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getting tests in place for optimier functions
This commit is contained in:
Khalim Conn-Kowlessar
parent
eecb9070cb
commit
a7c95ec897
3 changed files with 482 additions and 2 deletions
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import pandas as pd
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import backend.app.assumptions as assumptions
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from Property import Property
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from app.plan.schemas import PlanTriggerRequest
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from backend.Property import Property
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from backend.app.plan.schemas import PlanTriggerRequest
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from backend.app.utils import epc_to_sap_lower_bound
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from recommendations.optimiser.CostOptimiser import CostOptimiser
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350
recommendations/tests/test_data/measures_to_optimise.py
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350
recommendations/tests/test_data/measures_to_optimise.py
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import datetime
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import numpy as np
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from numpy import nan
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from pandas import Timestamp
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measures_to_optimise = [
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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', '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, 'material_cost': 0.0,
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'labour_cost': 0.0, 'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
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'total_cost': 298.35, '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 Slip '
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'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, '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, 'recommendation_id': '0_phase=0',
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'efficiency': 11229.568317120522, 'co2_equivalent_savings': np.float64(0.5),
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'heat_demand': np.float64(37.099999999999994), 'kwh_savings': np.float64(1813.199999999999),
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'energy_cost_savings': np.float64(135.03007058823516)}, {'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 SWIP '
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'EcoBatt & '
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'Plastered finish '
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'on 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': False,
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'walls_insulation_thickness_ending': 'average',
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'internal_insulation_ending':
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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, '
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'with '
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'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), 'heat_demand': np.float64(
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35.30000000000001), 'kwh_savings': np.float64(1424.699999999999), 'energy_cost_savings': np.float64(
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106.09824705882352)}], [{'phase': 1, 'parts': [
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{'id': 2351, 'type': 'loft_insulation', 'description': 'Knauf Loft Roll 44 glass fibre roll', 'depth': 300.0,
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'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
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'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.044,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin', 'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.11, 'plant_cost': 0.0, 'total_cost': 15.0,
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'notes': 'This is the cost if there is less than 100mm existing insulation', 'is_installer_quote': True,
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'quantity': 63.98796761892035, 'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8, 'labour_days': 1}],
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'type': 'loft_insulation', '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,
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'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(572.5500000000002),
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'energy_cost_savings': np.float64(42.638135294117774)}], [{'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'}
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], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
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'description':
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'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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-45.899999999999636),
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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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-3.4181999999999846),
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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, extract only'},
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'description_simulation': {
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'mechanical-ventilation': 'mechanical, extract only'},
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'recommendation_id': '3_phase=2',
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'efficiency': 0}
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], [
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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, 'quantity_unit': 'm2',
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'total': 4031.25, '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 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':
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'average',
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'floor_thermal_transmittance_ending':
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0.685593},
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'description_simulation': {'floor-description': 'Suspended, insulated'}, 'total': 4031.25,
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'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), 'heat_demand': np.float64(33.5),
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'kwh_savings': np.float64(1018.0999999999995),
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'energy_cost_savings': np.float64(75.8185058823529)}], [
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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 14 outlets', 'starting_u_value': None,
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'new_u_value': None, 'already_installed': False, 'sap_points': 2, 'kwh_savings': 766.5,
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'energy_cost_savings': 197.22044999999997,
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'co2_equivalent_savings': np.float64(0.09999999999999964),
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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': 58.8, 'labour_hours': 1,
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'labour_days': 0.125, 'survey': True, 'recommendation_id': '5_phase=4', 'efficiency': 29.4,
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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 smart '
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'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,
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'simulation_config': {'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'},
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'description_simulation': {'mainheatcont-description': 'Time and temperature zone control',
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'mainheatc-energy-eff': 'Very Good'},
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'recommendation_id': '6_phase=5', 'efficiency': 739.576,
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'co2_equivalent_savings': np.float64(0.30000000000000027),
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'heat_demand': np.float64(6.599999999999994), 'kwh_savings': np.float64(853.6999999999998),
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'energy_cost_savings': np.float64(63.57554117647055)}], [
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{'phase': 6, 'parts': [], 'type': 'secondary_heating', '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(202.30000000000018),
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'energy_cost_savings': np.float64(15.065400000000011)}], [
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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),
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'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),
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'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),
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'co2_equivalent_savings': np.float64(0.599689273456),
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'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
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'energy_cost_savings': np.float64(665.0864226734)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
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'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
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'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)}]
|
||||
]
|
||||
|
|
@ -1,6 +1,10 @@
|
|||
import pytest
|
||||
import numpy as np
|
||||
from types import SimpleNamespace
|
||||
from recommendations.tests.test_data.measures_to_optimise import measures_to_optimise
|
||||
from recommendations.optimiser import optimiser_functions
|
||||
from recommendations.optimiser.GainOptimiser import GainOptimiser
|
||||
from recommendations.optimiser.CostOptimiser import CostOptimiser
|
||||
|
||||
|
||||
class TestPrepareInputMeasures:
|
||||
|
|
@ -133,3 +137,129 @@ class TestFlattenRecommendationsWithDefaults:
|
|||
assert all("default" in rec for rec in result)
|
||||
assert next(r for r in result if r["recommendation_id"] == "b")["default"] is True
|
||||
assert next(r for r in result if r["recommendation_id"] == "a")["default"] is False
|
||||
|
||||
|
||||
class TestIncreasingEpcE2e:
|
||||
"""
|
||||
Test out the classic increasing EPC optimisation flow end-to-end.
|
||||
We have a goal (Increasing EPC), no budget, and we expect the optimiser to choose
|
||||
the best set of measures and include best-practice ventilation.
|
||||
"""
|
||||
|
||||
@pytest.fixture
|
||||
def setup_case(self):
|
||||
# ✅ Dummy property object
|
||||
p = SimpleNamespace(
|
||||
id="P1",
|
||||
has_ventilation=False,
|
||||
data={"current-energy-efficiency": "52"},
|
||||
)
|
||||
|
||||
# ✅ Dummy request body
|
||||
body = SimpleNamespace(
|
||||
goal="Increasing EPC",
|
||||
goal_value="C",
|
||||
optimise=True,
|
||||
budget=None,
|
||||
simulate_sap_10=False,
|
||||
required_measures=[]
|
||||
)
|
||||
|
||||
# ✅ Use your massive measures_to_optimise list
|
||||
|
||||
recommendations = {"P1": measures_to_optimise}
|
||||
|
||||
return p, body, recommendations
|
||||
|
||||
def test_end_to_end_increasing_epc(self, setup_case, monkeypatch):
|
||||
p, body, recommendations = setup_case
|
||||
|
||||
# ✅ Patch assumptions to simplify behaviour
|
||||
monkeypatch.setattr(optimiser_functions.assumptions, "measures_needing_ventilation",
|
||||
["external_wall_insulation", "internal_wall_insulation"])
|
||||
|
||||
# ✅ Patch CostOptimiser.calculate_sap_gain_with_slack so we don't need SAP logic
|
||||
monkeypatch.setattr(CostOptimiser, "calculate_sap_gain_with_slack", staticmethod(lambda x: x))
|
||||
|
||||
# ✅ Patch epc_to_sap_lower_bound for a known SAP target
|
||||
monkeypatch.setattr(optimiser_functions, "epc_to_sap_lower_bound", lambda goal: 69) # EPC C lower bound ~69 SAP
|
||||
|
||||
# ---------------------
|
||||
# RUN THE OPTIMISATION LOOP
|
||||
# ---------------------
|
||||
|
||||
property_measure_types = {rec["type"] for recs in recommendations[p.id] for rec in recs}
|
||||
property_required_measures = [m for m in recommendations[p.id] if m[0]["type"] in body.required_measures]
|
||||
measures_to_optimise = [m for m in recommendations[p.id] if m[0]["type"] not in body.required_measures]
|
||||
|
||||
# ventilation flag
|
||||
needs_ventilation = any(
|
||||
x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation
|
||||
) and not p.has_ventilation
|
||||
|
||||
assert needs_ventilation
|
||||
|
||||
input_measures = optimiser_functions.prepare_input_measures(measures_to_optimise, body.goal, needs_ventilation)
|
||||
|
||||
assert input_measures, "Expected measures to optimise"
|
||||
assert len(input_measures) == 7
|
||||
|
||||
fixed_gain = optimiser_functions.calculate_fixed_gain(
|
||||
property_required_measures, recommendations, p, needs_ventilation
|
||||
)
|
||||
assert fixed_gain == 0, "No required measures should mean fixed gain is 0"
|
||||
|
||||
gain = optimiser_functions.calculate_gain(body=body, p=p, fixed_gain=fixed_gain)
|
||||
|
||||
assert gain == 18.5, "Expected gain to be calculated correctly based on fixed gain and SAP target"
|
||||
|
||||
optimiser = (
|
||||
GainOptimiser(
|
||||
input_measures, max_cost=body.budget, max_gain=gain,
|
||||
allow_slack=body.goal == "Increasing EPC"
|
||||
) if body.budget else CostOptimiser(input_measures, min_gain=gain)
|
||||
)
|
||||
optimiser.setup()
|
||||
optimiser.solve()
|
||||
solution = optimiser.solution
|
||||
|
||||
# ✅ Validate solution makes sense
|
||||
assert solution, "Optimiser should return a non-empty solution"
|
||||
assert all("id" in m for m in solution)
|
||||
assert any("solar_pv" in m["type"] for m in solution), "Expected solar PV to be included"
|
||||
|
||||
# ✅ Collect selected measure IDs
|
||||
selected = {r["id"] for r in solution}
|
||||
|
||||
assert selected == {'8_phase=7', '5_phase=4', '7_phase=6'}
|
||||
|
||||
# ✅ Add required measures (none here)
|
||||
solution = optimiser_functions.add_required_measures(
|
||||
property_id=p.id, property_required_measures=property_required_measures,
|
||||
recommendations=recommendations, selected=selected,
|
||||
)
|
||||
|
||||
assert solution == [
|
||||
{'id': '5_phase=4', 'cost': 58.8, 'gain': 2, 'type': 'low_energy_lighting'},
|
||||
{'id': '7_phase=6', 'cost': 30.0, 'gain': np.float64(3.6), 'type': 'secondary_heating'},
|
||||
{'id': '8_phase=7', 'cost': 6013.139999999999, 'gain': np.float64(13.0), 'type': 'solar_pv'}
|
||||
]
|
||||
|
||||
total_optimised_gain = sum(m["gain"] for m in solution)
|
||||
assert total_optimised_gain == 18.6, "Total gain of optimised measures should meet or exceed target gain"
|
||||
|
||||
selected = optimiser_functions.add_best_practice_measures(p.id, solution, recommendations, selected)
|
||||
|
||||
# ✅ Flatten recommendations for output
|
||||
flattened = optimiser_functions.flatten_recommendations_with_defaults(p.id, recommendations, selected)
|
||||
|
||||
# ---------------------
|
||||
# FINAL ASSERTIONS
|
||||
# ---------------------
|
||||
assert isinstance(flattened, list)
|
||||
assert all("default" in rec for rec in flattened)
|
||||
assert any(rec["default"] for rec in flattened), "Some measures should be marked as default"
|
||||
|
||||
# We don't add ventilation as major insulation work isn't done
|
||||
ventilation_added = any(rec["recommendation_id"] == "3_phase=2" and rec["default"] for rec in flattened)
|
||||
assert not ventilation_added, "Ventilation should not be added without major insulation work"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue