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fixed heating recommendation tests
This commit is contained in:
Khalim Conn-Kowlessar
parent
fe5e781491
commit
201e8dd829
9 changed files with 94 additions and 730 deletions
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@ -236,10 +236,13 @@ def calculate_gain(
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if body.goal == "Increasing EPC":
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current_sap = int(p.data["current-energy-efficiency"]) + already_installed_gain
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target_sap = (
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eco_packages.get(p.id)[1] if eco_packages.get(p.id)[1] is not None
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else epc_to_sap_lower_bound(body.goal_value)
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)
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if eco_packages is None:
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target_sap = epc_to_sap_lower_bound(body.goal_value)
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else:
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target_sap = (
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eco_packages.get(p.id)[1] if eco_packages.get(p.id)[1] is not None
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else epc_to_sap_lower_bound(body.goal_value)
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)
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if target_sap <= current_sap:
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# We've already met or exceeded the target EPC
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@ -488,10 +488,11 @@ def estimate_perimeter(floor_area, num_rooms):
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return perimeter
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def get_exposed_floor_uvalue(insulation_thickness_str, age_band):
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def get_exposed_floor_uvalue(insulation_thickness_str: None | str, age_band: str) -> float:
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"""
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We implement the methodology as defined in section 5.6 and table S12 of the RdSAP document
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:param insulation_thickness_str:
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:param insulation_thickness_str: Insulation thickness as defined in the EPC data
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:param age_band: Age band of the property
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:return:
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"""
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@ -513,9 +514,15 @@ def get_exposed_floor_uvalue(insulation_thickness_str, age_band):
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else:
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insulation_thickness = int(insulation_thickness_str.replace("mm", ""))
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return s12[s12["age_band"] == age_band][
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filtered = s12[s12["age_band"] == age_band][
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f"insulation_{insulation_thickness}"
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].values[0]
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]
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if filtered.empty:
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# We don't have data so we use the median value
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return float(s12[f"insulation_{insulation_thickness}"].median())
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return float(filtered.values[0])
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def get_floor_u_value(
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@ -223,15 +223,16 @@ testing_examples = [
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'local-authority-label': 'Lewisham', 'constituency-label': 'Lewisham, Deptford', 'posttown': 'LONDON',
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'construction-age-band': 'England and Wales: before 1900', 'lodgement-datetime': '2014-06-26 11:40:50',
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'tenure': 'owner-occupied', 'fixed-lighting-outlets-count': 9.0, 'low-energy-fixed-light-count': 5.0,
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'uprn': 100021936225.0, 'uprn-source': 'Address Matched',
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'uprn': 100021936225, 'uprn-source': 'Address Matched',
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'roomstat_programmer_trvs',
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'time_temperature_zone_control'
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],
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"notes": "Because this property already has a boiler, we don't recommend HHR. We don't recommend an ashp "
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"because the home is mid-terraced. Because the heating controls are "
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"Programmer, no room thermostat, we have a programmer, room thermostat and trvs recommendation"
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"notes": "Because this property already has a boiler, we don't recommend HHR. "
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"Because the heating controls are Programmer, no room thermostat, "
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"we have a programmer, room thermostat and trvs recommendation"
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"for heating controls and for TTZC."
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},
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{
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@ -369,12 +370,13 @@ testing_examples = [
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'uprn-source': 'Address Matched', 'sheating-energy-eff': None, 'sheating-env-eff': None
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'boiler_upgrade',
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'high_heat_retention_storage_heaters',
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'boiler_upgrade'
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],
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"notes": "This property has assumed electric heating and is mid-terrace house. It has a mains gas connection."
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"We can recommend a boiler upgrade and high heat retention storage heaters"
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"We can recommend a boiler upgrade, high heat retention storage heaters, and an ASHP"
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},
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{
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"epc": {
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@ -510,12 +512,12 @@ testing_examples = [
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'boiler_upgrade',
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'boiler_upgrade',
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'high_heat_retention_storage_heaters',
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],
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"notes": "This property has assumed electric heaters. Boiler upgrade, HHR are recommended. We don't recommend"
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"an ASHP off of the bat because it's mid-terrace."
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"notes": "This property has assumed electric heaters. Boiler upgrade, ASHP are recommended. We don't recommend"
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"HHRSH since there is potential community heating"
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},
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{
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"epc": {
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@ -556,6 +558,7 @@ testing_examples = [
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'uprn-source': 'Energy Assessor', 'sheating-energy-eff': None, 'sheating-env-eff': None
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'boiler_upgrade',
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'high_heat_retention_storage_heaters',
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'boiler_upgrade'
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@ -603,12 +606,12 @@ testing_examples = [
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'uprn-source': 'Address Matched', 'sheating-energy-eff': None, 'sheating-env-eff': None
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'boiler_upgrade',
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'boiler_upgrade',
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'high_heat_retention_storage_heaters',
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],
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"notes": "This property already has storage heaters with manual charge control. The home is mid terrace so"
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"the ashp is not suitable"
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"notes": "This property already has storage heaters with manual charge control"
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},
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{
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"epc": {
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@ -1149,6 +1152,7 @@ testing_examples = [
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'uprn-source': 'Energy Assessor', 'sheating-energy-eff': None, 'sheating-env-eff': None
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},
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"heating_measure_types": [
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"air_source_heat_pump",
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'boiler_upgrade',
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'boiler_upgrade',
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'high_heat_retention_storage_heaters'
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@ -1193,10 +1197,9 @@ testing_examples = [
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'uprn': 100070685908, 'uprn-source': 'Address Matched', 'sheating-energy-eff': None,
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'sheating-env-eff': None
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},
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"heating_measure_types": [],
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"notes": "This property is a flat, without mains gas connection. Currently has underfloor electric heating"
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"don't recommend anything. HHRSH isn't recommended as with underfloor heating, it's quite"
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"disruptive"
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"heating_measure_types": ["high_heat_retention_storage_heaters"],
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"notes": "This property is a flat, without mains gas connection. Currently has underfloor electric heating. "
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"In this case we just recommend hhrsh as an additional heating system, which would become the primary"
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},
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{
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"epc": {
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@ -214,7 +214,7 @@ measures_to_optimise = [
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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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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 4}], '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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@ -226,7 +226,7 @@ measures_to_optimise = [
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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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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 4}], '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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@ -238,7 +238,7 @@ measures_to_optimise = [
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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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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.6}], '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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@ -249,7 +249,7 @@ measures_to_optimise = [
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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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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.6}], '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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@ -260,7 +260,7 @@ measures_to_optimise = [
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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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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.2}], '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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@ -271,7 +271,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
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'kwh_savings': np.float64(1650.2708274),
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'energy_cost_savings': np.float64(424.61468389001993)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 3.2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 3.2 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(11.0),
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'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
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@ -282,7 +282,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.53600796473952), 'heat_demand': np.float64(78.3),
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'kwh_savings': np.float64(2310.3791583599996),
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'energy_cost_savings': np.float64(594.4605574460278)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.8}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
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'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
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@ -293,7 +293,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
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'kwh_savings': np.float64(1453.5933906),
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'energy_cost_savings': np.float64(374.00957940138)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.8}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.8 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(9.0),
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'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
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@ -304,7 +304,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.47212713326688), 'heat_demand': np.float64(64.0),
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'kwh_savings': np.float64(2035.03074684),
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'energy_cost_savings': np.float64(523.6134111619319)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
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'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
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@ -315,7 +315,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
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'kwh_savings': np.float64(1255.12594),
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'energy_cost_savings': np.float64(322.94390436199996)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2.4}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.4 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(8.0),
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'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
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@ -326,7 +326,7 @@ measures_to_optimise = [
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'co2_equivalent_savings': np.float64(0.40766490531199995), 'heat_demand': np.float64(54.3),
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'kwh_savings': np.float64(1757.1763159999998),
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'energy_cost_savings': np.float64(452.1214661067999)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
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'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
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@ -336,7 +336,7 @@ measures_to_optimise = [
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'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
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'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
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'kwh_savings': np.float64(1048.341318), 'energy_cost_savings': np.float64(269.7382211214)},
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{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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{'phase': 7, 'parts': [{"type": "solar_pv", "size": 2}], 'type': 'solar_pv', 'measure_type': 'solar_pv',
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'description': 'Install a 2.0 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(7.0),
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'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
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@ -7,6 +7,7 @@ from etl.epc.Record import EPCRecord
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from etl.bill_savings.KwhData import KwhData
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from recommendations.HeatingRecommender import HeatingRecommender
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from recommendations.tests.test_data.heating_recommendations_data import testing_examples
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from recommendations.tests.test_data.materials import materials
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class TestHeatingRecommendations:
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@ -56,6 +57,7 @@ class TestHeatingRecommendations:
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x["has_hot-water-only"] = False
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x["has_mineral_and_wood"] = False
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x["has_dual_fuel_appliance"] = False
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x["has_wood_chips"] = False
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epc_records = {"original_epc": test_case["epc"].copy(), "full_sap_epc": {}, "old_data": []}
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@ -75,6 +77,7 @@ class TestHeatingRecommendations:
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"energy_assessment_is_newer": False
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}
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)
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p.already_installed = []
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# For these tests, this can be fixed
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kwh_predictions = {
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@ -92,7 +95,7 @@ class TestHeatingRecommendations:
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p.set_features(cleaned=cleaned, kwh_client=kwh_client, kwh_predictions=kwh_predictions)
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recommender = HeatingRecommender(property_instance=p)
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recommender = HeatingRecommender(property_instance=p, materials=materials)
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# Check they're empty
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assert not recommender.heating_recommendations
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@ -194,9 +197,9 @@ def test_pick_model_boundaries():
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"""
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assert HeatingRecommender.pick_model((2.0, 4.9), models_kw=(3, 5, 6, 8.5)) == 5
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assert HeatingRecommender.pick_model((5.0, 5.0), models_kw=(3, 5, 6, 8.5)) == 5
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assert HeatingRecommender.pick_model((5.0, 6.1), models_kw=(3, 5, 6, 8.5)) == 6
|
||||
assert HeatingRecommender.pick_model((5.0, 6.1), models_kw=(3, 5, 6, 8.5)) == 8.5
|
||||
assert HeatingRecommender.pick_model((8.6, 9.0), models_kw=(3, 5, 6, 8.5, 11.2)) == 11.2
|
||||
assert HeatingRecommender.pick_model((20, 25), models_kw=(3, 5, 6, 8.5, 11.2)) is None
|
||||
assert HeatingRecommender.pick_model((20, 25), models_kw=(3, 5, 6, 8.5, 11.2)) == 11.2 # largest model
|
||||
|
||||
|
||||
def test_parameter_validation_and_defaults():
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ class TestLightingRecommendations:
|
|||
epc_record.prepared_epc = {"county": "Greater London Authority"}
|
||||
input_property0 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
|
||||
input_property0.lighting = {"low_energy_proportion": 0}
|
||||
input_property0.already_installed = []
|
||||
# Test for invalid materials
|
||||
with pytest.raises(ValueError):
|
||||
LightingRecommendations(input_property0, [])
|
||||
|
|
@ -23,6 +24,7 @@ class TestLightingRecommendations:
|
|||
epc_record.prepared_epc = {"county": "Greater London Authority"}
|
||||
input_property1 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
|
||||
input_property1.lighting = {"low_energy_proportion": 100}
|
||||
input_property1.already_installed = []
|
||||
|
||||
lr = LightingRecommendations(input_property1, materials)
|
||||
lr.recommend()
|
||||
|
|
@ -35,19 +37,16 @@ class TestLightingRecommendations:
|
|||
input_property1 = Property(id=1, postcode="F4k3 6", address="623 fake street", epc_record=epc_record)
|
||||
input_property1.lighting = {"low_energy_proportion": 0.80}
|
||||
input_property1.number_lighting_outlets = 20
|
||||
input_property1.already_installed = []
|
||||
|
||||
lr = LightingRecommendations(input_property1, materials)
|
||||
lr.recommend()
|
||||
assert len(lr.recommendation) == 1
|
||||
|
||||
# Note - this test may be dependent on the ofgem price caps
|
||||
assert lr.recommendation == [
|
||||
{'phase': 0, 'parts': [], 'type': 'low_energy_lighting', 'measure_type': 'low_energy_lighting',
|
||||
'description': 'Install low energy lighting in 4 outlets', 'starting_u_value': None, 'new_u_value': None,
|
||||
'already_installed': False, 'sap_points': 0.4, 'kwh_savings': 219.0,
|
||||
'energy_cost_savings': 56.348699999999994, 'co2_equivalent_savings': 0.035478,
|
||||
'description_simulation': {'lighting-energy-eff': 'Very Good',
|
||||
'lighting-description': 'Low energy lighting in all fixed outlets',
|
||||
'low-energy-lighting': 100}, 'total': 188.76000000000002, 'subtotal': 157.3,
|
||||
'vat': 31.460000000000004, 'contingency': 14.3, 'material': 80.0, 'labour_hours': 3.2, 'labour_days': 0.4,
|
||||
'labour_cost': 63.0, 'survey': False}]
|
||||
assert lr.recommendation[0]["description_simulation"] == {'lighting-energy-eff': 'Very Good',
|
||||
'lighting-description': 'Low energy lighting in all '
|
||||
'fixed outlets',
|
||||
'low-energy-lighting': 100}
|
||||
assert lr.recommendation[0]["description"] == 'Install low energy lighting in 4 outlets'
|
||||
assert lr.recommendation[0]["total"] == 14
|
||||
|
|
|
|||
|
|
@ -108,7 +108,7 @@ class TestCalculateGain:
|
|||
body = SimpleNamespace(goal="Increasing EPC", goal_value="C", simulate_sap_10=False)
|
||||
prop = SimpleNamespace(data={"current-energy-efficiency": "50"})
|
||||
gain = optimiser_functions.calculate_gain(body, prop, fixed_gain=2)
|
||||
assert gain == 18.5
|
||||
assert gain == 17.5
|
||||
|
||||
|
||||
class TestAddRequiredMeasures:
|
||||
|
|
@ -235,7 +235,7 @@ class TestIncreasingEpcE2e:
|
|||
|
||||
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"
|
||||
assert gain == 17.5, "Expected gain to be calculated correctly based on fixed gain and SAP target"
|
||||
|
||||
optimiser = (
|
||||
GainOptimiser(
|
||||
|
|
@ -254,7 +254,8 @@ class TestIncreasingEpcE2e:
|
|||
# Collect selected measure IDs
|
||||
selected = {r["id"] for r in solution}
|
||||
|
||||
assert selected == {'8_phase=7', '5_phase=4', '7_phase=6'}
|
||||
assert selected == {'7_phase=6', '5_phase=4', '10_phase=7'}
|
||||
assert float(optimiser.solution_gain) == 17.6
|
||||
|
||||
# Add required measures (none here)
|
||||
solution = optimiser_functions.add_required_measures(
|
||||
|
|
@ -265,11 +266,11 @@ class TestIncreasingEpcE2e:
|
|||
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'}
|
||||
{'id': '10_phase=7', 'cost': 5826.491999999999, 'gain': np.float64(12.0), '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"
|
||||
assert total_optimised_gain == 17.6, "Total gain of optimised measures should meet or exceed target gain"
|
||||
|
||||
selected = optimiser_functions.add_best_practice_measures(p.id, solution, recommendations, selected)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,52 +1,6 @@
|
|||
from pandas import Timestamp
|
||||
from numpy import nan
|
||||
import datetime
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
from copy import deepcopy
|
||||
|
||||
from recommendations.optimiser import optimiser_functions
|
||||
from recommendations.optimiser.funding_optimiser import optimise_with_funding_paths, build_heat_pump_paths
|
||||
from backend.Funding import Funding
|
||||
from backend.app.plan.schemas import WALL_INSULATION_MEASURES, ROOF_INSULATION_MEASURES, ECO4_ELIGIBILE_FABRIC_MEASURES
|
||||
|
||||
ALLOWED_FABRIC_TYPES = set(WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + ECO4_ELIGIBILE_FABRIC_MEASURES)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_project_scores_matrix():
|
||||
data = []
|
||||
floor_segments = ["0-72", "73-97", "98-199", "200"]
|
||||
bands = [
|
||||
"Low_G", "High_G", "Low_F", "High_F", "Low_E", "High_E", "Low_D", "High_D", "Low_C", "High_C", "Low_B",
|
||||
"High_B", "Low_A", "High_A"
|
||||
]
|
||||
|
||||
cost = 50.0
|
||||
for floor in floor_segments:
|
||||
for start in bands:
|
||||
for finish in bands:
|
||||
if start != finish: # skip identical start/finish (no SAP movement)
|
||||
data.append({
|
||||
"Floor Area Segment": floor,
|
||||
"Starting Band": start,
|
||||
"Finishing Band": finish,
|
||||
"Cost Savings": cost
|
||||
})
|
||||
cost += 5.0 # increment to create variety
|
||||
|
||||
return pd.DataFrame(data)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_partial_scores_matrix():
|
||||
df = pd.read_csv("backend/tests/test_data/ECO4_Partial_Project_Scores_Matrix_v6.csv")
|
||||
df.columns = ['Measure category', 'Measure_Type', 'Pre_Main_Heating_Source',
|
||||
'Post_Main_Heating_Source', 'Total Floor Area Band', 'Starting Band',
|
||||
'Average Treatable Factor', 'Cost Savings', 'SAP Savings']
|
||||
return df
|
||||
from recommendations.optimiser.funding_optimiser import build_heat_pump_paths
|
||||
|
||||
|
||||
class DummyProp:
|
||||
|
|
@ -105,619 +59,6 @@ def p():
|
|||
return DummyProp()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def funding(monkeypatch, mock_partial_scores_matrix, mock_project_scores_matrix):
|
||||
"""Simple Funding that returns zero uplift so costs stay as provided."""
|
||||
# Build the Funding with tiny in-memory frames (avoid test I/O)
|
||||
|
||||
f = Funding(
|
||||
project_scores_matrix=mock_project_scores_matrix,
|
||||
partial_project_scores_matrix=mock_partial_scores_matrix,
|
||||
whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
|
||||
eco4_social_cavity_abs_rate=13.5, eco4_social_solid_abs_rate=17,
|
||||
eco4_private_cavity_abs_rate=13.5, eco4_private_solid_abs_rate=17,
|
||||
gbis_social_cavity_abs_rate=21, gbis_social_solid_abs_rate=25,
|
||||
gbis_private_cavity_abs_rate=22, gbis_private_solid_abs_rate=28,
|
||||
tenure="Social"
|
||||
)
|
||||
|
||||
# Keep innovation_uplift simple for the first test
|
||||
# monkeypatch.setattr(f, "get_innovation_uplift", lambda *args, **kwargs: 0.0)
|
||||
|
||||
# If your solar precondition matters, you can force True/False here:
|
||||
# monkeypatch.setattr(
|
||||
# __import__("backend").Funding, "check_solar_eligible_heating_system",
|
||||
# staticmethod(lambda mainheat_description, heating_control_description: False)
|
||||
# )
|
||||
|
||||
return f
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def property_recommendations():
|
||||
"""Short sample; replace with your full block if you want."""
|
||||
recs = [
|
||||
[{'phase': 0, 'parts': [{'id': 2466, 'type': 'external_wall_insulation',
|
||||
'description': 'EWI Pro EPS external wall insulation system with '
|
||||
'Brick Slip finish',
|
||||
'depth': 150.0, 'depth_unit': 'mm', 'cost': None,
|
||||
'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.02631579,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt',
|
||||
'thermal_conductivity': 0.038,
|
||||
'thermal_conductivity_unit': 'watt_per_meter_kelvin',
|
||||
'link': 'SCIS',
|
||||
'created_at': Timestamp('2025-03-16 15:26:22.379496'),
|
||||
'is_active': True, 'prime_material_cost': None,
|
||||
'material_cost': 0.0, 'labour_cost': 0.0,
|
||||
'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
|
||||
'total_cost': 298.35,
|
||||
'notes': 'This is the quoted value from SCIS',
|
||||
'is_installer_quote': True, 'quantity': 63.98796761892035,
|
||||
'quantity_unit': 'm2', 'total': 19090.810139104888,
|
||||
'labour_hours': 0.0, 'labour_days': 0.0}],
|
||||
'type': 'external_wall_insulation', 'measure_type': 'external_wall_insulation',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install 150mm EWI Pro EPS external wall insulation system with Brick '
|
||||
'Slip finish on external walls',
|
||||
'starting_u_value': 1.7, 'new_u_value': 0.32, 'already_installed': False,
|
||||
'sap_points': np.float64(9.6),
|
||||
'simulation_config': {'is_as_built_ending': False, 'walls_is_assumed_ending': False,
|
||||
'walls_insulation_thickness_ending': 'average',
|
||||
'external_insulation_ending': True,
|
||||
'walls_energy_eff_ending': 'Good',
|
||||
'walls_thermal_transmittance_ending': 0.23},
|
||||
'description_simulation': {'walls-description': 'Solid brick, with external insulation',
|
||||
'walls-energy-eff': 'Good'}, 'total': 19090.810139104888,
|
||||
'labour_hours': 0.0, 'labour_days': 0.0, 'survey': False,
|
||||
'recommendation_id': '0_phase=0', 'efficiency': 11229.568317120522,
|
||||
'co2_equivalent_savings': np.float64(0.5), 'heat_demand': np.float64(37.099999999999994),
|
||||
'kwh_savings': np.float64(1827.8999999999996),
|
||||
'energy_cost_savings': np.float64(136.1247882352941)}, {'phase': 0, 'parts': [
|
||||
{'id': 2373, 'type': 'internal_wall_insulation', 'description': 'SWIP EcoBatt & Plastered finish',
|
||||
'depth': 95.0,
|
||||
'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.03125,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.032,
|
||||
'thermal_conductivity_unit': None,
|
||||
'link': 'SCIS', 'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True,
|
||||
'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 2.1,
|
||||
'plant_cost': 0.0, 'total_cost': 89.0, 'notes': None, 'is_installer_quote': True,
|
||||
'quantity': 63.98796761892035,
|
||||
'quantity_unit': 'm2', 'total': 5694.929118083911, 'labour_hours': 134.37473199973275,
|
||||
'labour_days': 4.199210374991648}], 'type': 'internal_wall_insulation',
|
||||
'measure_type': 'internal_wall_insulation',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install 95mm '
|
||||
'SWIP EcoBatt & '
|
||||
'Plastered '
|
||||
'finish on '
|
||||
'internal walls',
|
||||
'starting_u_value': 1.7,
|
||||
'new_u_value': 0.32,
|
||||
'already_installed': False,
|
||||
'sap_points': 6,
|
||||
'simulation_config': {
|
||||
'is_as_built_ending': False,
|
||||
'walls_is_assumed_ending':
|
||||
False,
|
||||
'walls_insulation_thickness_ending': 'average',
|
||||
'internal_insulation_ending': True,
|
||||
'walls_energy_eff_ending':
|
||||
'Good',
|
||||
'walls_thermal_transmittance_ending': 0.29},
|
||||
'description_simulation': {
|
||||
'walls-description': 'Solid '
|
||||
'brick, with internal '
|
||||
'insulation',
|
||||
'walls-energy-eff': 'Good'},
|
||||
'total': 5694.929118083911,
|
||||
'labour_hours': 134.37473199973275,
|
||||
'labour_days': 4.199210374991648,
|
||||
'survey': True,
|
||||
'recommendation_id': '1_phase=0',
|
||||
'efficiency': 3349.6383047552417,
|
||||
'co2_equivalent_savings': np.float64(
|
||||
0.5),
|
||||
'heat_demand': np.float64(
|
||||
35.30000000000001),
|
||||
'kwh_savings': np.float64(
|
||||
1432.3999999999996),
|
||||
'energy_cost_savings': np.float64(
|
||||
106.67167058823532)}], [
|
||||
{'phase': 1, 'parts': [{'id': 2351, 'type': 'loft_insulation',
|
||||
'description': 'Knauf Loft Roll 44 glass fibre roll',
|
||||
'depth': 300.0, 'depth_unit': 'mm', 'cost': None,
|
||||
'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt',
|
||||
'thermal_conductivity': 0.044,
|
||||
'thermal_conductivity_unit': 'watt_per_meter_kelvin',
|
||||
'link': 'SCIS',
|
||||
'created_at': Timestamp('2025-03-16 15:26:22.379496'),
|
||||
'is_active': True, 'prime_material_cost': None,
|
||||
'material_cost': 0.0, 'labour_cost': 0.0,
|
||||
'labour_hours_per_unit': 0.11, 'plant_cost': 0.0,
|
||||
'total_cost': 15.0,
|
||||
'notes': 'This is the cost if there is less than 100mm '
|
||||
'existing insulation',
|
||||
'is_installer_quote': True, 'quantity': 63.98796761892035,
|
||||
'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8,
|
||||
'labour_days': 1}], 'type': 'loft_insulation',
|
||||
'measure_type': 'loft_insulation',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install 300mm of Knauf Loft Roll 44 glass fibre roll in your loft',
|
||||
'starting_u_value': 2.3, 'new_u_value': 2.3, 'sap_points': np.float64(2.4),
|
||||
'already_installed': False,
|
||||
'simulation_config': {'is_loft_ending': True, 'roof_is_assumed_ending': False,
|
||||
'roof_insulation_thickness_ending': '300',
|
||||
'roof_thermal_transmittance_ending': 2.3,
|
||||
'roof_energy_eff_ending': 'Very Good'},
|
||||
'description_simulation': {'roof-description': 'Pitched, 300mm loft insulation',
|
||||
'roof-energy-eff': 'Very Good'}, 'total': 645.0,
|
||||
'labour_hours': 8, 'labour_days': 1, 'survey': False, 'recommendation_id': '2_phase=1',
|
||||
'efficiency': 278.1347826086957,
|
||||
'co2_equivalent_savings': np.float64(0.10000000000000009),
|
||||
'heat_demand': np.float64(1.5), 'kwh_savings': np.float64(566.1499999999996),
|
||||
'energy_cost_savings': np.float64(42.16152352941185)}], [{'phase': 2, 'parts': [
|
||||
{'id': 2329, 'type': 'mechanical_ventilation', 'description': 'Mechanical Extract Ventilation',
|
||||
'depth': 0.0,
|
||||
'depth_unit': None, 'cost': None, 'cost_unit': 'gbp_per_unit', 'r_value_per_mm': nan,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': None,
|
||||
'thermal_conductivity_unit': None,
|
||||
'link': 'SCIS', 'created_at': datetime.datetime(2025, 3, 16, 15, 26, 22, 379496), 'is_active': True,
|
||||
'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0,
|
||||
'plant_cost': 0.0, 'total_cost': 350.0, 'notes': None, 'is_installer_quote': True, 'total': 700.0,
|
||||
'quantity': 2,
|
||||
'quantity_unit': 'part'}], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install 2 '
|
||||
'Mechanical '
|
||||
'Extract '
|
||||
'Ventilation units',
|
||||
'starting_u_value': None,
|
||||
'new_u_value': None,
|
||||
'already_installed': False,
|
||||
'sap_points': np.float64(
|
||||
-0.10000000000000142),
|
||||
'heat_demand': np.float64(
|
||||
-3.3999999999999773),
|
||||
'kwh_savings': np.float64(
|
||||
-53.80000000000018),
|
||||
'co2_equivalent_savings': np.float64(
|
||||
0.0),
|
||||
'energy_cost_savings': np.float64(
|
||||
-4.0065176470588995),
|
||||
'total': 700.0,
|
||||
'labour_hours': 8,
|
||||
'labour_days': 1.0,
|
||||
'simulation_config': {
|
||||
'mechanical_ventilation_ending':
|
||||
'mechanical, '
|
||||
'extract '
|
||||
'only'},
|
||||
'description_simulation': {
|
||||
'mechanical-ventilation': 'mechanical, '
|
||||
'extract only'},
|
||||
'recommendation_id': '3_phase=2',
|
||||
'efficiency': 0}], [
|
||||
{'phase': 3, 'parts': [{'id': 2409, 'type': 'suspended_floor_insulation',
|
||||
'description': 'Q-bot underfloor insulation', 'depth': 75.0,
|
||||
'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2',
|
||||
'r_value_per_mm': 0.045454547,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt',
|
||||
'thermal_conductivity': 0.022,
|
||||
'thermal_conductivity_unit': 'watt_per_meter_kelvin',
|
||||
'link': 'SCIS',
|
||||
'created_at': Timestamp('2025-03-16 15:26:22.379496'),
|
||||
'is_active': True, 'prime_material_cost': None,
|
||||
'material_cost': 0.0, 'labour_cost': 0.0,
|
||||
'labour_hours_per_unit': 1.63, 'plant_cost': 0.0,
|
||||
'total_cost': 93.75,
|
||||
'notes': 'Linearly interpolated based on Qbot costs',
|
||||
'is_installer_quote': True, 'quantity': 43.0,
|
||||
'quantity_unit': 'm2', 'total': 4031.25,
|
||||
'labour_hours': 70.08999999999999,
|
||||
'labour_days': 2.920416666666666}],
|
||||
'type': 'suspended_floor_insulation', 'measure_type': 'suspended_floor_insulation',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install 75mm Q-bot underfloor insulation insulation in suspended '
|
||||
'floor',
|
||||
'starting_u_value': 0.83, 'new_u_value': 0.22, 'sap_points': 2, 'survey': True,
|
||||
'already_installed': False, 'simulation_config': {'floor_is_assumed_ending': False,
|
||||
'floor_insulation_thickness_ending': 'average',
|
||||
'floor_thermal_transmittance_ending': 0.685593},
|
||||
'description_simulation': {'floor-description': 'Suspended, insulated'},
|
||||
'total': 4031.25, 'labour_hours': 70.08999999999999, 'labour_days': 2.920416666666666,
|
||||
'recommendation_id': '4_phase=3', 'efficiency': 4856.707710843373,
|
||||
'co2_equivalent_savings': np.float64(0.20000000000000018),
|
||||
'heat_demand': np.float64(33.5), 'kwh_savings': np.float64(1021.1999999999998),
|
||||
'energy_cost_savings': np.float64(76.04936470588231)}], [
|
||||
{'phase': 4, 'parts': [], 'type': 'low_energy_lighting',
|
||||
'measure_type': 'low_energy_lighting',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install low energy lighting in -886 outlets', 'starting_u_value': None,
|
||||
'new_u_value': None, 'already_installed': False, 'sap_points': 2,
|
||||
'kwh_savings': -48508.5, 'energy_cost_savings': -12481.237049999998,
|
||||
'co2_equivalent_savings': -7.858377,
|
||||
'description_simulation': {'lighting-energy-eff': 'Very Good',
|
||||
'lighting-description': 'Low energy lighting in all fixed'
|
||||
' outlets',
|
||||
'low-energy-lighting': 100}, 'total': -3411.1000000000004,
|
||||
'labour_hours': 1, 'labour_days': 0.125, 'survey': True,
|
||||
'recommendation_id': '5_phase=4', 'efficiency': -1705.5500000000002,
|
||||
'heat_demand': np.float64(5.099999999999994)}], [
|
||||
{'type': 'heating', 'phase': 5, 'measure_type': 'time_temperature_zone_control',
|
||||
"innovation_rate": 0,
|
||||
'parts': [],
|
||||
'description': 'Upgrade heating controls to Smart Thermostats, room sensors and '
|
||||
'smart radiator valves (time & temperature zone control)',
|
||||
'total': 739.576, 'subtotal': 700.48, 'vat': 39.096000000000004,
|
||||
'labour_hours': 3.6199999999999997, 'labour_days': np.float64(1.0),
|
||||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(2.9),
|
||||
'already_installed': False, 'simulation_config': {
|
||||
'thermostatic_control_ending': 'time and temperature zone control',
|
||||
'switch_system_ending': None, 'trvs_ending': None,
|
||||
'mainheatc_energy_eff_ending': 'Very Good'}, 'description_simulation': {
|
||||
'mainheatcont-description': 'Time and temperature zone control',
|
||||
'mainheatc-energy-eff': 'Very Good'}, 'recommendation_id': '6_phase=5',
|
||||
'efficiency': 739.576, 'co2_equivalent_savings': np.float64(0.30000000000000027),
|
||||
'heat_demand': np.float64(6.599999999999994),
|
||||
'kwh_savings': np.float64(876.8000000000002),
|
||||
'energy_cost_savings': np.float64(65.29581176470589)}], [
|
||||
{'phase': 6, 'parts': [], 'type': 'secondary_heating',
|
||||
'measure_type': 'secondary_heating',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Remove the secondary heating system', 'starting_u_value': None,
|
||||
'new_u_value': None, 'sap_points': np.float64(3.6), 'already_installed': False,
|
||||
'total': 30.0, 'subtotal': 25.0, 'vat': 5.0, 'labour_hours': 3.0,
|
||||
'labour_days': np.float64(1.0),
|
||||
'simulation_config': {'secondheat_description_ending': 'None'},
|
||||
'description_simulation': {'secondheat-description': 'None'},
|
||||
'recommendation_id': '7_phase=6', 'efficiency': 30.0,
|
||||
'co2_equivalent_savings': np.float64(0.10000000000000009),
|
||||
'heat_demand': np.float64(15.400000000000006),
|
||||
'kwh_savings': np.float64(196.29999999999927),
|
||||
'energy_cost_savings': np.float64(14.61857647058821)}], [
|
||||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
|
||||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
|
||||
'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
|
||||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
|
||||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
|
||||
'description_simulation': {'photo-supply': np.float64(65.0)},
|
||||
'recommendation_id': '8_phase=7', 'efficiency': np.float64(462.54923076923075),
|
||||
'co2_equivalent_savings': np.float64(0.47347873833399995),
|
||||
'heat_demand': np.float64(88.69999999999999),
|
||||
'kwh_savings': np.float64(2040.8566307499998),
|
||||
'energy_cost_savings': np.float64(525.1124110919749)},
|
||||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
|
||||
'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
|
||||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
|
||||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
|
||||
'description_simulation': {'photo-supply': np.float64(65.0)},
|
||||
'recommendation_id': '9_phase=7', 'efficiency': np.float64(810.5390769230769),
|
||||
'co2_equivalent_savings': np.float64(0.6628702336675999),
|
||||
'heat_demand': np.float64(88.69999999999999),
|
||||
'kwh_savings': np.float64(2857.1992830499994),
|
||||
'energy_cost_savings': np.float64(735.1573755287648)},
|
||||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
|
||||
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
|
||||
'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
|
||||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
||||
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
||||
'description_simulation': {'photo-supply': np.float64(60.0)},
|
||||
'recommendation_id': '10_phase=7', 'efficiency': np.float64(485.54099999999994),
|
||||
'co2_equivalent_savings': np.float64(0.42834948104),
|
||||
'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
|
||||
'energy_cost_savings': np.float64(475.0617304809999)},
|
||||
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||||
"innovation_rate": 0,
|
||||
'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
|
||||
'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,
|
||||
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
||||
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
||||
'description_simulation': {'photo-supply': np.float64(60.0)},
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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',
|
||||
"innovation_rate": 0,
|
||||
'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)}]
|
||||
]
|
||||
return recs
|
||||
|
||||
|
||||
def _attach_costs_and_uplifts(recs, funding, p):
|
||||
"""Mimic what your script did: add cost fields & innovation uplift."""
|
||||
out = deepcopy(recs)
|
||||
for group in out:
|
||||
for r in group:
|
||||
if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
|
||||
(
|
||||
r["partial_project_score"],
|
||||
r["partial_project_funding"],
|
||||
r["innovation_uplift"],
|
||||
r["uplift_project_score"],
|
||||
) = (
|
||||
0, 0, 0, 0
|
||||
)
|
||||
continue
|
||||
|
||||
(
|
||||
r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
|
||||
r["uplift_project_score"]
|
||||
) = funding.get_innovation_uplift(
|
||||
measure=r,
|
||||
starting_sap=55,
|
||||
floor_area=70.0,
|
||||
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="Very Good",
|
||||
)
|
||||
# the optimiser_functions.prepare_input_measures will translate these to input format; but
|
||||
# for safety add explicit cost fields some downstream code expects:
|
||||
r["total"] = float(r["total"])
|
||||
return out
|
||||
|
||||
|
||||
def _to_input_measures(recs, p):
|
||||
"""Use your own helper so we test the full pipeline."""
|
||||
property_measure_types = {rec["type"] for grp in recs for rec in grp}
|
||||
needs_ventilation = any(
|
||||
x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation
|
||||
) and not getattr(p, "has_ventilation", False)
|
||||
|
||||
# goal="Increasing EPC", add_uplift=True for Social path
|
||||
return optimiser_functions.prepare_input_measures(
|
||||
recs, goal="Increasing EPC", needs_ventilation=needs_ventilation, funding=True
|
||||
)
|
||||
|
||||
|
||||
def _types_of(picked_items):
|
||||
return {item["type"] for item in picked_items}
|
||||
|
||||
|
||||
def test_social_fabric_only_returns_only_fabric_types(p, funding, property_recommendations, monkeypatch):
|
||||
# 1) prepare data like your script
|
||||
recs = _attach_costs_and_uplifts(property_recommendations, funding, p)
|
||||
input_measures = _to_input_measures(recs, p)
|
||||
|
||||
# 2) run optimiser wrapper (budget and target_gain can be modest for the test)
|
||||
budget = 30000.0
|
||||
target_gain = 8.0
|
||||
|
||||
solutions = optimise_with_funding_paths(
|
||||
p=p,
|
||||
input_measures=input_measures,
|
||||
housing_type="Social",
|
||||
budget=budget,
|
||||
target_gain=target_gain,
|
||||
funding=funding
|
||||
)
|
||||
|
||||
# 3) basic shape assertions
|
||||
assert isinstance(solutions, pd.DataFrame)
|
||||
assert not solutions.empty
|
||||
|
||||
# 4) find the fabric-only ECO4 row
|
||||
fabric_rows = solutions[
|
||||
solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "fabric-only:eco4")]
|
||||
assert not fabric_rows.empty, "Expected a fabric-only:eco4 solution for Social tenure"
|
||||
|
||||
# 5) ensure only fabric measure types are present in that solution
|
||||
picked_types = _types_of(fabric_rows.iloc[0]["items"])
|
||||
assert picked_types == {'internal_wall_insulation+mechanical_ventilation',
|
||||
'suspended_floor_insulation'}, "incorrect types selected"
|
||||
|
||||
# 6) respect budget
|
||||
assert fabric_rows.iloc[0]["total_cost"] <= budget + 1e-9
|
||||
|
||||
# (optional) ensure unfunded baseline also appears
|
||||
unfunded_rows = solutions[
|
||||
solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "unfunded:all")]
|
||||
assert not unfunded_rows.empty
|
||||
|
||||
|
||||
def test_private_solid_wall_no_innovation_epc_d(p, funding, mock_project_scores_matrix, mock_partial_scores_matrix):
|
||||
"""
|
||||
We have a specific test for this case which was implemented incorrectly originally.
|
||||
This is an EPC D property and so shouldn't be eligible for ECO4. Instead, only GBIS should be considered.
|
||||
"""
|
||||
|
||||
# Overwrite the data - copied from real example
|
||||
p2 = deepcopy(p)
|
||||
p2.data = {
|
||||
"current-energy-rating": "D",
|
||||
"current-energy-efficiency": 68,
|
||||
"mainheat-energy-eff": "Good",
|
||||
}
|
||||
p2.walls = {'original_description': 'Sandstone or limestone, as built, no insulation (assumed)',
|
||||
'clean_description': 'Sandstone or limestone, as built, no insulation', 'thermal_transmittance': None,
|
||||
'thermal_transmittance_unit': None, 'is_cavity_wall': False, 'is_filled_cavity': False,
|
||||
'is_solid_brick': False, 'is_system_built': False, 'is_timber_frame': False,
|
||||
'is_granite_or_whinstone': False, 'is_as_built': True, 'is_cob': False, 'is_assumed': True,
|
||||
'is_sandstone_or_limestone': True, 'is_park_home': False, 'insulation_thickness': 'none',
|
||||
'external_insulation': False, 'internal_insulation': False}
|
||||
|
||||
funding2 = Funding(
|
||||
tenure="Private",
|
||||
project_scores_matrix=mock_project_scores_matrix,
|
||||
partial_project_scores_matrix=mock_partial_scores_matrix,
|
||||
whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
|
||||
eco4_social_cavity_abs_rate=12.5,
|
||||
eco4_social_solid_abs_rate=17,
|
||||
eco4_private_cavity_abs_rate=12.5,
|
||||
eco4_private_solid_abs_rate=17,
|
||||
gbis_social_cavity_abs_rate=21,
|
||||
gbis_social_solid_abs_rate=25,
|
||||
gbis_private_cavity_abs_rate=21,
|
||||
gbis_private_solid_abs_rate=28,
|
||||
)
|
||||
|
||||
input_measures = [
|
||||
[{'id': '0_phase=0', 'cost': np.float64(4441.202499013676), 'gain': np.float64(3.4000000000000057),
|
||||
'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
|
||||
'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
|
||||
'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
|
||||
'uplift_project_score': np.float64(0.0)}], [
|
||||
{'id': '2_phase=2', 'cost': np.float64(2280.0), 'gain': np.float64(0.4), 'type': 'secondary_glazing',
|
||||
'innovation_uplift': np.float64(0.0), 'cost_minus_uplift': np.float64(2280.0),
|
||||
'raw_cost': np.float64(2280.0), 'partial_project_funding': np.float64(1421.1999999999998),
|
||||
'partial_project_score': np.float64(83.6), 'uplift_project_score': np.float64(0.0)}], [
|
||||
{'id': '3_phase=3', 'cost': np.float64(604.5840000000001), 'gain': np.float64(1.2),
|
||||
'type': 'time_temperature_zone_control', 'innovation_uplift': np.float64(0.0),
|
||||
'cost_minus_uplift': np.float64(604.5840000000001), 'raw_cost': 604.5840000000001,
|
||||
'partial_project_funding': np.float64(702.0999999999999), 'partial_project_score': np.float64(41.3),
|
||||
'uplift_project_score': np.float64(0.0)}], [
|
||||
{'id': '4_phase=4', 'cost': 60.0, 'gain': np.float64(0.0), 'type': 'secondary_heating',
|
||||
'innovation_uplift': 0, 'cost_minus_uplift': 60.0, 'raw_cost': 60.0, 'partial_project_funding': 0,
|
||||
'partial_project_score': 0, 'uplift_project_score': 0}]
|
||||
]
|
||||
|
||||
solutions = optimise_with_funding_paths(
|
||||
p=p2,
|
||||
input_measures=input_measures,
|
||||
housing_type="Private",
|
||||
budget=None,
|
||||
target_gain=1.5,
|
||||
funding=funding2
|
||||
)
|
||||
|
||||
# 3) basic shape assertions
|
||||
assert isinstance(solutions, pd.DataFrame)
|
||||
assert not solutions.empty
|
||||
|
||||
# We should have 2 rows
|
||||
assert solutions.shape[0] == 2
|
||||
|
||||
# We should only have None or GBIS
|
||||
assert set(solutions["scheme"].unique()) == {"none", "gbis"}
|
||||
|
||||
meets_upgrade_gbis = solutions[solutions["meets_upgrade_target"] & solutions["is_eligible"]]
|
||||
assert meets_upgrade_gbis.shape[0] == 1
|
||||
|
||||
# Check exact result
|
||||
assert meets_upgrade_gbis.squeeze().to_dict() == {
|
||||
'fixed_ids': ['0_phase=0'], 'items': [
|
||||
{'id': '0_phase=0', 'cost': 3881.2024990136756, 'gain': np.float64(3.4000000000000057),
|
||||
'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
|
||||
'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
|
||||
'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
|
||||
'uplift_project_score': np.float64(0.0)}], 'total_cost': 3881.2024990136756,
|
||||
'total_gain': 3.4000000000000057, 'path': [{'AND': ['internal_wall_insulation+mechanical_ventilation'],
|
||||
'reference':
|
||||
'internal_wall_insulation+mechanical_ventilation:gbis'}],
|
||||
'scheme': 'gbis', 'is_eligible': True, 'unfunded_items': [], 'meets_upgrade_target': True, 'starting_sap': 68,
|
||||
'floor_area': 70.0, 'ending_sap': 71.4, 'starting_band': 'High_D', 'ending_band': 'Low_C',
|
||||
'floor_area_band': '0-72', 'project_score': 540.0, 'full_project_funding': 0.0,
|
||||
'partial_project_funding': 2300.1000000000004, 'partial_project_score': 135.3, 'total_uplift': 0.0,
|
||||
'total_uplift_score': 0.0
|
||||
}
|
||||
|
||||
|
||||
def test_build_heat_pump_paths():
|
||||
eg1 = build_heat_pump_paths([], ["loft_insulation"])
|
||||
|
||||
|
|
|
|||
|
|
@ -279,27 +279,34 @@ class TestRecommendationUtils:
|
|||
|
||||
# Test with wall_type not in default_wall_thickness
|
||||
def test_wall_type_not_in_default_wall_thickness(self):
|
||||
with pytest.raises(IndexError):
|
||||
recommendation_utils.get_floor_u_value(
|
||||
floor_type="solid",
|
||||
area=100,
|
||||
perimeter=40,
|
||||
age_band="A",
|
||||
wall_type="InvalidWallType",
|
||||
insulation_thickness=None,
|
||||
)
|
||||
# THis previously raised an error but because it largely dicates the thickness, often defaulted to
|
||||
# 300, we just use the default instead of raising an error. We see cases of this in the wild, where we
|
||||
# estimate EPCs and end up with unusual wall types, so we have fallbacks in place
|
||||
assert recommendation_utils.get_floor_u_value(
|
||||
floor_type="solid",
|
||||
area=100,
|
||||
perimeter=40,
|
||||
age_band="A",
|
||||
wall_type="InvalidWallType",
|
||||
insulation_thickness=None,
|
||||
) == 0.6
|
||||
|
||||
# Test with age_band not in s11
|
||||
def test_age_band_not_in_s11(self):
|
||||
with pytest.raises(IndexError):
|
||||
recommendation_utils.get_floor_u_value(
|
||||
floor_type="solid",
|
||||
area=100,
|
||||
perimeter=40,
|
||||
age_band="Z",
|
||||
wall_type="Cavity",
|
||||
insulation_thickness=None,
|
||||
)
|
||||
# This previously raised an error but because it largely dicates the thickness, often defaulted to
|
||||
# 300, we just use the default instead of raising an error. We see cases of this in the wild, where we
|
||||
# might estimate an EPC
|
||||
recommendation_utils.get_floor_u_value(
|
||||
floor_type="solid",
|
||||
area=100,
|
||||
perimeter=40,
|
||||
age_band="Z",
|
||||
wall_type="Cavity",
|
||||
insulation_thickness=None,
|
||||
)
|
||||
|
||||
def test_age_band_not_in_s11_exposed_floor(self):
|
||||
recommendation_utils.get_exposed_floor_uvalue(None, "BadValue")
|
||||
|
||||
def test_convert_thickness_to_numeric(self):
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue