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preparing integration test
This commit is contained in:
Khalim Conn-Kowlessar
parent
b6c56393c0
commit
b632823ca2
4 changed files with 334 additions and 0 deletions
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@ -73,6 +73,11 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
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"Electric storage heaters, Room heaters, electric": {"fuel": "Electricity", "cop": 1},
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'Boiler and underfloor heating, oil': {"fuel": "Oil", "cop": 0.85},
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"Boiler and radiators, smokeless fuel": {"fuel": "Smokeless Fuel", "cop": 0.85},
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"Boiler and radiators, mains gas, Boiler and underfloor heating, mains gas": {"fuel": "Natural Gas", "cop": 0.85},
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"Electric ceiling heating, electric": {"fuel": "Electricity", "cop": 1},
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"Air source heat pump, warm air, electric": {
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"fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100
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}
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}
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# These are the measure types where if there is a ventilation recommendation, we force the inclusion of it
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@ -1,6 +1,7 @@
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import ast
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import json
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from copy import deepcopy
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from dataclasses import replace
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from datetime import datetime
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import random
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@ -209,3 +210,322 @@ for p in tqdm(input_properties):
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)
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recommendations_scoring_data.extend(p.recommendations_scoring_data)
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recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
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recommendations_scoring_data = recommendations_scoring_data.drop(
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columns=[
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"rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
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"carbon_ending"
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]
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)
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model_predictions_mocked = {
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"sap_change_predictions": None,
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"heat_demand_predictions": None,
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"carbon_change_predictions": None,
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"heating_kwh_predictions": None,
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"hotwater_kwh_predictions": None,
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}
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for k in model_predictions_mocked.keys():
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model_predictions_mocked[k] = recommendations_scoring_data[["id"]].copy()
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model_predictions_mocked[k][['property_id', 'recommendation_id']] = (
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model_predictions_mocked[k]['id'].str.split('+', expand=True)
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)
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model_predictions_mocked[k]['phase'] = model_predictions_mocked[k]['recommendation_id'].apply(
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ModelApi.extract_phase)
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if k in ["heating_kwh_predictions", "hotwater_kwh_predictions"]:
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model_predictions_mocked[k]["predictions"] = random.choices(range(100, 3000),
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k=len(recommendations_scoring_data))
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continue
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model_predictions_mocked[k] = model_predictions_mocked[k].sort_values(["property_id", "phase"], ascending=True)
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preds = []
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for p_id in model_predictions_mocked[k]["property_id"].unique():
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# We add some amount each time
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p = [p for p in input_properties if str(p.id) == p_id][0]
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if k == "sap_change_predictions":
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start = p.data["current-energy-efficiency"]
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elif k == "heat_demand_predictions":
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start = p.data["energy-consumption-current"]
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else:
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start = p.data["co2-emissions-current"]
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df = model_predictions_mocked[k][model_predictions_mocked[k]["property_id"] == p_id].copy()
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# Add some amount each time
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to_add = random.choices(range(0, 15), k=len(df))
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to_add = np.cumsum(to_add)
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df["predictions"] = start + to_add
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preds.append(df)
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preds = pd.concat(preds)
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model_predictions_mocked[k] = preds
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for property_id in tqdm(recommendations.keys(), total=len(recommendations)):
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property_instance = [p for p in input_properties if p.id == property_id][0]
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recommendations_with_impact, impact_summary = (
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Recommendations.calculate_recommendation_impact(
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property_instance=property_instance,
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all_predictions=model_predictions_mocked,
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recommendations=recommendations,
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representative_recommendations=representative_recommendations
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)
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)
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# We use the impact_summary to update the simulation_epcs with the new SAP, heat demand, carbon, cost etc
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# at each phase
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property_instance.update_simulation_epcs(impact_summary)
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recommendations[property_id] = recommendations_with_impact
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for property_id in tqdm([p.id for p in input_properties]):
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property_recommendations = recommendations.get(property_id, [])
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property_instance = [p for p in input_properties if p.id == property_id][0]
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property_current_energy_bill = (
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Recommendations.calculate_recommendation_tenant_savings(
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property_instance=property_instance,
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kwh_simulation_predictions=model_predictions_mocked,
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property_recommendations=property_recommendations,
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ashp_cop=2.8
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)
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)
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property_instance.current_energy_bill = property_current_energy_bill
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body = PlanTriggerRequest(
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**{'budget': None, 'goal': 'Increasing EPC', 'housing_type': 'Social', 'goal_value': 'B', 'portfolio_id': 0,
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'trigger_file_path': '', 'already_installed_file_path': '',
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'patches_file_path': None, 'non_invasive_recommendations_file_path': None,
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'valuation_file_path': '',
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'required_measures': [], 'scenario_name': 'EPC B', 'scenario_id': None,
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'multi_plan': True, 'optimise': True, 'default_u_values': True, 'ashp_cop': 2.8,
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'event_type': 'remote_assessment', 'simulate_sap_10': False, 'file_type': None, 'file_format': None,
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'sheet_name': None, 'sheet_count': None, 'index_start': None, 'index_end': None}
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)
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for p in tqdm(input_properties):
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if not recommendations.get(p.id):
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continue
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# we need to double unlist because we have a list of lists
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property_measure_types = {rec["type"] for recs in recommendations[p.id] for rec in recs}
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property_required_measures = [m for m in recommendations[p.id] if m[0]["type"] in body.required_measures]
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measures_to_optimise = [m for m in recommendations[p.id] if m[0]["type"] not in body.required_measures]
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# If a measure requiring ventilation is selected, and the property does not have ventilation, we enfore
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# its inclusion
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needs_ventilation = any(
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x in property_measure_types for x in assumptions.measures_needing_ventilation
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) and not p.has_ventilation
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if not measures_to_optimise:
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# Nothing to do, we just reshape the recommendations
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recommendations[p.id] = optimiser_functions.flatten_recommendations_with_defaults(
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p.id, recommendations, set()
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)
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continue
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fixed_gain = optimiser_functions.calculate_fixed_gain(
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property_required_measures, recommendations, p, needs_ventilation
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)
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gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain)
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funding = Funding(
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tenure="Social",
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project_scores_matrix=project_scores_matrix,
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partial_project_scores_matrix=partial_project_scores_matrix,
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whlg_eligible_postcodes=whlg_eligible_postcodes,
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eco4_social_cavity_abs_rate=12.5,
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eco4_social_solid_abs_rate=17,
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eco4_private_cavity_abs_rate=12.5,
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eco4_private_solid_abs_rate=17,
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gbis_social_cavity_abs_rate=21,
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gbis_social_solid_abs_rate=25,
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gbis_private_cavity_abs_rate=21,
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gbis_private_solid_abs_rate=28,
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)
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li_thickness = convert_thickness_to_numeric(
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p.roof["insulation_thickness"], p.roof["is_pitched"], p.roof["is_flat"]
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)
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current_wall_u_value = p.walls["thermal_transmittance"]
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if current_wall_u_value is None:
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current_wall_u_value = get_wall_u_value(
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clean_description=p.walls["clean_description"],
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age_band=p.age_band,
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is_granite_or_whinstone=p.walls["is_granite_or_whinstone"],
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is_sandstone_or_limestone=p.walls["is_sandstone_or_limestone"],
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)
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# We insert the innovation uplift
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measures_to_optimise_with_uplift = deepcopy(measures_to_optimise)
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# TODO: Turn this into a function and store the innovaiton uplift
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for group in measures_to_optimise_with_uplift:
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for r in group:
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if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating",
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"extension_cavity_wall_insulation", "draught_proofing", "sealing_open_fireplace"]:
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(
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r["partial_project_score"],
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r["partial_project_funding"],
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r["innovation_uplift"],
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r["uplift_project_score"],
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) = (
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0, 0, 0, 0
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)
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continue
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(
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r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
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r["uplift_project_score"]
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) = funding.get_innovation_uplift(
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measure=r,
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starting_sap=p.data["current-energy-efficiency"],
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floor_area=p.floor_area,
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is_cavity=p.walls["is_cavity_wall"],
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current_wall_uvalue=current_wall_u_value,
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is_partial="partial" in p.walls["clean_description"].lower(),
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existing_li_thickness=li_thickness,
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mainheating=p.main_heating,
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main_fuel=p.main_fuel,
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mainheat_energy_eff=p.data["mainheat-energy-eff"],
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)
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input_measures = optimiser_functions.prepare_input_measures(
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measures_to_optimise_with_uplift, body.goal, needs_ventilation, funding=True
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)
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# When the goal is Increasing EPC, we can run the funding optimiser
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if body.goal == "Increasing EPC":
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solutions = optimise_with_funding_paths(
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p=p,
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input_measures=input_measures,
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housing_type=body.housing_type,
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budget=body.budget,
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target_gain=gain,
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funding=funding
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)
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# Given the solutions we select the optimal one
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solutions["cost_less_full_project_funding"] = np.where(
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solutions["scheme"] == "eco4",
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solutions["total_cost"] - solutions["full_project_funding"] - solutions["total_uplift"],
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solutions["total_cost"] - solutions["partial_project_funding"] - solutions["total_uplift"]
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)
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solutions["cost_less_full_project_funding"] = (
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solutions["total_cost"] - solutions["full_project_funding"] - solutions["total_uplift"]
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)
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solutions = solutions.sort_values("cost_less_full_project_funding", ascending=True)
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if solutions["meets_upgrade_target"].any():
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# If we have a solution that meets the upgrade target, we select that one
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optimal_solution = solutions[solutions["meets_upgrade_target"]].iloc[0]
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else:
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# Pick the cheapest
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optimal_solution = solutions.iloc[0]
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# This is the list of measures that we will recommend
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scheme = optimal_solution["scheme"]
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funded_measures = optimal_solution["items"] if scheme != "none" else []
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solution = optimal_solution["items"] + optimal_solution["unfunded_items"]
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# This is the total amount of funding that the project will produce (including uplifts) (£)
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project_funding = optimal_solution["full_project_funding"] if scheme == "eco4" else \
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optimal_solution["partial_project_funding"]
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# This is the total amount of funding associated to the uplift (£)
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total_uplift = optimal_solution["total_uplift"]
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# This is the funding scheme selected
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# This is the full project ABS
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full_project_score = optimal_solution["project_score"]
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# This is the partial project ABS
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partial_project_score = optimal_solution["partial_project_score"]
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# This is the uplift score ABS
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uplift_project_score = optimal_solution["total_uplift_score"]
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else:
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# We optimise and then we determine eligibility for funding, based on the measures selected
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optimiser = (
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GainOptimiser(
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input_measures, max_cost=body.budget, max_gain=gain, allow_slack=False
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) if body.budget else CostOptimiser(input_measures, min_gain=gain)
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)
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optimiser.setup()
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optimiser.solve()
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solution = optimiser.solution
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recommendation_types = []
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for measures in input_measures:
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for measure in measures:
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recommendation_types.append(measure["type"])
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recommendation_types = set(recommendation_types)
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has_wall_insulation_recommendation = any(
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(m in recommendation_types or "+".join([m, "mechanical_ventilation"])) for m in
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WALL_INSULATION_MEASURES
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)
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has_roof_insulation_recommendation = any(
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(m in recommendation_types or "+".join([m, "mechanical_ventilation"])) for m in
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ROOF_INSULATION_MEASURES
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)
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funding.check_funding(
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measures=solution,
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starting_sap=p.data["current-energy-efficiency"],
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ending_sap=p.data["current-energy-efficiency"] + sum([x["gain"] for x in solution]),
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floor_area=p.floor_area,
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mainheat_description=p.main_heating["clean_description"],
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heating_control_description=p.main_heating_controls["clean_description"],
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is_cavity=p.walls["is_cavity_wall"],
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current_wall_uvalue=current_wall_u_value,
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is_partial="partial" in p.walls["clean_description"].lower(),
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existing_li_thickness=li_thickness,
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mainheating=p.main_heating,
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main_fuel=p.main_fuel,
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mainheat_energy_eff=p.data["mainheat-energy-eff"],
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has_wall_insulation_recommendation=has_wall_insulation_recommendation,
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has_roof_insulation_recommendation=has_roof_insulation_recommendation,
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)
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# Determine the scheme
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scheme = "none"
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if funding.eco4_eligible:
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scheme = "eco4"
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if scheme == "none" and funding.gbis_eligible:
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scheme = "gbis"
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funded_measures = solution if scheme in ["gbis", "eco4"] else []
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project_funding = 0 if funding.full_project_abs is not None else funding.full_project_abs
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total_uplift = funding.eco4_uplift
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full_project_score = 0 if funding.full_project_abs is not None else funding.full_project_abs
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partial_project_score = funding.partial_project_abs
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uplift_project_score = funding.eco4_uplift if scheme == "eco4" else funding.gbis_uplift
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selected = {r["id"] for r in solution}
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if property_required_measures:
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solution = optimiser_functions.add_required_measures(
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property_id=p.id, property_required_measures=property_required_measures,
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recommendations=recommendations, selected=selected,
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)
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# Add best practice measures (ventilation/trickle vents)
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selected = optimiser_functions.add_best_practice_measures(p.id, solution, recommendations, selected)
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# Final flattening - Don't do this!
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# recommendations[p.id] = optimiser_functions.flatten_recommendations_with_defaults(
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# p.id, recommendations, selected
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# )
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# TODO: functionise
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for measure in funded_measures:
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if "+mechanical_ventilation" in measure["type"]:
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measure["type"] = measure["type"].split("+mechanical_ventilation")[0]
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p.insert_funding(
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scheme=scheme,
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funded_measures=funded_measures,
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project_funding=project_funding,
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total_uplift=total_uplift,
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full_project_score=full_project_score,
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partial_project_score=partial_project_score,
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uplift_project_score=uplift_project_score
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)
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@ -82,6 +82,14 @@ class HeatingRecommender:
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"controls_prefix": ""
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},
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"dual": None
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},
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'Electric storage heaters, room heaters, electric': {
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"hhr": {
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"mainheating_description": "Electric storage heaters, radiators",
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"recommendation_description": "Install high heat retention electric storage heaters.",
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"controls_prefix": ""
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},
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"dual": None
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}
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}
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@ -693,6 +693,7 @@ class Recommendations:
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if hotwater_description in [
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"From main system", "From main system, no cylinder thermostat",
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'From main system, waste water heat recovery'
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]:
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return {
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"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
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