc6sTNygoSL/88ku6gtlLlSql1atXq1SpUh7nzYigoCDNnz9fVatWdSt9eHh4hr+jy5Ur5/Z22mw2vfbaa/r4448t09SpUyfDAWbr169X+/btlZCQkKFyrEydOlWtWrVyO32tWrU0d+5ct9Pnzp1bK1eu1H333edRnnHjxrmdXpJy5cqlX375Rb169fIoX968eTV37lx1797do3yuBAUF6bvvvtOwYcPc/i5OrXPnzlq1apWKFy/utTYBAAAAyDoENOAfoW7duoqMjHRa3rhxYxUtWtTr9T322GOKjo7W8OHD0yy/atWqmjx5sg4cOKB69ep5vS0ZUbZsWR06dEiDBw9W7dq1M/x0lrflz59fmzd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+ "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": { + "image/png": { + "height": 450, + "width": 1050 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "monthly_rows: list[dict[str, object]] = []\n", + "for scenario in SCENARIO_ORDER:\n", + " monthly = bills_by_scenario[scenario].filter(pl.col(\"month\").is_in(MONTH_ORDER))\n", + " for month in MONTH_ORDER:\n", + " mdf = monthly.filter(pl.col(\"month\") == month)\n", + " if mdf.is_empty():\n", + " continue\n", + " monthly_rows.append(\n", + " {\n", + " \"scenario\": scenario,\n", + " \"month\": month,\n", + " \"energy_bill\": weighted_mean(mdf, \"energy_total_bill\"),\n", + " \"elec_bill\": weighted_mean(mdf, \"elec_total_bill\"),\n", + " }\n", + " )\n", + "\n", + "monthly_df = pl.DataFrame(monthly_rows).with_columns(\n", + " pl.col(\"scenario\").cast(pl.Enum(SCENARIO_ORDER)),\n", + " pl.col(\"month\").cast(pl.Enum(MONTH_ORDER)),\n", + ")\n", + "\n", + "(\n", + " ggplot(monthly_df, aes(x=\"month\", y=\"energy_bill\", fill=\"scenario\"))\n", + " + geom_col(position=position_dodge(width=0.8), width=0.7)\n", + " + scale_fill_manual(values={\"Upgrade 00\": SB_COLORS[\"sky\"], \"Upgrade 02\": SB_COLORS[\"carrot\"]})\n", + " + scale_x_discrete(limits=MONTH_ORDER)\n", + " + scale_y_continuous(labels=lambda xs: [f\"${x:,.0f}\" for x in xs])\n", + " + labs(\n", + " x=\"Month\",\n", + " y=\"Weighted mean household energy bill\",\n", + " fill=\"Scenario\",\n", + " title=\"Monthly household energy bills \u2014 upgrade 00 vs. upgrade 02\",\n", + " )\n", + " + theme_switchbox()\n", + " + theme(figure_size=(10.5, 4.5), legend_position=\"top\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000028", + "metadata": {}, + "source": [ + "## Section 5: Bill change analysis (upgrade 00 \u2192 upgrade 02)\n", + "\n", + "This section compares each building's annual energy bill **before** (upgrade 00) and\n", + "**after** (upgrade 02) the heat pump retrofit.\n", + "\n", + "Bill change \u03b4 = `bill_after \u2212 bill_before`; negative \u03b4 means savings.\n", + "\n", + "Buildings are classified by their **upgrade 00** heating type throughout \u2014 i.e., by\n", + "the baseline fuel they would be replacing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000029", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bill delta computed for 33,790 buildings. 54.1% have negative delta (savings).\n", + "Heating groups: ['Fossil fuel', 'Electric resistance', 'Existing heat pump']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/ebs/tmp/ipykernel_13622/1938810118.py:6: DeprecationWarning: the `default` parameter for `replace` is deprecated. Use `replace_strict` instead to set a default while replacing values.\n", + "(Deprecated in version 1.0.0)\n" + ] + } + ], + "source": [ + "# \u2500\u2500 Quadrant definitions (from analysis.qmd) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", + "\n", + "QUADRANT_COLORS: dict[str, str] = {\n", + " \"savings > $1k\": \"#1b5e20\",\n", + " \"savings $0-1k\": \"#81c784\",\n", + " \"losses $0-1k\": \"#ef9a9a\",\n", + " \"losses > $1k\": \"#b71c1c\",\n", + "}\n", + "QUADRANT_ORDER = list(QUADRANT_COLORS.keys())\n", + "QUADRANT_LABELS: dict[str, str] = {\n", + " \"losses > $1k\": \"LOSE > $1K\",\n", + " \"losses $0-1k\": \"LOSE $0-1K\",\n", + " \"savings $0-1k\": \"SAVE $0-1K\",\n", + " \"savings > $1k\": \"SAVE > $1K\",\n", + "}\n", + "\n", + "\n", + "def quadrant_pcts(df: pl.DataFrame) -> dict[str, float]:\n", + " \"\"\"Weighted % of households in each bill-change quadrant (df must have 'delta' + 'weight').\"\"\"\n", + " total = cast(float, df[\"weight\"].sum())\n", + " return {\n", + " \"savings > $1k\": cast(float, df.filter(pl.col(\"delta\") < -1000)[\"weight\"].sum()) / total * 100,\n", + " \"savings $0-1k\": cast(\n", + " float,\n", + " df.filter((pl.col(\"delta\") >= -1000) & (pl.col(\"delta\") < 0))[\"weight\"].sum(),\n", + " )\n", + " / total\n", + " * 100,\n", + " \"losses $0-1k\": cast(\n", + " float,\n", + " df.filter((pl.col(\"delta\") >= 0) & (pl.col(\"delta\") < 1000))[\"weight\"].sum(),\n", + " )\n", + " / total\n", + " * 100,\n", + " \"losses > $1k\": cast(float, df.filter(pl.col(\"delta\") >= 1000)[\"weight\"].sum()) / total * 100,\n", + " }\n", + "\n", + "\n", + "# \u2500\u2500 Bill change dataframe \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", + "\n", + "bill_delta = (\n", + " add_heating_label(\n", + " bills_by_scenario[\"Upgrade 00\"]\n", + " .filter(pl.col(\"month\") == \"Annual\")\n", + " .select(\n", + " BLDG_ID,\n", + " UTILITY_COL,\n", + " \"weight\",\n", + " HEATING_TYPE_COL,\n", + " \"heats_with_natgas\",\n", + " \"heats_with_oil\",\n", + " \"heats_with_propane\",\n", + " pl.col(\"energy_total_bill\").alias(\"bill_before\"),\n", + " )\n", + " )\n", + " .join(\n", + " bills_by_scenario[\"Upgrade 02\"]\n", + " .filter(pl.col(\"month\") == \"Annual\")\n", + " .select(BLDG_ID, pl.col(\"energy_total_bill\").alias(\"bill_after\")),\n", + " on=BLDG_ID,\n", + " how=\"inner\",\n", + " validate=\"1:1\",\n", + " )\n", + " .with_columns((pl.col(\"bill_after\") - pl.col(\"bill_before\")).alias(\"delta\"))\n", + ")\n", + "\n", + "# Only analyze heating types that would actually be upgrading to a heat pump\n", + "# (exclude \"Existing heat pump\" - they already have heat pumps)\n", + "avail_heating = [\n", + " h for h in HEATING_ORDER if h in bill_delta[\"heating_label\"].unique().to_list() and h != \"Existing heat pump\"\n", + "]\n", + "n_save = bill_delta.filter(pl.col(\"delta\") < 0).height\n", + "pct_save = n_save / bill_delta.height * 100\n", + "print(f\"Bill delta computed for {bill_delta.height:,} buildings. {pct_save:.1f}% have negative delta (savings).\")\n", + "print(f\"Heating groups: {avail_heating}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000030", + "metadata": {}, + "source": [ + "### 5a: Quadrant bar chart by heating type\n", + "\n", + "Each bar shows the percentage of weighted households in four outcome bins:\n", + "\n", + "| Bin | Definition |\n", + "|-----|------------|\n", + "| SAVE > $1K | Annual energy bill decreases by more than $1,000 |\n", + "| SAVE $0-1K | Annual energy bill decreases by $0\u2013$1,000 |\n", + "| LOSE $0-1K | Annual energy bill increases by $0\u2013$1,000 |\n", + "| LOSE > $1K | Annual energy bill increases by more than $1,000 |\n", + "\n", + "A bar leaning heavily green indicates that most households in that group would save\n", + "money by switching to a heat pump under current rates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000031", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": { + "image/png": { + "height": 519, + "width": 1050 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "qb_records: list[dict[str, object]] = []\n", + "for group in avail_heating:\n", + " gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n", + " pct = quadrant_pcts(gdf)\n", + " for q in QUADRANT_ORDER:\n", + " qb_records.append({\"heating_label\": group, \"quadrant\": q, \"pct\": pct[q]})\n", + "\n", + "qb_plot = pl.DataFrame(qb_records).with_columns(\n", + " pl.col(\"heating_label\").cast(pl.Enum(list(reversed(avail_heating)))),\n", + " pl.col(\"quadrant\").cast(pl.Enum(QUADRANT_ORDER)),\n", + ")\n", + "\n", + "(\n", + " ggplot(qb_plot, aes(x=\"heating_label\", y=\"pct\", fill=\"quadrant\"))\n", + " + geom_col(position=\"stack\", width=0.55)\n", + " + geom_text(\n", + " mapping=aes(label=\"pct\"),\n", + " data=qb_plot.filter(pl.col(\"pct\") >= 3),\n", + " position=position_stack(vjust=0.5),\n", + " format_string=\"{:.1f}%\",\n", + " color=\"white\",\n", + " size=11,\n", + " fontweight=\"bold\",\n", + " )\n", + " + scale_fill_manual(values=QUADRANT_COLORS, breaks=QUADRANT_ORDER)\n", + " + scale_y_continuous(expand=(0, 0, 0.02, 0))\n", + " + coord_flip()\n", + " + guides(fill=False)\n", + " + labs(\n", + " x=\"\",\n", + " y=\"% of weighted households\",\n", + " title=\"Change in total annual energy bill after upgrading to heat pump (upgrade 00 \u2192 02)\",\n", + " )\n", + " + theme_switchbox()\n", + " + theme(figure_size=(10.5, max(3.5, 1.0 + 1.4 * len(avail_heating))))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000032", + "metadata": {}, + "source": [ + "### 5b: Savings breakdown table\n", + "\n", + "The table below shows the fraction of weighted households in each savings/loss bin,\n", + "broken out by baseline heating type, plus weighted mean and median bill changes.\n", + "The bin boundaries ($0, $1k, $2k) are the same as in the quadrant bars above, with\n", + "an additional `Save > $2k/yr` bin to capture large savings from oil/propane retrofits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000033", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 10)
Baseline heatingBuildingsWeighted hholdsSave > $2k/yrSave $1k-$2k/yrSave $0-$1k/yrLose $0-$1k/yrLose > $1k/yrMean change ($/yr)Median change ($/yr)
stri64i64strstrstrstrstrstrstr
"Fossil fuel"295546349536"5.5%""8.4%""34.2%""43.0%""8.9%""$-113""$0"
"Electric resistance"3070654504"20.3%""24.7%""52.2%""2.9%""0.0%""$-1,311""$-879"
"Existing heat pump"918198368"10.3%""18.0%""70.9%""0.8%""0.0%""$-817""$-428"
" + ], + "text/plain": [ + "shape: (3, 10)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 Baseline \u2506 Buildings \u2506 Weighted \u2506 Save > \u2506 \u2026 \u2506 Lose \u2506 Lose > \u2506 Mean \u2506 Median \u2502\n", + "\u2502 heating \u2506 --- \u2506 hholds \u2506 $2k/yr \u2506 \u2506 $0-$1k/yr \u2506 $1k/yr \u2506 change \u2506 change \u2502\n", + "\u2502 --- \u2506 i64 \u2506 --- \u2506 --- \u2506 \u2506 --- \u2506 --- \u2506 ($/yr) \u2506 ($/yr) \u2502\n", + "\u2502 str \u2506 \u2506 i64 \u2506 str \u2506 \u2506 str \u2506 str \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 str \u2506 str \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 Fossil \u2506 29554 \u2506 6349536 \u2506 5.5% \u2506 \u2026 \u2506 43.0% \u2506 8.9% \u2506 $-113 \u2506 $0 \u2502\n", + "\u2502 fuel \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Electric \u2506 3070 \u2506 654504 \u2506 20.3% \u2506 \u2026 \u2506 2.9% \u2506 0.0% \u2506 $-1,311 \u2506 $-879 \u2502\n", + "\u2502 resistanc \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 e \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 Existing \u2506 918 \u2506 198368 \u2506 10.3% \u2506 \u2026 \u2506 0.8% \u2506 0.0% \u2506 $-817 \u2506 $-428 \u2502\n", + "\u2502 heat pump \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SAVE_BINS: list[tuple[float, float, str]] = [\n", + " (-float(\"inf\"), -2000, \"Save > $2k/yr\"),\n", + " (-2000, -1000, \"Save $1k-$2k/yr\"),\n", + " (-1000, 0, \"Save $0-$1k/yr\"),\n", + " (0, 1000, \"Lose $0-$1k/yr\"),\n", + " (1000, float(\"inf\"), \"Lose > $1k/yr\"),\n", + "]\n", + "\n", + "savings_rows: list[dict[str, object]] = []\n", + "for group in avail_heating:\n", + " gdf = bill_delta.filter(pl.col(\"heating_label\") == group)\n", + " total_w = cast(float, gdf[\"weight\"].sum())\n", + " row: dict[str, object] = {\n", + " \"Baseline heating\": group,\n", + " \"Buildings\": gdf.height,\n", + " \"Weighted hholds\": round(total_w),\n", + " }\n", + " for lo, hi, label in SAVE_BINS:\n", + " if lo == -float(\"inf\"):\n", + " filt = gdf.filter(pl.col(\"delta\") < hi)\n", + " elif hi == float(\"inf\"):\n", + " filt = gdf.filter(pl.col(\"delta\") >= lo)\n", + " else:\n", + " filt = gdf.filter((pl.col(\"delta\") >= lo) & (pl.col(\"delta\") < hi))\n", + " row[label] = f\"{cast(float, filt['weight'].sum()) / total_w * 100:.1f}%\"\n", + " row[\"Mean change ($/yr)\"] = f\"${weighted_mean(gdf, 'delta'):,.0f}\"\n", + " row[\"Median change ($/yr)\"] = f\"${weighted_quantile(gdf, 'delta', 0.5):,.0f}\"\n", + " savings_rows.append(row)\n", + "\n", + "savings_df = pl.DataFrame(savings_rows)\n", + "print(\"Bill change after upgrade 02, by baseline heating type (% of weighted households in each bin):\")\n", + "display(savings_df)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000034", + "metadata": {}, + "source": [ + "### 5c: Bill change histogram by heating type\n", + "\n", + "Distribution of annual bill changes, trimmed to the 1st-99th percentile, faceted by\n", + "baseline heating type. Green bars are savings; orange bars are increases. A well-behaved\n", + "batch produces a smooth, roughly unimodal distribution centered left of zero for\n", + "fossil-fuel homes and a broader, more symmetric distribution for electric resistance\n", + "homes (which lose the gas bill but gain less electric savings)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000035", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": { + "image/png": { + "height": 750, + "width": 1050 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "BIN_WIDTH = 100\n", + "_lo = math.floor(weighted_quantile(bill_delta, \"delta\", 0.01) / BIN_WIDTH) * BIN_WIDTH\n", + "_hi = math.ceil(weighted_quantile(bill_delta, \"delta\", 0.99) / BIN_WIDTH) * BIN_WIDTH\n", + "\n", + "hist_data = (\n", + " bill_delta.filter(pl.col(\"heating_label\").is_in(avail_heating))\n", + " .with_columns(\n", + " ((pl.col(\"delta\") / BIN_WIDTH).floor() * BIN_WIDTH + BIN_WIDTH / 2).alias(\"bin_center\"),\n", + " pl.when(pl.col(\"delta\") < 0).then(pl.lit(\"Savings\")).otherwise(pl.lit(\"Increase\")).alias(\"direction\"),\n", + " )\n", + " .filter(pl.col(\"bin_center\").is_between(_lo, _hi))\n", + " .group_by(\"heating_label\", \"bin_center\", \"direction\")\n", + " .agg(pl.col(\"weight\").sum().alias(\"weighted_households\"))\n", + " .with_columns(\n", + " pl.col(\"heating_label\").cast(pl.Enum(avail_heating)),\n", + " pl.col(\"direction\").cast(pl.Enum([\"Savings\", \"Increase\"])),\n", + " )\n", + ")\n", + "\n", + "(\n", + " ggplot(hist_data, aes(x=\"bin_center\", y=\"weighted_households\", fill=\"direction\"))\n", + " + geom_col(width=BIN_WIDTH * 0.9)\n", + " + facet_wrap(\"heating_label\", ncol=1, scales=\"free_y\")\n", + " + scale_fill_manual(values={\"Savings\": SB_COLORS[\"sky\"], \"Increase\": SB_COLORS[\"carrot\"]})\n", + " + scale_y_continuous(labels=lambda xs: [f\"{x:,.0f}\" for x in xs])\n", + " + labs(\n", + " x=\"Annual energy bill change ($/year)\",\n", + " y=\"Weighted households\",\n", + " fill=\"Outcome\",\n", + " title=\"Distribution of annual bill changes after upgrade 02 (1st-99th percentile)\",\n", + " )\n", + " + theme_switchbox()\n", + " + theme(\n", + " figure_size=(10.5, max(4.5, 2.5 * len(avail_heating))),\n", + " legend_position=\"top\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a0000036", + "metadata": {}, + "source": [ + "### 5d: Utility-level summary\n", + "\n", + "Per-utility summary of bill-change and BAT results. Helps identify whether one\n", + "utility's results are driving state-level patterns, and flags utilities with\n", + "implausible outliers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000037", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Per-utility summary (upgrade 00 BAT + upgrade 00\u219202 bill change):\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (7, 8)
utilitybuildingsweighted_householdsmean_bill_changemedian_bill_changepct_savingmean_bat_total_u0pct_overpaying_u0
stri64i64f64f64f64f64f64
"cenhud"1182270859-721.0-471.078.20.039.1
"coned"154353064038-156.0-69.059.1-0.033.2
"nimo"67911532294-44.0150.036.0-0.036.9
"nyseg"3682792300-242.00.047.60.035.7
"or"829211011-352.0-124.063.80.041.7
"psegli"44271029955-720.0-332.070.8-0.042.9
"rge"144435148129.0236.026.4-0.032.8
" + ], + "text/plain": [ + "shape: (7, 8)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 utility \u2506 buildings \u2506 weighted_h \u2506 mean_bill_ \u2506 median_bil \u2506 pct_saving \u2506 mean_bat_t \u2506 pct_overp \u2502\n", + "\u2502 --- \u2506 --- \u2506 ouseholds \u2506 change \u2506 l_change \u2506 --- \u2506 otal_u0 \u2506 aying_u0 \u2502\n", + "\u2502 str \u2506 i64 \u2506 --- \u2506 --- \u2506 --- \u2506 f64 \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 \u2506 i64 \u2506 f64 \u2506 f64 \u2506 \u2506 f64 \u2506 f64 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 cenhud \u2506 1182 \u2506 270859 \u2506 -721.0 \u2506 -471.0 \u2506 78.2 \u2506 0.0 \u2506 39.1 \u2502\n", + "\u2502 coned \u2506 15435 \u2506 3064038 \u2506 -156.0 \u2506 -69.0 \u2506 59.1 \u2506 -0.0 \u2506 33.2 \u2502\n", + "\u2502 nimo \u2506 6791 \u2506 1532294 \u2506 -44.0 \u2506 150.0 \u2506 36.0 \u2506 -0.0 \u2506 36.9 \u2502\n", + "\u2502 nyseg \u2506 3682 \u2506 792300 \u2506 -242.0 \u2506 0.0 \u2506 47.6 \u2506 0.0 \u2506 35.7 \u2502\n", + "\u2502 or \u2506 829 \u2506 211011 \u2506 -352.0 \u2506 -124.0 \u2506 63.8 \u2506 0.0 \u2506 41.7 \u2502\n", + "\u2502 psegli \u2506 4427 \u2506 1029955 \u2506 -720.0 \u2506 -332.0 \u2506 70.8 \u2506 -0.0 \u2506 42.9 \u2502\n", + "\u2502 rge \u2506 1444 \u2506 351481 \u2506 29.0 \u2506 236.0 \u2506 26.4 \u2506 -0.0 \u2506 32.8 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util_bat_u0 = bat_by_scenario[\"Upgrade 00\"]\n", + "util_rows: list[dict[str, object]] = []\n", + "\n", + "for utility in sorted(annual_u0[UTILITY_COL].drop_nulls().unique().to_list()):\n", + " delta_u = bill_delta.filter(pl.col(UTILITY_COL) == utility)\n", + " bat_u = util_bat_u0.filter(pl.col(UTILITY_COL) == utility)\n", + " if delta_u.is_empty() or bat_u.is_empty():\n", + " continue\n", + " util_rows.append(\n", + " {\n", + " \"utility\": utility,\n", + " \"buildings\": delta_u.height,\n", + " \"weighted_households\": round(cast(float, delta_u[\"weight\"].sum())),\n", + " \"mean_bill_change\": round(weighted_mean(delta_u, \"delta\"), 0),\n", + " \"median_bill_change\": round(weighted_quantile(delta_u, \"delta\", 0.5), 0),\n", + " \"pct_saving\": round(\n", + " cast(float, delta_u.filter(pl.col(\"delta\") < 0)[\"weight\"].sum())\n", + " / cast(float, delta_u[\"weight\"].sum())\n", + " * 100,\n", + " 1,\n", + " ),\n", + " \"mean_bat_total_u0\": round(weighted_mean(bat_u, \"BAT_percustomer_total\"), 0),\n", + " \"pct_overpaying_u0\": round(\n", + " cast(\n", + " float,\n", + " bat_u.filter(pl.col(\"BAT_percustomer_total\") > 0)[\"weight\"].sum(),\n", + " )\n", + " / cast(float, bat_u[\"weight\"].sum())\n", + " * 100,\n", + " 1,\n", + " ),\n", + " }\n", + " )\n", + "\n", + "util_df = pl.DataFrame(util_rows)\n", + "print(\"Per-utility summary (upgrade 00 BAT + upgrade 00\u219202 bill change):\")\n", + "display(util_df)" + ] + }, + { + "cell_type": "markdown", + "id": "a0000038", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Interpretation checklist\n", + "\n", + "Before using these results in a report or policy analysis:\n", + "\n", + "1. **Structural checks passed** \u2014 All assertions in Section 3 must pass cleanly. A\n", + " failure indicates a specific post-processing step to investigate.\n", + "\n", + "2. **Utility coverage looks right** \u2014 Confirm that weighted household totals per\n", + " utility (Section 2a) are stable across scenarios and plausible for the service\n", + " territory.\n", + "\n", + "3. **Heating-type breakdown is plausible** \u2014 The upgrade 00 heating-type composition\n", + " (Section 2b) should reflect the state's known building stock. A suspiciously low\n", + " fossil-fuel share or high heat-pump share may indicate a ResStock metadata issue.\n", + "\n", + "4. **Gas null fraction makes sense** \u2014 High gas-null share is expected in all-electric\n", + " territories; investigate if it is unexpectedly low in a gas-dense market.\n", + "\n", + "5. **BAT signs are correct** \u2014 Under flat default rates, fossil-fuel customers should\n", + " show negative BAT (they underpay); HP/ER customers should show positive BAT\n", + " (they overpay). If this pattern is reversed, check the residual allocation\n", + " configuration in the CAIRO scenario YAML.\n", + "\n", + "6. **Cross-subsidy magnitude is reasonable** \u2014 Compare against prior state runs at\n", + " similar utility scales to identify implausible outliers.\n", + "\n", + "7. **Bill change signs match fuel-cost logic** \u2014 Fossil-fuel homes should generally\n", + " save in warmer states (low heating loads) and see more mixed results in cold\n", + " states with high gas demand. Oil/propane homes typically show larger savings.\n", + "\n", + "8. **To review another state or batch** \u2014 Change `STATE` and `BATCH` in the\n", + " Parameters cell, restart the kernel, and run all cells." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}