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SkittyWittySkittyWitty
committedFeb 21, 2025·
Merge branch 'main' into tsg/update_health_api_demo
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‎notebooks/All About REM Statistics.ipynb

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@@ -12,7 +12,7 @@
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"[Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method)\n",
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"simulation over a number of theoretical homes that closely\n",
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"resemble the target home and, taken as a whole, \n",
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"probabalistically represent it. \n",
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"probabilistically represent it. \n",
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"\n",
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"We do this because we generally don't know everything about each\n",
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"home. We have a large database of home properties\n",
@@ -22,7 +22,7 @@
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"[conditional probability distribution](https://github.com/NREL/resstock/tree/develop/project_national/housing_characteristics) based on the properties\n",
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"that we do know. We then predict the energy consumption for each \n",
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"sample home using a machine learning model, which yields\n",
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"a distribution of outcomes that probablistically represents the\n",
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"a distribution of outcomes that probabilistically represents the\n",
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"query home's energy consumption under the baseline and upgrade scenarios.\n",
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"We compute statistics across the this distribution to decide\n",
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"what is likely to happen in the query home.\n",
@@ -41,6 +41,10 @@
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"work, describe how they are computed, and discuss how and why\n",
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"they should or should not be used in particular ways.\n",
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"\n",
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"To learn more about modeling only baseline\n",
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"view the [REM Core Demo notebook](https://github.com/rewiringamerica/api_demos/tree/main/notebooks/REM%20Demo).\n",
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"\n",
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"\n",
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"## Imports and Configuration"
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]
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},
@@ -100,9 +104,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"address = '165 Hope St, Providence, RI 02906'\n",
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"upgrade = \"med_eff_hp_hers_sizing_no_setback\"\n",
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"heating_fuel = 'fuel_oil'"
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"address = \"165 Hope St, Providence, RI 02906\"\n",
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"upgrade = \"hvac__heat_pump_seer18_hspf10\"\n",
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"heating_fuel = \"fuel_oil\""
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]
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},
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{
@@ -125,9 +129,7 @@
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"response = requests.get(\n",
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" url=REM_ADDRESS_URL,\n",
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" headers=headers,\n",
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" params=dict(\n",
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" address=address, upgrade=upgrade, heating_fuel=heating_fuel\n",
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" )\n",
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" params=dict(address=address, upgrade=upgrade, heating_fuel=heating_fuel),\n",
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")"
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]
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},
@@ -191,18 +193,18 @@
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{
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"data": {
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"text/plain": [
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"{'energy': {'mean': {'value': 1896.4884, 'units': 'gallon'},\n",
195-
" 'median': {'value': 1835.3018, 'units': 'gallon'},\n",
196-
" 'percentile_20': {'value': 1421.099, 'units': 'gallon'},\n",
197-
" 'percentile_80': {'value': 2339.4446, 'units': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': 23339.9604, 'units': 'kgCO2e'},\n",
199-
" 'median': {'value': 22586.9408, 'units': 'kgCO2e'},\n",
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" 'percentile_20': {'value': 17489.3729, 'units': 'kgCO2e'},\n",
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" 'percentile_80': {'value': 28791.3938, 'units': 'kgCO2e'}},\n",
202-
" 'cost': {'mean': {'value': 7583.0636, 'units': '$'},\n",
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" 'median': {'value': 7338.4104, 'units': '$'},\n",
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" 'percentile_20': {'value': 5682.2301, 'units': '$'},\n",
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" 'percentile_80': {'value': 9354.2134, 'units': '$'}}}"
196+
"{'energy': {'mean': {'value': 1639.9717, 'unit': 'gallon'},\n",
197+
" 'median': {'value': 1592.8951, 'unit': 'gallon'},\n",
198+
" 'percentile_20': {'value': 1217.5114, 'unit': 'gallon'},\n",
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" 'percentile_80': {'value': 2014.3037, 'unit': 'gallon'}},\n",
200+
" 'emissions': {'mean': {'value': 20183.0257, 'unit': 'kgCO2e'},\n",
201+
" 'median': {'value': 19603.6575, 'unit': 'kgCO2e'},\n",
202+
" 'percentile_20': {'value': 14983.8342, 'unit': 'kgCO2e'},\n",
203+
" 'percentile_80': {'value': 24789.9057, 'unit': 'kgCO2e'}},\n",
204+
" 'cost': {'mean': {'value': 6557.3876, 'unit': '$'},\n",
205+
" 'median': {'value': 6369.1531, 'unit': '$'},\n",
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" 'percentile_20': {'value': 4868.1902, 'unit': '$'},\n",
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" 'percentile_80': {'value': 8054.1452, 'unit': '$'}}}"
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]
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},
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"execution_count": 9,
@@ -211,7 +213,7 @@
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}
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],
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"source": [
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"fuel_results['fuel_oil']['baseline']"
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"fuel_results[\"fuel_oil\"][\"baseline\"]"
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]
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},
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{
@@ -223,10 +225,10 @@
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{
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"data": {
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"text/plain": [
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"{'electricity': {'value': 0.1241, 'units': 'kgCO2e/kWh'},\n",
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" 'natural_gas': {'value': 6.6798, 'units': 'kgCO2e/therm'},\n",
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" 'fuel_oil': {'value': 12.3069, 'units': 'kgCO2e/gallon'},\n",
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" 'propane': {'value': 7.3776, 'units': 'kgCO2e/gallon'}}"
228+
"{'electricity': {'value': 0.1241, 'unit': 'kgCO2e/kWh'},\n",
229+
" 'natural_gas': {'value': 6.6798, 'unit': 'kgCO2e/therm'},\n",
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" 'fuel_oil': {'value': 12.3069, 'unit': 'kgCO2e/gallon'},\n",
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" 'propane': {'value': 7.3776, 'unit': 'kgCO2e/gallon'}}"
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]
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},
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"execution_count": 10,
@@ -235,7 +237,7 @@
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}
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],
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"source": [
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"data['emissions_factors']"
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"data[\"emissions_factors\"]"
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]
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},
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{
@@ -245,8 +247,8 @@
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"source": [
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"## Let's look at fuel oil, since that's what we are replacing\n",
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"\n",
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"It's a fairly big block of nested dictionaries, but we will go through it peice by\n",
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"peice."
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"It's a fairly big block of nested dictionaries, but we will go through it piece by\n",
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"piece."
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]
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},
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{
@@ -268,42 +270,42 @@
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{
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"data": {
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"text/plain": [
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"{'baseline': {'energy': {'mean': {'value': 1896.4884, 'units': 'gallon'},\n",
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" 'median': {'value': 1835.3018, 'units': 'gallon'},\n",
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" 'percentile_20': {'value': 1421.099, 'units': 'gallon'},\n",
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" 'percentile_80': {'value': 2339.4446, 'units': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': 23339.9604, 'units': 'kgCO2e'},\n",
276-
" 'median': {'value': 22586.9408, 'units': 'kgCO2e'},\n",
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" 'percentile_20': {'value': 17489.3729, 'units': 'kgCO2e'},\n",
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" 'percentile_80': {'value': 28791.3938, 'units': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': 7583.0636, 'units': '$'},\n",
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" 'median': {'value': 7338.4104, 'units': '$'},\n",
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" 'percentile_20': {'value': 5682.2301, 'units': '$'},\n",
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" 'percentile_80': {'value': 9354.2134, 'units': '$'}}},\n",
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" 'upgrade': {'energy': {'mean': {'value': 66.8291, 'units': 'gallon'},\n",
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" 'median': {'value': 0.0, 'units': 'gallon'},\n",
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" 'percentile_20': {'value': 0.0, 'units': 'gallon'},\n",
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" 'percentile_80': {'value': 127.0023, 'units': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': 822.4611, 'units': 'kgCO2e'},\n",
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" 'median': {'value': 0.0, 'units': 'kgCO2e'},\n",
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" 'percentile_20': {'value': 0.0, 'units': 'kgCO2e'},\n",
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" 'percentile_80': {'value': 1563.0089, 'units': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': 267.2145, 'units': '$'},\n",
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" 'median': {'value': 0.0, 'units': '$'},\n",
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" 'percentile_20': {'value': 0.0, 'units': '$'},\n",
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" 'percentile_80': {'value': 507.8156, 'units': '$'}}},\n",
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" 'delta': {'energy': {'mean': {'value': -1829.6594, 'units': 'gallon'},\n",
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" 'median': {'value': -1734.66, 'units': 'gallon'},\n",
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" 'percentile_20': {'value': -2254.9817, 'units': 'gallon'},\n",
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" 'percentile_80': {'value': -1351.139, 'units': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': -22517.4993, 'units': 'kgCO2e'},\n",
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" 'median': {'value': -21348.3478, 'units': 'kgCO2e'},\n",
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" 'percentile_20': {'value': -27751.9135, 'units': 'kgCO2e'},\n",
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" 'percentile_80': {'value': -16628.3804, 'units': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': -7315.8491, 'units': '$'},\n",
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" 'median': {'value': -6935.9963, 'units': '$'},\n",
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" 'percentile_20': {'value': -9016.4902, 'units': '$'},\n",
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" 'percentile_80': {'value': -5402.497, 'units': '$'}}}}"
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"{'baseline': {'energy': {'mean': {'value': 1639.9717, 'unit': 'gallon'},\n",
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" 'median': {'value': 1592.8951, 'unit': 'gallon'},\n",
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" 'percentile_20': {'value': 1217.5114, 'unit': 'gallon'},\n",
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" 'percentile_80': {'value': 2014.3037, 'unit': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': 20183.0257, 'unit': 'kgCO2e'},\n",
278+
" 'median': {'value': 19603.6575, 'unit': 'kgCO2e'},\n",
279+
" 'percentile_20': {'value': 14983.8342, 'unit': 'kgCO2e'},\n",
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" 'percentile_80': {'value': 24789.9057, 'unit': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': 6557.3876, 'unit': '$'},\n",
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" 'median': {'value': 6369.1531, 'unit': '$'},\n",
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" 'percentile_20': {'value': 4868.1902, 'unit': '$'},\n",
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" 'percentile_80': {'value': 8054.1452, 'unit': '$'}}},\n",
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" 'upgrade': {'energy': {'mean': {'value': 73.1678, 'unit': 'gallon'},\n",
286+
" 'median': {'value': 0.0, 'unit': 'gallon'},\n",
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" 'percentile_20': {'value': 0.0, 'unit': 'gallon'},\n",
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" 'percentile_80': {'value': 161.4628, 'unit': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': 900.471, 'unit': 'kgCO2e'},\n",
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" 'median': {'value': 0.0, 'unit': 'kgCO2e'},\n",
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" 'percentile_20': {'value': 0.0, 'unit': 'kgCO2e'},\n",
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" 'percentile_80': {'value': 1987.1118, 'unit': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': 292.5596, 'unit': '$'},\n",
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" 'median': {'value': 0.0, 'unit': '$'},\n",
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" 'percentile_20': {'value': 0.0, 'unit': '$'},\n",
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" 'percentile_80': {'value': 645.605, 'unit': '$'}}},\n",
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" 'delta': {'energy': {'mean': {'value': -1566.8039, 'unit': 'gallon'},\n",
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" 'median': {'value': -1521.6893, 'unit': 'gallon'},\n",
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" 'percentile_20': {'value': -1945.3572, 'unit': 'gallon'},\n",
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" 'percentile_80': {'value': -1122.8551, 'unit': 'gallon'}},\n",
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" 'emissions': {'mean': {'value': -19282.5547, 'unit': 'kgCO2e'},\n",
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" 'median': {'value': -18727.3322, 'unit': 'kgCO2e'},\n",
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" 'percentile_20': {'value': -23941.3851, 'unit': 'kgCO2e'},\n",
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" 'percentile_80': {'value': -13818.9057, 'unit': 'kgCO2e'}},\n",
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" 'cost': {'mean': {'value': -6264.828, 'unit': '$'},\n",
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" 'median': {'value': -6084.4384, 'unit': '$'},\n",
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" 'percentile_20': {'value': -7778.4641, 'unit': '$'},\n",
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" 'percentile_80': {'value': -4489.7094, 'unit': '$'}}}}"
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]
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},
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"execution_count": 12,
@@ -366,10 +368,7 @@
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"source": [
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"def results_for_stat(results, metric, stat: str):\n",
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" \"\"\"A helper function to pull out subsets of the results.\"\"\"\n",
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" return {\n",
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" k: results[k][metric][stat]\n",
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" for k in result_keys\n",
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" }"
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" return {k: results[k][metric][stat] for k in result_keys}"
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]
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},
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{
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{
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"data": {
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"text/plain": [
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"{'baseline': {'value': 1896.4884, 'units': 'gallon'},\n",
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" 'upgrade': {'value': 66.8291, 'units': 'gallon'},\n",
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" 'delta': {'value': -1829.6594, 'units': 'gallon'}}"
394+
"{'baseline': {'value': 1639.9717, 'unit': 'gallon'},\n",
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" 'upgrade': {'value': 73.1678, 'unit': 'gallon'},\n",
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" 'delta': {'value': -1566.8039, 'unit': 'gallon'}}"
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]
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},
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"execution_count": 15,
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}
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],
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"source": [
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"round(mean_energy['upgrade']['value'] - (mean_energy['baseline']['value'] + mean_energy['delta']['value']), 2)"
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"round(\n",
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" mean_energy[\"upgrade\"][\"value\"]\n",
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" - (mean_energy[\"baseline\"][\"value\"] + mean_energy[\"delta\"][\"value\"]),\n",
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" 2,\n",
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")"
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]
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},
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{
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{
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"data": {
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"text/plain": [
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"{'baseline': {'value': 1835.3018, 'units': 'gallon'},\n",
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" 'upgrade': {'value': 0.0, 'units': 'gallon'},\n",
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" 'delta': {'value': -1734.66, 'units': 'gallon'}}"
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"{'baseline': {'value': 1592.8951, 'unit': 'gallon'},\n",
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" 'upgrade': {'value': 0.0, 'unit': 'gallon'},\n",
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" 'delta': {'value': -1521.6893, 'unit': 'gallon'}}"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'value': 0.0, 'units': 'gallon'}"
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"{'value': 0.0, 'unit': 'gallon'}"
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]
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},
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"execution_count": 18,
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{
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"data": {
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"text/plain": [
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"{'value': 127.0023, 'units': 'gallon'}"
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"{'value': 161.4628, 'unit': 'gallon'}"
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]
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},
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"execution_count": 19,
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{
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"data": {
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"text/plain": [
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"{'baseline': {'value': 24494.8882, 'units': 'kgCO2e'},\n",
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" 'upgrade': {'value': 4827.1306, 'units': 'kgCO2e'},\n",
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" 'delta': {'value': -19006.6082, 'units': 'kgCO2e'}}"
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"{'baseline': {'value': 21423.6426, 'unit': 'kgCO2e'},\n",
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" 'upgrade': {'value': 4743.954, 'unit': 'kgCO2e'},\n",
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" 'delta': {'value': -16628.4887, 'unit': 'kgCO2e'}}"
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]
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},
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"execution_count": 22,
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{
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"data": {
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"text/plain": [
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"-661.15"
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"-51.2"
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]
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},
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"execution_count": 23,
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}
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],
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"source": [
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"round(median_emissions['upgrade']['value'] - (median_emissions['baseline']['value'] + median_emissions['delta']['value']), 2)"
623+
"round(\n",
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" median_emissions[\"upgrade\"][\"value\"]\n",
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" - (median_emissions[\"baseline\"][\"value\"] + median_emissions[\"delta\"][\"value\"]),\n",
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" 2,\n",
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")"
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]
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},
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{
@@ -643,7 +650,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},

‎notebooks/REM Demo.ipynb

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@@ -22,7 +22,8 @@
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"outputs": [],
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"source": [
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"import requests\n",
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"from pathlib import Path"
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"from pathlib import Path\n",
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"import pandas as pd"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"address = '8009 Belmont Ave., Lubbock, TX 79424'\n",
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"upgrade = \"high_eff_hp_elec_backup\"\n",
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"heating_fuel = 'natural_gas'"
74+
"address = \"8009 Belmont Ave., Lubbock, TX 79424\"\n",
75+
"upgrade = \"hvac__heat_pump_seer24_hspf13\"\n",
76+
"heating_fuel = \"natural_gas\""
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]
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},
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{
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"response = requests.get(\n",
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" url=REM_ADDRESS_URL,\n",
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" headers=headers,\n",
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" params=dict(\n",
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" address=address, upgrade=upgrade, heating_fuel=heating_fuel\n",
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" )\n",
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" params=dict(address=address, upgrade=upgrade, heating_fuel=heating_fuel),\n",
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")"
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]
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},
@@ -125,12 +124,14 @@
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"cell_type": "code",
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"execution_count": 7,
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"id": "6aab1b8e-c3d9-4d0f-a7d4-1a86f1543def",
128-
"metadata": {},
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Expected annual savings: $630.39'"
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"'Expected annual savings: $659.07'"
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]
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},
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"execution_count": 7,
@@ -139,23 +140,122 @@
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}
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],
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"source": [
142-
"annual_savings = -data['fuel_results']['total']['delta']['cost']['mean']['value']\n",
143+
"annual_savings = -data[\"fuel_results\"][\"total\"][\"delta\"][\"cost\"][\"mean\"][\"value\"]\n",
143144
"\n",
144145
"f\"Expected annual savings: ${annual_savings:.2f}\""
145146
]
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},
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{
149+
"cell_type": "markdown",
150+
"id": "5f962705",
151+
"metadata": {},
152+
"source": [
153+
"## Modeling Only Baseline in REM\n",
154+
"\n",
155+
"REM typically provides three estimates for a home: one without an upgrade, one with the requested upgrade, and the difference (delta) between them.\n",
156+
"For a detailed explanation of these statistics, refer to the [All About REM Statistics notebook](https://github.com/rewiringamerica/api_demos/blob/main/notebooks/All%20About%20REM%20Statistics.ipynb).\n",
157+
"\n",
158+
"A special case occurs when `baseline` is requested as the upgrade. In this case, REM returns a subset of the expected data structure.\n",
159+
"For each `fuel_type` within `fuel_results`, the `upgrade` and `delta` fields will not be populated. \n",
160+
"Requesting `baseline` tells REM to perform estimation without applying any upgrades.\n",
161+
"\n",
162+
"Performing a request with only `baseline` can be done similarly to other requests."
163+
]
164+
},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d697ac84",
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"metadata": {},
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"outputs": [],
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"source": [
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"headers = {\"Authorization\": f\"Bearer {API_KEY}\"}\n",
173+
"\n",
174+
"response = requests.get(\n",
175+
" url=REM_ADDRESS_URL,\n",
176+
" headers=headers,\n",
177+
" params=dict(address=address, upgrade=\"baseline\", heating_fuel=heating_fuel),\n",
178+
")"
179+
]
180+
},
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{
182+
"cell_type": "markdown",
183+
"id": "31758a83",
184+
"metadata": {},
185+
"source": [
186+
"### Pull out the results for a baseline request\n",
187+
"\n",
188+
"We will extract the `fuel_results`, as we have done previously. In the response, `baseline` contains valid values, while `upgrade` and `delta` are null, as expected since no upgrade was applied to the home."
189+
]
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},
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{
192+
"cell_type": "code",
193+
"execution_count": null,
194+
"id": "1831a4bd",
195+
"metadata": {},
196+
"outputs": [],
197+
"source": [
198+
"data = response.json()\n",
199+
"fuel_results = data[\"fuel_results\"]"
200+
]
201+
},
202+
{
203+
"cell_type": "code",
204+
"execution_count": null,
205+
"id": "cf88eec5",
206+
"metadata": {},
207+
"outputs": [],
208+
"source": [
209+
"# Convert the baseline from JSON to a pandas data frame for further\n",
210+
"# analysis.\n",
211+
"baseline = fuel_results[heating_fuel][\"baseline\"]\n",
212+
"\n",
213+
"\n",
214+
"def stats_df(baseline):\n",
215+
" return pd.DataFrame(\n",
216+
" [\n",
217+
" {\"metric\": metric, \"stat\": stat} | value\n",
218+
" for metric, stats in baseline.items()\n",
219+
" for stat, value in stats.items()\n",
220+
" ]\n",
221+
" )\n",
222+
"\n",
223+
"\n",
224+
"stats_df(baseline)"
225+
]
226+
},
227+
{
228+
"cell_type": "code",
229+
"execution_count": null,
230+
"id": "5e302eba",
231+
"metadata": {},
232+
"outputs": [],
233+
"source": [
234+
"fuel_results[heating_fuel][\"upgrade\"]"
235+
]
236+
},
237+
{
238+
"cell_type": "code",
239+
"execution_count": null,
240+
"id": "f32a8ed4",
241+
"metadata": {},
242+
"outputs": [],
243+
"source": [
244+
"fuel_results[heating_fuel][\"delta\"]"
245+
]
246+
},
247+
{
248+
"cell_type": "code",
249+
"execution_count": null,
250+
"id": "e39346ac-4825-4f21-a1e8-74c004629005",
251+
"metadata": {},
252+
"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
158-
"display_name": ".venv",
258+
"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},

‎react/README.md

+11
Original file line numberDiff line numberDiff line change
@@ -11,3 +11,14 @@ The only difference is that this version is written in react.
1111
If you are not already familiar with react, we suggest you have a look
1212
at the [Tic-Tac-Toe Tutorial](https://react.dev/learn/tutorial-tic-tac-toe),
1313
which is a great interactive introduction to react.
14+
15+
16+
To use, install [Node.js](https://nodejs.org/en/) and run
17+
```
18+
npm install
19+
```
20+
21+
Then run the app with
22+
```
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npm start
24+
```

‎react/src/App.js

+2-2
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,7 @@ function AddressForm() {
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const [savings, setSavings] = useState("")
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const [hidden, setHidden] = useState(true)
2020

21-
const upgrade = 'high_eff_hp_elec_backup'
21+
const upgrade = 'hvac__heat_pump_seer24_hspf13'
2222

2323
// This is the URL for the REM API.
2424
const remApiURL = "https://api.rewiringamerica.org/api/v1/rem/address"
@@ -174,4 +174,4 @@ export default function App() {
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</div>
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</>
176176
);
177-
}
177+
}

‎rem-with-nextjs/app/serverSavings.ts

+1-1
Original file line numberDiff line numberDiff line change
@@ -30,7 +30,7 @@ const RA_API_KEY = await accessRaApiKey()
3030

3131
export default async function serverSavings(address : string, currentFuel: string) {
3232

33-
const upgrade = 'high_eff_hp_elec_backup';
33+
const upgrade = 'hvac__heat_pump_seer24_hspf13';
3434

3535
// This is the URL for the REM API.
3636
const remApiURL = "https://api.rewiringamerica.org/api/v1/rem/address";

‎www/index.html

+1-1
Original file line numberDiff line numberDiff line change
@@ -36,7 +36,7 @@
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const address = document.getElementById("address").value;
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const fuel = document.querySelector('input[name="fuel"]:checked').value;
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const base_url = "https://api.rewiringamerica.org/api/v1/rem/address"
39-
const url = encodeURI(base_url + "?upgrade=high_eff_hp_elec_backup&address=" + address + "&heating_fuel=" + fuel)
39+
const url = encodeURI(base_url + "?upgrade=hvac__heat_pump_seer24_hspf13&address=" + address + "&heating_fuel=" + fuel)
4040
const options = {method: 'GET', accept: "application/json", headers: {Authorization: "Bearer " + api_key}};
4141

4242
console.log("Fetching " + url)

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