mirror of
https://github.com/Hestia-Homes/Model.git
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968 lines
54 KiB
Python
968 lines
54 KiB
Python
import numpy as np
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import pandas as pd
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from pandas import Timestamp
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from numpy import nan
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import datetime
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from copy import deepcopy
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from recommendations.optimiser.CostOptimiser import CostOptimiser
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from recommendations.optimiser.GainOptimiser import GainOptimiser
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from backend.Funding import Funding
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project_scores_matrix = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv")
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partial_project_scores_matrix = pd.read_csv("backend/tests/test_data/ECO4_Partial_Project_Scores_Matrix_v6.csv")
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partial_project_scores_matrix.columns = ['Measure category', 'Measure_Type', 'Pre_Main_Heating_Source',
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'Post_Main_Heating_Source', 'Total Floor Area Band', 'Starting Band',
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'Average Treatable Factor', 'Cost Savings', 'SAP Savings']
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whlg_eligible_postcodes = pd.DataFrame([{"Postcode": "ab12cd"}])
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funding = Funding(
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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=13.5,
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eco4_social_solid_abs_rate=17,
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eco4_private_cavity_abs_rate=13.5,
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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=22,
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gbis_private_solid_abs_rate=28,
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tenure="Social"
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)
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# Assume these costs have been adjusted
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property_recommendations = [
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[{'phase': 0, 'parts': [{'id': 2466, 'type': 'external_wall_insulation',
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'description': 'EWI Pro EPS external wall insulation system with '
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'Brick Slip finish',
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'depth': 150.0, 'depth_unit': 'mm', 'cost': None,
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'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.02631579,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.038,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
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'total_cost': 298.35,
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'notes': 'This is the quoted value from SCIS',
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'is_installer_quote': True, 'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 19090.810139104888,
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'labour_hours': 0.0, 'labour_days': 0.0}],
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'type': 'external_wall_insulation', 'measure_type': 'external_wall_insulation',
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'description': 'Install 150mm EWI Pro EPS external wall insulation system with Brick '
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'Slip finish on external walls',
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'starting_u_value': 1.7, 'new_u_value': 0.32, 'already_installed': False,
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'sap_points': np.float64(9.6),
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'simulation_config': {'is_as_built_ending': False, 'walls_is_assumed_ending': False,
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'walls_insulation_thickness_ending': 'average',
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'external_insulation_ending': True,
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'walls_energy_eff_ending': 'Good',
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'walls_thermal_transmittance_ending': 0.23},
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'description_simulation': {'walls-description': 'Solid brick, with external insulation',
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'walls-energy-eff': 'Good'}, 'total': 19090.810139104888,
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'labour_hours': 0.0, 'labour_days': 0.0, 'survey': False,
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'recommendation_id': '0_phase=0', 'efficiency': 11229.568317120522,
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'co2_equivalent_savings': np.float64(0.5), 'heat_demand': np.float64(37.099999999999994),
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'kwh_savings': np.float64(1827.8999999999996),
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'energy_cost_savings': np.float64(136.1247882352941)}, {'phase': 0, 'parts': [
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{'id': 2373, 'type': 'internal_wall_insulation', 'description': 'SWIP EcoBatt & Plastered finish',
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'depth': 95.0,
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'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.03125,
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'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.032,
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'thermal_conductivity_unit': None,
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'link': 'SCIS', 'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True,
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'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 2.1,
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'plant_cost': 0.0, 'total_cost': 89.0, 'notes': None, 'is_installer_quote': True,
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'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 5694.929118083911, 'labour_hours': 134.37473199973275,
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'labour_days': 4.199210374991648}], 'type': 'internal_wall_insulation',
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'measure_type': 'internal_wall_insulation',
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'description': 'Install 95mm '
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'SWIP EcoBatt & '
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'Plastered '
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'finish on '
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'internal walls',
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'starting_u_value': 1.7,
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'new_u_value': 0.32,
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'already_installed': False,
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'sap_points': 6,
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'simulation_config': {
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'is_as_built_ending': False,
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'walls_is_assumed_ending':
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False,
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'walls_insulation_thickness_ending': 'average',
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'internal_insulation_ending': True,
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'walls_energy_eff_ending':
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'Good',
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'walls_thermal_transmittance_ending': 0.29},
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'description_simulation': {
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'walls-description': 'Solid '
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'brick, with internal '
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'insulation',
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'walls-energy-eff': 'Good'},
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'total': 5694.929118083911,
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'labour_hours': 134.37473199973275,
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'labour_days': 4.199210374991648,
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'survey': True,
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'recommendation_id': '1_phase=0',
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'efficiency': 3349.6383047552417,
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'co2_equivalent_savings': np.float64(
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0.5),
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'heat_demand': np.float64(
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35.30000000000001),
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'kwh_savings': np.float64(
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1432.3999999999996),
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'energy_cost_savings': np.float64(
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106.67167058823532)}], [
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{'phase': 1, 'parts': [{'id': 2351, 'type': 'loft_insulation',
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'description': 'Knauf Loft Roll 44 glass fibre roll',
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'depth': 300.0, 'depth_unit': 'mm', 'cost': None,
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'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.044,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 0.11, 'plant_cost': 0.0,
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'total_cost': 15.0,
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'notes': 'This is the cost if there is less than 100mm '
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'existing insulation',
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'is_installer_quote': True, 'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8,
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'labour_days': 1}], 'type': 'loft_insulation',
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'measure_type': 'loft_insulation',
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'description': 'Install 300mm of Knauf Loft Roll 44 glass fibre roll in your loft',
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'starting_u_value': 2.3, 'new_u_value': 2.3, 'sap_points': np.float64(2.4),
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'already_installed': False,
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'simulation_config': {'is_loft_ending': True, 'roof_is_assumed_ending': False,
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'roof_insulation_thickness_ending': '300',
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'roof_thermal_transmittance_ending': 2.3,
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'roof_energy_eff_ending': 'Very Good'},
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'description_simulation': {'roof-description': 'Pitched, 300mm loft insulation',
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'roof-energy-eff': 'Very Good'}, 'total': 645.0,
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'labour_hours': 8, 'labour_days': 1, 'survey': False, 'recommendation_id': '2_phase=1',
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'efficiency': 278.1347826086957,
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'co2_equivalent_savings': np.float64(0.10000000000000009),
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'heat_demand': np.float64(1.5), 'kwh_savings': np.float64(566.1499999999996),
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'energy_cost_savings': np.float64(42.16152352941185)}], [{'phase': 2, 'parts': [
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{'id': 2329, 'type': 'mechanical_ventilation', 'description': 'Mechanical Extract Ventilation', 'depth': 0.0,
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'depth_unit': None, 'cost': None, 'cost_unit': 'gbp_per_unit', 'r_value_per_mm': nan,
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'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': None,
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'thermal_conductivity_unit': None,
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'link': 'SCIS', 'created_at': datetime.datetime(2025, 3, 16, 15, 26, 22, 379496), 'is_active': True,
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'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0,
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'plant_cost': 0.0, 'total_cost': 350.0, 'notes': None, 'is_installer_quote': True, 'total': 700.0,
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'quantity': 2,
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'quantity_unit': 'part'}], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
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'description': 'Install 2 '
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'Mechanical '
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'Extract '
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'Ventilation units',
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'starting_u_value': None,
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'new_u_value': None,
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'already_installed': False,
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'sap_points': np.float64(
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-0.10000000000000142),
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'heat_demand': np.float64(
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-3.3999999999999773),
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'kwh_savings': np.float64(
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-53.80000000000018),
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'co2_equivalent_savings': np.float64(
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0.0),
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'energy_cost_savings': np.float64(
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-4.0065176470588995),
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'total': 700.0,
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'labour_hours': 8,
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'labour_days': 1.0,
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'simulation_config': {
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'mechanical_ventilation_ending': 'mechanical, '
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'extract only'},
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'description_simulation': {
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'mechanical-ventilation': 'mechanical, '
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'extract only'},
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'recommendation_id': '3_phase=2',
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'efficiency': 0}], [
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{'phase': 3, 'parts': [{'id': 2409, 'type': 'suspended_floor_insulation',
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'description': 'Q-bot underfloor insulation', 'depth': 75.0,
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'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2',
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'r_value_per_mm': 0.045454547,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.022,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 1.63, 'plant_cost': 0.0,
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'total_cost': 93.75,
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'notes': 'Linearly interpolated based on Qbot costs',
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'is_installer_quote': True, 'quantity': 43.0,
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'quantity_unit': 'm2', 'total': 4031.25,
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'labour_hours': 70.08999999999999,
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'labour_days': 2.920416666666666}],
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'type': 'suspended_floor_insulation', 'measure_type': 'suspended_floor_insulation',
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'description': 'Install 75mm Q-bot underfloor insulation insulation in suspended '
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'floor',
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'starting_u_value': 0.83, 'new_u_value': 0.22, 'sap_points': 2, 'survey': True,
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'already_installed': False, 'simulation_config': {'floor_is_assumed_ending': False,
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'floor_insulation_thickness_ending': 'average',
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'floor_thermal_transmittance_ending': 0.685593},
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'description_simulation': {'floor-description': 'Suspended, insulated'},
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'total': 4031.25, 'labour_hours': 70.08999999999999, 'labour_days': 2.920416666666666,
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'recommendation_id': '4_phase=3', 'efficiency': 4856.707710843373,
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'co2_equivalent_savings': np.float64(0.20000000000000018),
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'heat_demand': np.float64(33.5), 'kwh_savings': np.float64(1021.1999999999998),
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'energy_cost_savings': np.float64(76.04936470588231)}], [
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{'phase': 4, 'parts': [], 'type': 'low_energy_lighting',
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'measure_type': 'low_energy_lighting',
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'description': 'Install low energy lighting in -886 outlets', 'starting_u_value': None,
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'new_u_value': None, 'already_installed': False, 'sap_points': 2,
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'kwh_savings': -48508.5, 'energy_cost_savings': -12481.237049999998,
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'co2_equivalent_savings': -7.858377,
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'description_simulation': {'lighting-energy-eff': 'Very Good',
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'lighting-description': 'Low energy lighting in all fixed'
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' outlets',
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'low-energy-lighting': 100}, 'total': -3411.1000000000004,
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'labour_hours': 1, 'labour_days': 0.125, 'survey': True,
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'recommendation_id': '5_phase=4', 'efficiency': -1705.5500000000002,
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'heat_demand': np.float64(5.099999999999994)}], [
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{'type': 'heating', 'phase': 5, 'measure_type': 'time_temperature_zone_control',
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'parts': [],
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'description': 'Upgrade heating controls to Smart Thermostats, room sensors and '
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'smart radiator valves (time & temperature zone control)',
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'total': 739.576, 'subtotal': 700.48, 'vat': 39.096000000000004,
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'labour_hours': 3.6199999999999997, 'labour_days': np.float64(1.0),
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(2.9),
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'already_installed': False, 'simulation_config': {
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'thermostatic_control_ending': 'time and temperature zone control',
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'switch_system_ending': None, 'trvs_ending': None,
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'mainheatc_energy_eff_ending': 'Very Good'}, 'description_simulation': {
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'mainheatcont-description': 'Time and temperature zone control',
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'mainheatc-energy-eff': 'Very Good'}, 'recommendation_id': '6_phase=5',
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'efficiency': 739.576, 'co2_equivalent_savings': np.float64(0.30000000000000027),
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'heat_demand': np.float64(6.599999999999994),
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'kwh_savings': np.float64(876.8000000000002),
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'energy_cost_savings': np.float64(65.29581176470589)}], [
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{'phase': 6, 'parts': [], 'type': 'secondary_heating',
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'measure_type': 'secondary_heating',
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'description': 'Remove the secondary heating system', 'starting_u_value': None,
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'new_u_value': None, 'sap_points': np.float64(3.6), 'already_installed': False,
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'total': 30.0, 'subtotal': 25.0, 'vat': 5.0, 'labour_hours': 3.0,
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'labour_days': np.float64(1.0),
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'simulation_config': {'secondheat_description_ending': 'None'},
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'description_simulation': {'secondheat-description': 'None'},
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'recommendation_id': '7_phase=6', 'efficiency': 30.0,
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'co2_equivalent_savings': np.float64(0.10000000000000009),
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'heat_demand': np.float64(15.400000000000006),
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'kwh_savings': np.float64(196.29999999999927),
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'energy_cost_savings': np.float64(14.61857647058821)}], [
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
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'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
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'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
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'has_battery': False, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
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'description_simulation': {'photo-supply': np.float64(65.0)},
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'recommendation_id': '8_phase=7', 'efficiency': np.float64(462.54923076923075),
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'co2_equivalent_savings': np.float64(0.47347873833399995),
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'heat_demand': np.float64(88.69999999999999),
|
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'kwh_savings': np.float64(2040.8566307499998),
|
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'energy_cost_savings': np.float64(525.1124110919749)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
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'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
|
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'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
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'has_battery': True, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
|
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'description_simulation': {'photo-supply': np.float64(65.0)},
|
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'recommendation_id': '9_phase=7', 'efficiency': np.float64(810.5390769230769),
|
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'co2_equivalent_savings': np.float64(0.6628702336675999),
|
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'heat_demand': np.float64(88.69999999999999),
|
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'kwh_savings': np.float64(2857.1992830499994),
|
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'energy_cost_savings': np.float64(735.1573755287648)},
|
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
|
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'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
|
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'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
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'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
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'description_simulation': {'photo-supply': np.float64(60.0)},
|
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'recommendation_id': '10_phase=7', 'efficiency': np.float64(485.54099999999994),
|
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'co2_equivalent_savings': np.float64(0.42834948104),
|
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'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
|
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'energy_cost_savings': np.float64(475.0617304809999)},
|
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
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'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
|
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
|
||
'already_installed': False, 'total': 10350.359999999999, 'subtotal': 8625.3, 'vat': 0,
|
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'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
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'description_simulation': {'photo-supply': np.float64(60.0)},
|
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'recommendation_id': '11_phase=7', 'efficiency': np.float64(862.5299999999999),
|
||
'co2_equivalent_savings': np.float64(0.599689273456),
|
||
'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
|
||
'energy_cost_savings': np.float64(665.0864226734)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
|
||
'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
|
||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
|
||
'description_simulation': {'photo-supply': np.float64(55.0)},
|
||
'recommendation_id': '12_phase=7', 'efficiency': np.float64(512.964),
|
||
'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
|
||
'kwh_savings': np.float64(1650.2708274),
|
||
'energy_cost_savings': np.float64(424.61468389001993)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
|
||
'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
|
||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
|
||
'description_simulation': {'photo-supply': np.float64(55.0)},
|
||
'recommendation_id': '13_phase=7', 'efficiency': np.float64(924.2247272727273),
|
||
'co2_equivalent_savings': np.float64(0.53600796473952),
|
||
'heat_demand': np.float64(78.3), 'kwh_savings': np.float64(2310.3791583599996),
|
||
'energy_cost_savings': np.float64(594.4605574460278)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
|
||
'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
|
||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
|
||
'description_simulation': {'photo-supply': np.float64(45.0)},
|
||
'recommendation_id': '14_phase=7', 'efficiency': np.float64(606.5253333333333),
|
||
'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
|
||
'kwh_savings': np.float64(1453.5933906),
|
||
'energy_cost_savings': np.float64(374.00957940138)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
|
||
'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
|
||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
|
||
'description_simulation': {'photo-supply': np.float64(45.0)},
|
||
'recommendation_id': '15_phase=7', 'efficiency': np.float64(1109.1773333333333),
|
||
'co2_equivalent_savings': np.float64(0.47212713326688),
|
||
'heat_demand': np.float64(64.0), 'kwh_savings': np.float64(2035.03074684),
|
||
'energy_cost_savings': np.float64(523.6134111619319)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
|
||
'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
|
||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2510.25188),
|
||
'description_simulation': {'photo-supply': np.float64(40.0)},
|
||
'recommendation_id': '16_phase=7', 'efficiency': np.float64(659.3565),
|
||
'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
|
||
'kwh_savings': np.float64(1255.12594),
|
||
'energy_cost_savings': np.float64(322.94390436199996)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
|
||
'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
|
||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2510.25188),
|
||
'description_simulation': {'photo-supply': np.float64(40.0)},
|
||
'recommendation_id': '17_phase=7', 'efficiency': np.float64(1224.84),
|
||
'co2_equivalent_savings': np.float64(0.40766490531199995),
|
||
'heat_demand': np.float64(54.3), 'kwh_savings': np.float64(1757.1763159999998),
|
||
'energy_cost_savings': np.float64(452.1214661067999)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
|
||
'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
|
||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2096.682636),
|
||
'description_simulation': {'photo-supply': np.float64(35.0)},
|
||
'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
|
||
'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
|
||
'kwh_savings': np.float64(1048.341318),
|
||
'energy_cost_savings': np.float64(269.7382211214)},
|
||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||
'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
|
||
'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
|
||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
|
||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2096.682636),
|
||
'description_simulation': {'photo-supply': np.float64(35.0)},
|
||
'recommendation_id': '19_phase=7', 'efficiency': np.float64(1373.5491428571427),
|
||
'co2_equivalent_savings': np.float64(0.3405012600864), 'heat_demand': np.float64(48.5),
|
||
'kwh_savings': np.float64(1467.6778451999999),
|
||
'energy_cost_savings': np.float64(377.6335095699599)}]
|
||
]
|
||
|
||
main_heating = {
|
||
'original_description': 'Boiler and radiators, mains gas', 'clean_description': 'Boiler and radiators, mains gas',
|
||
'has_radiators': True, 'has_fan_coil_units': False, 'has_pipes_in_screed_above_insulation': False,
|
||
'has_pipes_in_insulated_timber_floor': False, 'has_pipes_in_concrete_slab': False, 'has_boiler': True,
|
||
'has_air_source_heat_pump': False, 'has_room_heaters': False, 'has_electric_storage_heaters': False,
|
||
'has_warm_air': False, 'has_electric_underfloor_heating': False, 'has_electric_ceiling_heating': False,
|
||
'has_community_scheme': False, 'has_ground_source_heat_pump': False, 'has_no_system_present': False,
|
||
'has_portable_electric_heaters': False, 'has_water_source_heat_pump': False, 'has_electric_heat_pump': False,
|
||
'has_micro-cogeneration': False, 'has_solar_assisted_heat_pump': False, 'has_exhaust_source_heat_pump': False,
|
||
'has_community_heat_pump': False, 'has_hot-water-only': False, 'has_electric': False, 'has_mains_gas': True,
|
||
'has_wood_logs': False, 'has_coal': False, 'has_oil': False, 'has_wood_pellets': False, 'has_anthracite': False,
|
||
'has_dual_fuel_mineral_and_wood': False, 'has_smokeless_fuel': False, 'has_lpg': False, 'has_b30k': False,
|
||
'has_mineral_and_wood': False, 'has_dual_fuel_appliance': False, 'has_assumed': False, 'has_electricaire': False,
|
||
'has_assumed_for_most_rooms': False, 'has_underfloor_heating': False
|
||
}
|
||
|
||
main_fuel = {
|
||
'original_description': 'mains gas (not community)', 'clean_description': 'Mains gas not community',
|
||
'fuel_type': 'mains gas', 'tariff_type': None, 'is_community': False,
|
||
'no_individual_heating_or_community_network': False, 'complex_fuel_type': None
|
||
}
|
||
|
||
# Insert the funding uplifts
|
||
for recs in property_recommendations:
|
||
for r in recs:
|
||
# Insert randomly
|
||
# Select one of 0, 0.25 or 0.45
|
||
r["uplift"] = np.random.choice([0, 0.25, 0.45])
|
||
|
||
# We calculate the innovation uplift against each measure
|
||
for recs in property_recommendations:
|
||
for r in recs:
|
||
if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
|
||
r["innovation_uplift"] = 0
|
||
continue
|
||
r["innovation_uplift"] = funding.get_innovation_uplift(
|
||
measure=r,
|
||
starting_sap=p.data["current-energy-efficiency"],
|
||
floor_area=p.floor_area,
|
||
is_cavity=False,
|
||
current_wall_uvalue=1.7,
|
||
is_partial=False,
|
||
existing_li_thickness=150,
|
||
mainheating=p.main_heating,
|
||
main_fuel=p.main_fuel,
|
||
mainheat_energy_eff=p.data["mainheat-energy-eff"],
|
||
)
|
||
print(r["innovation_uplift"])
|
||
|
||
property_measure_types = {rec["type"] for recs in property_recommendations for rec in recs}
|
||
property_required_measures = [m for m in property_recommendations if m[0]["type"] in []]
|
||
measures_to_optimise = [m for m in property_recommendations if m[0]["type"] not in []]
|
||
|
||
# If a measure requiring ventilation is selected, and the property does not have ventilation, we enfore
|
||
# its inclusion
|
||
needs_ventilation = any(
|
||
x in property_measure_types for x in assumptions.measures_needing_ventilation
|
||
) and not p.has_ventilation
|
||
|
||
input_measures = optimiser_functions.prepare_input_measures(
|
||
measures_to_optimise, "Increasing EPC", needs_ventilation, True
|
||
)
|
||
|
||
# ---- rule definitions you can tweak -------------------------------------
|
||
|
||
HEATING_TYPES = {"air_source_heat_pump", "high_heat_retention_storage_heater", "solar_pv"}
|
||
MIN_INSULATION_OR = [{"loft_insulation"}, {"cavity_wall_insulation"}] # extend if needed
|
||
|
||
# “Funding paths”: each is a list of elements; each element is:
|
||
# - {"OR": {"types": {..}}} means choose one option from any group whose type is in that set
|
||
# - {"AND": [{"types": {..}}, {"types": {..}}]} means choose one from each of those
|
||
FUNDING_PATHS = [
|
||
# Path A: IWI OR EWI
|
||
[
|
||
{
|
||
"OR": {
|
||
"types": {"internal_wall_insulation", "external_wall_insulation"}
|
||
}
|
||
}
|
||
],
|
||
# Path B: Solar PV AND HHRSH
|
||
[{"AND": [{"types": {"solar_pv"}}, {"types": {"high_heat_retention_storage_heater"}}]}],
|
||
# Path C: ASHP alone (may still trigger min insulation rule below)
|
||
[{"OR": {"types": {"air_source_heat_pump"}}}],
|
||
#
|
||
]
|
||
|
||
|
||
def _find_measure(input_measures, measure_type):
|
||
for measures in input_measures:
|
||
for m in measures:
|
||
if measure_type in m["type"]:
|
||
return True
|
||
return False
|
||
|
||
|
||
def _make_generic_eco4_funding_paths(p, input_measures, funding_paths, remaining_insulation_type):
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Solar PV with existing eligible heating system
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
has_eligible_heating_system = funding.check_solar_eligible_heating_system(
|
||
mainheat_description=p.main_heating["clean_description"],
|
||
heating_control_description=p.main_heating_controls["clean_description"]
|
||
)
|
||
|
||
if has_eligible_heating_system and _find_measure(input_measures, "solar_pv"):
|
||
single_solar_template = [{"AND": ["solar_pv"], "reference": None}]
|
||
# We now look to pair this with any lingering insulation measures
|
||
solar_paths = []
|
||
for insulation_measure in remaining_insulation_type:
|
||
new_solar_path = deepcopy(single_solar_template)
|
||
new_solar_path[0]["AND"].append(insulation_measure)
|
||
# Make a specific reference for this path
|
||
new_solar_path[0]["reference"] = "solar_pv+" + insulation_measure + ":eco4"
|
||
solar_paths.append(new_solar_path)
|
||
|
||
if solar_paths:
|
||
funding_paths.extend(solar_paths)
|
||
else:
|
||
# If we have no insulation measures, we just add the solar PV path
|
||
funding_paths.append(single_solar_template)
|
||
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Solar PV + HHRSH
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
if (_find_measure(input_measures, "solar_pv") and
|
||
_find_measure(input_measures, "high_heat_retention_storage_heater")):
|
||
funding_paths.append(
|
||
[{"AND": ["solar_pv", "high_heat_retention_storage_heater"], "reference": "solar_pv+hhrsh:eco4"}]
|
||
)
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Solar PV + ASHP
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
if (_find_measure(input_measures, "solar_pv") and
|
||
_find_measure(input_measures, "air_source_heat_pump")):
|
||
funding_paths.append([{"AND": ["solar_pv", "air_source_heat_pump"], "reference": "solar_pv+ashp:eco4"}])
|
||
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Solar PV + Electric Boiler
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
if (_find_measure(input_measures, "solar_pv") and
|
||
_find_measure(input_measures, "electric_boiler")):
|
||
funding_paths.append([{"AND": ["solar_pv", "electric_boiler"], "reference": "solar_pv+electric_boiler:eco4"}])
|
||
|
||
# We've actually covered all possible options where solar PV can be included in a funded package, so where
|
||
# solar PV is not in a reference, we can exclude it
|
||
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Heating Upgrades
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# Must have an existing eligible heating system
|
||
|
||
measure_references = {
|
||
"boiler_upgrade": "boiler_upgrade",
|
||
"high_heat_retention_storage_heater": "hhrsh",
|
||
"air_source_heat_pump": "ashp"
|
||
}
|
||
for heating_upgrade in ["boiler_upgrade", "high_heat_retention_storage_heater", "air_source_heat_pump"]:
|
||
if _find_measure(input_measures, heating_upgrade):
|
||
# We check if we have any remaining insulation measures to be applied to the property
|
||
if remaining_insulation_type:
|
||
hhrsh_template = [
|
||
{"AND": [heating_upgrade], "reference": None}
|
||
]
|
||
hhrsh_paths = []
|
||
for insulation_measure in remaining_insulation_type:
|
||
new_hhrsh_path = deepcopy(hhrsh_template)
|
||
new_hhrsh_path[0]["AND"].append(insulation_measure)
|
||
# Make a specific reference for this path
|
||
new_hhrsh_path[0]["reference"] = (
|
||
measure_references[heating_upgrade] + "+" + insulation_measure + ":eco4"
|
||
)
|
||
hhrsh_paths.append(new_hhrsh_path)
|
||
|
||
funding_paths.extend(hhrsh_paths)
|
||
else:
|
||
# If we have no insulation measures, we just add the HHRSH path
|
||
funding_paths.append(
|
||
[{"AND": [heating_upgrade],
|
||
"reference": measure_references[heating_upgrade] + ":eco4"}]
|
||
)
|
||
|
||
return funding_paths
|
||
|
||
|
||
def _make_generic_gbis_funding_paths(input_gbis_measures, funding_paths):
|
||
"""
|
||
For GBIS, the packages are single insulation measure.
|
||
|
||
We also have potential GBIS packages that allow heating controls as a secondary measure, however this
|
||
is not currently implemented in the optimiser due to not being certain about the heating controls pre conditions
|
||
:param input_gbis_measures:
|
||
:param funding_paths:
|
||
:return:
|
||
"""
|
||
|
||
gbis_funding_paths = []
|
||
for input_measure in input_gbis_measures:
|
||
for measure in input_measure:
|
||
# We create a path for each measure
|
||
gbis_funding_paths.append([{"AND": [measure["type"]], "reference": measure["type"] + ":gbis"}])
|
||
|
||
return funding_paths + gbis_funding_paths
|
||
|
||
|
||
def make_funding_paths(p, input_measures, tenure):
|
||
"""
|
||
This function generates funding paths based on the input measures and the tenure of the property.
|
||
It checks for the presence of specific measures and creates paths that include necessary insulation measures
|
||
to meet minimum insulation requirements, particularly when a heating system is recommended.
|
||
|
||
Remaining measures that are not fixed as part of the package are then optimised
|
||
:param p: The property object containing details about the property, including main heating and controls.
|
||
:param input_measures:
|
||
:param tenure:
|
||
:return:
|
||
"""
|
||
# We handle the case of minimum insulation requirements. Whenever we have a heating system recommendation,
|
||
# we *must* include an additional insulation measure, unless the property already has sufficient insulation.
|
||
|
||
# We determine which insulation measures need to be included
|
||
wall_insulation_measures = [
|
||
"internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",
|
||
"extension_cavity_wall_insulation"
|
||
]
|
||
roof_insulation_measures = [
|
||
"loft_insulation", "flat_roof_insulation", "room_roof_insulation"
|
||
]
|
||
other_gbis_insulation_measures = [
|
||
"suspended_floor_insulation", "solid_floor_insulation",
|
||
]
|
||
# These are the insulation measures that the property still needs and so will be considered for
|
||
# filling the minimum insulation requirements
|
||
remaining_insulation_type = []
|
||
for insulation_measure in wall_insulation_measures + roof_insulation_measures:
|
||
if _find_measure(input_measures, insulation_measure):
|
||
remaining_insulation_type.append(insulation_measure)
|
||
|
||
funding_paths = []
|
||
|
||
if tenure == "Social" and p.data["current-energy-rating"] == "D":
|
||
# If the property is currently EPC D, we can only include innovation measures or measures to meet the
|
||
# minimum insulation requirements
|
||
input_measures_innovation = []
|
||
input_gbis_measures_innovation = []
|
||
for measures in input_measures:
|
||
for measure in measures:
|
||
if measure["innovation_uplift"] or measure["type"] in remaining_insulation_type:
|
||
input_measures_innovation.append([measure])
|
||
|
||
if measure["innovation_uplift"] and measure["type"] in (
|
||
remaining_insulation_type + other_gbis_insulation_measures
|
||
):
|
||
input_gbis_measures_innovation.append([measure])
|
||
|
||
funding_paths = _make_generic_eco4_funding_paths(
|
||
p, input_measures_innovation, funding_paths, remaining_insulation_type
|
||
)
|
||
|
||
# Can only be innovation GBIS measures
|
||
funding_paths = _make_generic_gbis_funding_paths(input_gbis_measures_innovation, funding_paths)
|
||
return funding_paths
|
||
|
||
if tenure == "Private":
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# EWI or IWI
|
||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
# 1) The package must include EWI or IWI if the property is privately owned
|
||
# We check if we have any EWI or IWI measures available
|
||
ewi_or_iwi = [{"OR": []}]
|
||
reference_measures = []
|
||
# If we have EWI we add it in
|
||
if _find_measure(input_measures, "external_wall_insulation"):
|
||
ewi_or_iwi[0]["OR"].append("external_wall_insulation")
|
||
reference_measures.append("ewi")
|
||
|
||
if _find_measure(input_measures, "internal_wall_insulation"):
|
||
ewi_or_iwi[0]["OR"].append("internal_wall_insulation")
|
||
reference_measures.append("iwi")
|
||
|
||
if ewi_or_iwi[0]["OR"]:
|
||
ewi_or_iwi[0]["reference"] = "+".join(reference_measures) + ":eco4"
|
||
funding_paths.append(ewi_or_iwi)
|
||
|
||
funding_paths = _make_generic_eco4_funding_paths(
|
||
p, input_measures, funding_paths, remaining_insulation_type
|
||
)
|
||
|
||
# If we have any remaining insulation measures, we add them to the funding paths
|
||
input_gbis_measures = []
|
||
for measures in input_measures:
|
||
for measure in measures:
|
||
if measure["type"] in remaining_insulation_type + other_gbis_insulation_measures:
|
||
input_gbis_measures.append([measure])
|
||
|
||
funding_paths = _make_generic_gbis_funding_paths(input_gbis_measures, funding_paths)
|
||
|
||
return funding_paths
|
||
|
||
|
||
# ---- main wrapper around your optimiser ----------------------------------
|
||
|
||
def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, social=False):
|
||
"""
|
||
run_optimizer(sub_measures, budget, target_gain) -> (picked_options, sub_cost, sub_gain)
|
||
"""
|
||
# TODO: Should be EPC D only that we require only innovation measures
|
||
# Social housing: filter to innovation-only before doing anything else
|
||
# if social:
|
||
# filtered = []
|
||
# for group in input_measures:
|
||
# opts = [o for o in group if o.get("is_innovation", False)]
|
||
# if opts:
|
||
# filtered.append(opts)
|
||
# input_measures = filtered
|
||
|
||
# Always include a "no funding path" baseline (empty fixed)
|
||
all_paths = FUNDING_PATHS + [[]]
|
||
|
||
solutions = []
|
||
for path_spec in all_paths:
|
||
# 1) expand fixed selections for this path
|
||
fixed_selections = expand_funding_path(input_measures, path_spec) if path_spec else [[]]
|
||
if not fixed_selections:
|
||
continue
|
||
|
||
for fixed in fixed_selections:
|
||
|
||
# 2) min insulation if heating is already in fixed
|
||
fixed_variants = expand_min_insulation_if_needed(input_measures, fixed)
|
||
if not fixed_variants:
|
||
continue
|
||
|
||
for fixed2 in fixed_variants:
|
||
# 3) compute fixed cost/gain, and strip those groups from subproblem
|
||
fixed_items = [opt for (_, _, opt) in fixed2]
|
||
fixed_ids = [opt['id'] for opt in fixed_items]
|
||
fixed_cost, fixed_gain = sum_cost_gain(fixed_items)
|
||
fixed_groups = {gi for (gi, _, _) in fixed2}
|
||
|
||
sub_measures = strip_groups(input_measures, fixed_groups)
|
||
|
||
# 4) run your existing optimiser for the remaining groups
|
||
# If we have a budget, we need to ensure the subproblem respects it so we remove the fixed cost (which
|
||
# may already be over budget) and the fixed gain (which may not be achievable)
|
||
picked, sub_cost, sub_gain = run_optimizer(
|
||
sub_measures,
|
||
budget - fixed_cost if budget is not None else None,
|
||
sub_target_gain=target_gain - fixed_gain if target_gain is not None else None
|
||
)
|
||
|
||
if picked is None:
|
||
continue
|
||
|
||
total_cost = fixed_cost + sub_cost
|
||
total_gain = fixed_gain + sub_gain
|
||
total_picks = fixed_items + picked
|
||
|
||
# you can change the objective here; I’ll use max gain under budget
|
||
if budget is not None and total_cost > budget + 1e-9:
|
||
continue
|
||
|
||
solutions.append({
|
||
"fixed_ids": fixed_ids,
|
||
"items": total_picks,
|
||
"total_cost": total_cost,
|
||
"total_gain": total_gain,
|
||
"path": path_spec,
|
||
})
|
||
|
||
solutions = pd.DataFrame(solutions)
|
||
|
||
return solutions
|
||
|
||
|
||
# Run inputs:
|
||
target_gain = 18.5
|
||
|
||
from itertools import product
|
||
import math
|
||
|
||
|
||
# ---- helpers -------------------------------------------------------------
|
||
|
||
def split_types(t):
|
||
# supports "external_wall_insulation+mechanical_ventilation"
|
||
return set(part.strip() for part in str(t).split('+'))
|
||
|
||
|
||
def group_has_type(group, want):
|
||
# group is a list[option], all same 'type' pattern
|
||
return any(want in split_types(opt['type']) for opt in group)
|
||
|
||
|
||
def find_groups(input_measures, type_name):
|
||
return [(gi, g) for gi, g in enumerate(input_measures) if group_has_type(g, type_name)]
|
||
|
||
|
||
def strip_groups(input_measures, taken_group_indices):
|
||
return [g for gi, g in enumerate(input_measures) if gi not in taken_group_indices]
|
||
|
||
|
||
def sum_cost_gain(items):
|
||
c = sum(float(x['cost']) for x in items)
|
||
g = sum(float(x['gain']) for x in items)
|
||
return c, g
|
||
|
||
|
||
# ---- candidate expansion -------------------------------------------------
|
||
|
||
def iter_or_candidates(input_measures, type_set):
|
||
# collect all groups that match ANY type in type_set
|
||
matching = [(gi, group) for gi, group in enumerate(input_measures)
|
||
if any(group_has_type(group, t) for t in type_set)]
|
||
if not matching:
|
||
return # nothing to yield
|
||
# choose ONE option from ANY one of these groups
|
||
for gi, group in matching:
|
||
for oi, opt in enumerate(group):
|
||
yield {"fixed": [(gi, oi, opt)]}
|
||
|
||
|
||
def iter_and_candidates(input_measures, type_vec):
|
||
# type_vec is like [{"types": {"solar_pv"}}, {"types": {"high_heat_retention_storage_heater"}}]
|
||
per_leg = []
|
||
for leg in type_vec:
|
||
leg_types = leg["types"]
|
||
leg_groups = [(gi, group) for gi, group in enumerate(input_measures)
|
||
if any(group_has_type(group, t) for t in leg_types)]
|
||
if not leg_groups:
|
||
return # this AND path isn’t available in this property; skip
|
||
# options for this leg: (gi, oi, opt)
|
||
options = []
|
||
for gi, group in leg_groups:
|
||
for oi, opt in enumerate(group):
|
||
options.append((gi, oi, opt))
|
||
per_leg.append(options)
|
||
for combo in product(*per_leg):
|
||
yield {"fixed": list(combo)}
|
||
|
||
|
||
def expand_funding_path(input_measures, path_spec):
|
||
# path_spec is a list of elements; combine all elements (they’re all required)
|
||
# Start with one empty selection; then cross-product accumulate
|
||
selections = [[]]
|
||
for elem in path_spec:
|
||
new_selections = []
|
||
if "OR" in elem:
|
||
for cand in iter_or_candidates(input_measures, elem["OR"]["types"]):
|
||
for base in selections:
|
||
new_selections.append(base + cand["fixed"])
|
||
elif "AND" in elem:
|
||
for cand in iter_and_candidates(input_measures, elem["AND"]):
|
||
for base in selections:
|
||
new_selections.append(base + cand["fixed"])
|
||
else:
|
||
raise ValueError("unknown path element")
|
||
selections = new_selections
|
||
if not selections:
|
||
break
|
||
# selections are lists of (gi, oi, opt)
|
||
# dedupe by group index (if users set a weird path that hits same group twice)
|
||
deduped = []
|
||
for sel in selections:
|
||
seen = set()
|
||
clean = []
|
||
ok = True
|
||
for gi, oi, opt in sel:
|
||
if gi in seen:
|
||
ok = False
|
||
break
|
||
seen.add(gi)
|
||
clean.append((gi, oi, opt))
|
||
if ok:
|
||
deduped.append(clean)
|
||
return deduped
|
||
|
||
|
||
# ---- minimum insulation handling ----------------------------------------
|
||
|
||
def expand_min_insulation_if_needed(input_measures, fixed_selection):
|
||
# If fixed contains any HEATING_TYPES, we must also include at least one of MIN_INSULATION_OR groups.
|
||
fixed_types = set()
|
||
fixed_group_idx = {gi for gi, _, _ in fixed_selection}
|
||
for _, _, opt in fixed_selection:
|
||
fixed_types |= split_types(opt['type'])
|
||
|
||
if not (fixed_types & HEATING_TYPES):
|
||
# BUT: heating might later be picked by optimiser… If you want to be strict,
|
||
# you can also add a *feasibility check* after optimisation and reject combos
|
||
# that pick heating without min insulation. For now we enforce only when
|
||
# already in fixed set.
|
||
return [fixed_selection]
|
||
|
||
# Build OR candidates for required insulation, but exclude groups already fixed
|
||
or_pool = []
|
||
for alt in MIN_INSULATION_OR:
|
||
types = alt
|
||
matches = []
|
||
for gi, group in enumerate(input_measures):
|
||
if gi in fixed_group_idx:
|
||
continue
|
||
if any(group_has_type(group, t) for t in types):
|
||
for oi, opt in enumerate(group):
|
||
matches.append((gi, oi, opt))
|
||
if not matches:
|
||
# No feasible insulation to satisfy the rule -> invalidate this branch
|
||
return []
|
||
or_pool.append(matches)
|
||
|
||
# choose one from any of the alt sets (if you have more than one OR bucket, pick one from at least one;
|
||
# simplest: union first OR bucket only — or take the union and pick one)
|
||
# Here we’ll take the union across all buckets then pick exactly one.
|
||
union = {(gi, oi): (gi, oi, opt)
|
||
for matches in or_pool for (gi, oi, opt) in matches}.values()
|
||
|
||
expanded = []
|
||
for gi, oi, opt in union:
|
||
# avoid duplicating the same group as fixed
|
||
if gi in fixed_group_idx:
|
||
continue
|
||
expanded.append(fixed_selection + [(gi, oi, opt)])
|
||
return expanded
|
||
|
||
|
||
# ---- tiny utilities ----------------------------------------------------------
|
||
|
||
def parse_types(t):
|
||
# e.g. "external_wall_insulation+mechanical_ventilation" -> {"external_wall_insulation","mechanical_ventilation"}
|
||
return set(map(str.strip, t.split("+"))) if isinstance(t, str) else set()
|
||
|
||
|
||
def includes_heating(opt_types):
|
||
return any(x in opt_types for x in {
|
||
"air_source_heat_pump",
|
||
"high_heat_retention_storage_heater",
|
||
"time_temperature_zone_control", # controls count as a heating measure in your pipeline
|
||
"solar_pv" # you treat PV as heating for funding logic
|
||
})
|
||
|
||
|
||
def contributes_min_insulation(opt_types):
|
||
# MIR satisfiers you mentioned (extend as needed)
|
||
return any(x in opt_types for x in {
|
||
"external_wall_insulation",
|
||
"internal_wall_insulation",
|
||
"loft_insulation",
|
||
"cavity_wall_insulation",
|
||
})
|
||
|
||
|
||
def run_optimizer(input_measures, budget=None, sub_target_gain=None, allow_slack=False):
|
||
"""
|
||
Thin wrapper over your optimisers.
|
||
Returns: list[dict] selected_options
|
||
"""
|
||
if budget is not None:
|
||
opt = GainOptimiser(
|
||
input_measures, max_cost=budget, max_gain=(sub_target_gain or float("inf")),
|
||
allow_slack=allow_slack
|
||
)
|
||
else:
|
||
if sub_target_gain is None:
|
||
raise ValueError("Either budget or target_gain must be provided.")
|
||
opt = CostOptimiser(input_measures, min_gain=sub_target_gain)
|
||
|
||
opt.setup()
|
||
opt.solve()
|
||
return opt.solution, opt.solution_cost, opt.solution_gain
|