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Adding in pipline to test model simulations
This commit is contained in:
Khalim Conn-Kowlessar
parent
68320571ba
commit
a0bbdadd1f
2 changed files with 399 additions and 0 deletions
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@ -184,6 +184,15 @@ class Property:
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recommendation_record["walls_thermal_transmittance_ending"] = recommendation["new_u_value"]
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recommendation_record["walls_insulation_thickness_ending"] = "above average"
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recommendation_record["walls_energy_eff_ending"] = "Good"
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if recommendation["type"] == "external_wall_insulation":
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recommendation_record["external_insulation"] = True
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recommendation_record["internal_insulation"] = False
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if recommendation["type"] == "internal_wall_insulation":
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recommendation_record["external_insulation"] = False
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recommendation_record["internal_insulation"] = True
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else:
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if recommendation_record["walls_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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@ -269,6 +278,11 @@ class Property:
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else:
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raise ValueError("Invalid glazing type - implement me")
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if is_secondary_glazing:
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recommendation_record["glazed_type_ending"] = "secondary glazing"
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else:
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recommendation_record["glazed_type_ending"] = "double glazing installed during or after 2002 "
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if recommendation["type"] == "solar_pv":
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recommendation_record["photo_supply_ending"] = recommendation["photo_supply"]
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385
etl/testing_data/sap_model_simulation.py
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385
etl/testing_data/sap_model_simulation.py
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@ -0,0 +1,385 @@
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import pandas as pd
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from utils.s3 import read_dataframe_from_s3_parquet
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from backend.Property import Property
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def app():
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dataset = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-datalake-dev",
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file_key="dataset_with0perm_all.parquet"
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)
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thresholds = dataset["total_floor_area_starting"].quantile(
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[0.3, 0.6, 0.9]
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).values
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dataset["floor_area_quantile"] = pd.cut(
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dataset["total_floor_area_starting"],
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bins=[0] + list(thresholds) + [float('inf')],
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labels=False,
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include_lowest=True
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)
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# We want to set up some tests to deduce the following:
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# For different property types, of various sizes, what is the impact of the various measures that we recommend
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# 1) Insulating the loft. We test the impact of bringing the loft to 270mm insulation and 300mm insulation
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property_types = dataset[
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["property_type", "built_form", "floor_area_quantile", "construction_age_band"]
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].drop_duplicates()
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property_types = property_types.sort_values(
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["property_type", "built_form", "floor_area_quantile", "construction_age_band"]
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)
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# For each property type congifuration, we take an example property with different starting loft thresholds. We take
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# the value with the lowest U-value, since when simulating, we often work with particularly low u-values
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# TODOS
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# 1) When simulating with loft insulation, make sure is_loft is definitely true, because the roof could start as
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# pitched, but is_loft false
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# TODO: We have a description: "Pitched, loft insulation", which seems to have its insulation thickness set to
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# "none"
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# Example UPRN: 100021359753, 10001204228
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# TODO: For windows, we have glazing_type and glazed_type. When simulating, we don't set glazed_type_ending which
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# could be set to "double glazing installed during or after 2002" (THIS HAS BEEN ADDED!)
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# TODO: When simulating external wall insulation vs internal wall insulation, I need to set the external_insulation
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# or internal_insulation boolean values to true (THIS HAS BEEN ADDED!)
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# TODO: We could probably re-map some of the values of glazed_type_ending
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# For simulating
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# 1) loft insulation - we take the lowest u-value when loft insulation is 270mm and 300mm, the values we most
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# commonly simulate to - For loft insulation, these values are in-line with
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best_270mm_uvalue = dataset[dataset["roof_insulation_thickness"] == "270"]["roof_thermal_transmittance"].min()
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best_300mm_uvalue = dataset[dataset["roof_insulation_thickness"] == "300"]["roof_thermal_transmittance"].min()
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# 2) Intenal wall insulation - we take the lowest u-value when simulating internal wall insulation
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best_internal_wall_uvalue = dataset[
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dataset["internal_insulation"] & dataset["is_solid_brick"]
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]["walls_thermal_transmittance"].min()
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# 3) External wall insulation - we take the lowest u-value when simulating external wall insulation
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best_external_wall_uvalue = dataset[
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dataset["external_insulation"] & dataset["is_solid_brick"]
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]["walls_thermal_transmittance"].min()
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# 4) Cavity wall insulation - we take the lowest u-value when simulating cavity wall insulation
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# This is 0.28, which is a sufficiently low value
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best_cavity_wall_uvalue = dataset[
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dataset["is_cavity_wall"] & dataset["is_filled_cavity"] & (~dataset["external_insulation"]) & (
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~dataset["internal_insulation"])
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]["walls_thermal_transmittance"].min()
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loft_insulation_testing_data = []
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solid_wall_testing_data = []
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cavity_wall_testing_data = []
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solid_floor_testing_data = []
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suspended_floor_testing_data = []
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single_glazed_testing_data = []
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partial_double_glazed_testing_data = []
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partial_secondary_glazed_testing_data = []
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for property_config in property_types.itertuples():
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# Take a sample row
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population = dataset[
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(dataset["property_type"] == property_config.property_type) &
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(dataset["built_form"] == property_config.built_form) &
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(dataset["floor_area_quantile"] == property_config.floor_area_quantile) &
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(dataset["construction_age_band"] == property_config.construction_age_band)
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]
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# 1) Loft insulation
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# For loft insulation, there are two scenarios we test.
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# 1) Loft insulation to 270mm
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# 2) Lost insulation to 300mm
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for insulation_thickness in ["none", "12", "50", "75", "100", "150", "200", "250"]:
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if insulation_thickness == "none":
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row = population[
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(population["roof_insulation_thickness"] == "none") &
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(population["is_pitched"])
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].sample(1)
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else:
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row = population[
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(population["roof_insulation_thickness"] == insulation_thickness) &
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(population["is_pitched"])
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].sample(1)
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loft_insulation_270mm_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "loft_insulation_270mm",
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"type": "loft_insulation",
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"new_u_value": best_270mm_uvalue,
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"parts": [
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{"depth": 270}
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]
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}
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)
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loft_insulation_300mm_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "loft_insulation_300mm",
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"type": "loft_insulation",
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"new_u_value": best_300mm_uvalue,
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"parts": [
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{"depth": 300}
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]
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}
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)
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# Insert simulation specific configuration details
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loft_insulation_270mm_simulation = {
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"simulation_ending_insulation_thickness": "270",
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"simulation_starting_insulation_thickness": insulation_thickness,
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**loft_insulation_270mm_simulation
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}
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loft_insulation_300mm_simulation = {
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"simulation_ending_insulation_thickness": "300",
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"simulation_starting_insulation_thickness": insulation_thickness,
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**loft_insulation_300mm_simulation
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}
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loft_insulation_testing_data.append(loft_insulation_270mm_simulation)
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loft_insulation_testing_data.append(loft_insulation_300mm_simulation)
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# 2) Solid wall insulation
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solid_wall_sample = population[
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population["is_solid_brick"] & (population["walls_insulation_thickness"] == "none")
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]
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# We take 1 sample for each value of walls_thermal_transmittance
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for uvalue in solid_wall_sample["walls_thermal_transmittance"].unique():
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row = solid_wall_sample[
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solid_wall_sample["walls_thermal_transmittance"] == uvalue
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].sample(1)
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# Simulated IWI
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internal_wall_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0].to_dict("records")[0],
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recommendation_record=row.copy(),
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recommendation={
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"recommendation_id": "internal_wall_insulation",
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"type": "internal_wall_insulation",
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"new_u_value": best_internal_wall_uvalue,
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"parts": []
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}
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)
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# Simulated EWI
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best_external_wall_uvalue_wall_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy(),
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recommendation={
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"recommendation_id": "external_wall_insulation",
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"type": "external_wall_insulation",
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"new_u_value": best_external_wall_uvalue,
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"parts": []
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}
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)
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# The iww/ewi simulations will be next to each other, so we can see how they differ for the same property
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solid_wall_testing_data.append(internal_wall_insulation_simulation)
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solid_wall_testing_data.append(best_external_wall_uvalue_wall_insulation_simulation)
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# 3) Cavity wall insulation
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cavity_wall_sample = population[
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population["is_cavity_wall"] & (~population["is_filled_cavity"]) & (
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~population["external_insulation"]
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) & (~population["internal_insulation"])
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]
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# We take 1 sample for each value of walls_thermal_transmittance
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for uvalue in cavity_wall_sample["walls_thermal_transmittance"].unique():
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row = cavity_wall_sample[
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cavity_wall_sample["walls_thermal_transmittance"] == uvalue
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].sample(1)
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# Simulated filled cavity
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filled_cavity_wall_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "cavity_wall_insulation",
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"type": "cavity_wall_insulation",
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"new_u_value": best_cavity_wall_uvalue,
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"parts": []
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}
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)
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cavity_wall_testing_data.append(filled_cavity_wall_insulation_simulation)
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# 4) Solid floor insulation
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solid_floor_sample = population[
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population["is_solid"] & (population["floor_insulation_thickness"] == "none")
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]
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# We have many different values of u-value for solid floors, we we'll take a sample at the 25%, 50% and 75%
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# values
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# We must take a value that is in one of the unique values for floor_thermal_transmittance
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for uvalue in solid_floor_sample["floor_thermal_transmittance"].quantile([0.25, 0.5, 0.75]).values:
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nearest_value = solid_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin()
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nearest_row = solid_floor_sample.loc[[nearest_value]].sample(1)
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# Simulated solid floor insulation
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solid_floor_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=nearest_row["uprn"].values[0],
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recommendation_record=nearest_row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "solid_floor_insulation",
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"type": "solid_floor_insulation",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": []
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}
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)
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solid_floor_testing_data.append(solid_floor_insulation_simulation)
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# 5) Suspended floor insulation
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suspended_floor_sample = population[
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population["is_suspended"] & (population["floor_insulation_thickness"] == "none")
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]
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# We take the same approach as for solid floors
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for uvalue in suspended_floor_sample["floor_thermal_transmittance"].quantile([0.25, 0.5, 0.75]).values:
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nearest_value = suspended_floor_sample['floor_thermal_transmittance'].sub(uvalue).abs().idxmin()
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nearest_row = suspended_floor_sample.loc[[nearest_value]].sample(1)
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# Simulated suspended floor insulation
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suspended_floor_insulation_simulation = Property.create_recommendation_scoring_data(
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property_id=nearest_row["uprn"].values[0],
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recommendation_record=nearest_row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "suspended_floor_insulation",
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"type": "suspended_floor_insulation",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": []
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}
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)
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suspended_floor_testing_data.append(suspended_floor_insulation_simulation)
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# 6) Windows - single glazing
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single_glazing_sample = population[
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(population["glazing_type"] == "single")
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]
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# We take multiple values for multi_glaze_proportion_starting. We definitely need zero, but then we also
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# take the 25%, 50% and 75% values
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multi_glaze_values = [0] + list(
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single_glazing_sample["multi_glaze_proportion_starting"].quantile([0.25, 0.5, 0.75]).values
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)
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multi_glaze_values = set(multi_glaze_values)
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for value in multi_glaze_values:
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row = single_glazing_sample[
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single_glazing_sample["multi_glaze_proportion_starting"] == value
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].sample(1)
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# For single glazed windows, we can recommend double glazing or secondary glazing
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# Simulated double glazing
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double_glazing_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "windows_glazing",
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"type": "windows_glazing",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"is_secondary_glazing": False
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}
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)
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# Simulated secondary glazing
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secondary_glazing_simulation = Property.create_recommendation_scoring_data(
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property_id=row["uprn"].values[0],
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recommendation_record=row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "windows_glazing",
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"type": "windows_glazing",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"is_secondary_glazing": True
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}
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)
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# Add in simulation specific details
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# Add to the beginning of the dictionary
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double_glazing_simulation = {
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"simulation_ending_window_finish": "double",
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**double_glazing_simulation
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}
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secondary_glazing_simulation = {
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"simulation_ending_window_finish": "secondary",
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**secondary_glazing_simulation
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}
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single_glazed_testing_data.append(double_glazing_simulation)
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single_glazed_testing_data.append(secondary_glazing_simulation)
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# 7) Windows - partial double glazed
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partial_double_glazing_sample = population[
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(population["glazing_type"] == "double") & (population["multi_glaze_proportion_starting"] > 0) & (
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population["multi_glaze_proportion_starting"] < 100
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)
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]
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for value in partial_double_glazing_sample["multi_glaze_proportion_starting"].quantile(
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[0.25, 0.5, 0.75]
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).values:
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nearest_value = partial_double_glazing_sample['multi_glaze_proportion_starting'].sub(value).abs().idxmin()
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nearest_row = partial_double_glazing_sample.loc[[nearest_value]].sample(1)
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# If we start with partial double glazing, we recommend completing the job
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# Simulated double glazing
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double_glazing_simulation = Property.create_recommendation_scoring_data(
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property_id=nearest_row["uprn"].values[0],
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recommendation_record=nearest_row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "windows_glazing",
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"type": "windows_glazing",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"is_secondary_glazing": False
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}
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)
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partial_double_glazed_testing_data.append(double_glazing_simulation)
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# 8) Windows - partial secondary glazed
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partial_secondary_glazing_sample = population[
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(population["glazing_type"] == "secondary") & (population["multi_glaze_proportion_starting"] > 0) & (
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population["multi_glaze_proportion_starting"] < 100
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)
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]
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for value in partial_secondary_glazing_sample["multi_glaze_proportion_starting"].quantile(
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[0.25, 0.5, 0.75]
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).values:
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nearest_value = partial_secondary_glazing_sample['multi_glaze_proportion_starting'].sub(
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value).abs().idxmin()
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nearest_row = partial_secondary_glazing_sample.loc[[nearest_value]].sample(1)
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# If we start with partial secondary glazing, we recommend completing the job
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# Simulated secondary glazing
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secondary_glazing_simulation = Property.create_recommendation_scoring_data(
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property_id=nearest_row["uprn"].values[0],
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recommendation_record=nearest_row.copy().to_dict("records")[0],
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recommendation={
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"recommendation_id": "windows_glazing",
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"type": "windows_glazing",
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"new_u_value": None, # This doesn't matter at the moment
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"parts": [],
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"is_secondary_glazing": True
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}
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)
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partial_secondary_glazed_testing_data.append(secondary_glazing_simulation)
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