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adding further tests for filtering phase adjustments
This commit is contained in:
Khalim Conn-Kowlessar
parent
cda31420e8
commit
40f3c36dbb
4 changed files with 228 additions and 11 deletions
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@ -490,7 +490,7 @@ class Property:
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for rec_id in rec_ids:
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sim_epc = self.simulation_epcs[rec_id].copy()
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rec_impact = [x for x in impact_summary if x["recommendation_id"] == rec_id][0]
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# We update all of the features that should have an impact on the kwh model
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# We update all features that should have an impact on the kwh model
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sim_epc.update(
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{
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@ -499,8 +499,16 @@ class Recommendations:
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return predicted_appliances_cost_reduction, predicted_appliances_kwh_reduction
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@staticmethod
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def _check_ventilation_out_of_bounds(sap_impact, ventilation_sap_limit):
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return (sap_impact < ventilation_sap_limit) or (sap_impact >= 0)
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def _check_ventilation_out_of_bounds(sap_impact: float, ventilation_sap_limit: float) -> bool:
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"""
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Checks if the SAP impact of a ventilation recommendation is out of bounds, which would indicate that the
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recommendation is not appropriate.
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:param sap_impact: The SAP impact of the ventilation recommendation, which is typically negative or zero
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:param ventilation_sap_limit: The SAP limit for ventilation recommendations, which is typically a negative
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number. E.g. -4
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:return:
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"""
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return (sap_impact < ventilation_sap_limit) or (sap_impact > 0)
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@staticmethod
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def _adjust_ventilation_sap(sap_impact, ventilation_sap_limit):
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@ -691,7 +699,8 @@ class Recommendations:
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previous_phase_values: dict,
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current_phase_values: dict,
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adjustments: list,
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property_instance,
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property_instance: Property,
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model_predicted_sap: float,
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):
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# For the moment, we cap the number of SAP points that can be achieved by LEDs at 2
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if rec["type"] == "low_energy_lighting":
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@ -785,7 +794,6 @@ class Recommendations:
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# Update the current phase values
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current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"]
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elif rec["type"] == "loft_insulation":
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# When we have a loft insulation recommendation, where there is an extension and the existing
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# amount of loft insulation is already good, we limit the SAP points
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@ -831,6 +839,27 @@ class Recommendations:
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# Update the current phase values
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current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"]
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elif rec["measure_type"] in ["roomstat_programmer_trvs", "time_temperature_zone_control"]:
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# We trim the SAP point recommendations based on the minimum of the predicted and the survey SAP
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# points
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predicted_difference = model_predicted_sap - previous_phase_values["sap_prediction"]
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proposed_impact = property_phase_impact["sap"]
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numerically_the_same = np.isclose(proposed_impact, predicted_difference)
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if predicted_difference > 0 and (predicted_difference < proposed_impact) and not numerically_the_same:
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# We constrain the impact based on what the model predicts.
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# We update the proposed impact to be the predicted difference
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adjustments.append(
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{
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"recommendation_id": rec["recommendation_id"],
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"phase": rec["phase"],
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# If we've made an adjustment, it will be negative
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"sap_adjustment": property_phase_impact["sap"] - predicted_difference,
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}
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)
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property_phase_impact["sap"] = predicted_difference
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# Update the current phase values
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current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"]
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return property_phase_impact, current_phase_values, adjustments
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@ -963,7 +992,8 @@ class Recommendations:
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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property_instance=property_instance,
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model_predicted_sap=phase_energy_efficiency_metrics["sap_change"],
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)
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# Insert this information into the recommendation.
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@ -18,6 +18,9 @@ class SecondaryHeating:
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def recommend(self, phase: int):
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# Reset
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self.recommendation = []
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if self.property.epc_record.secondheat_description in ["None", None]:
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# No secondary heating system, so no recommendation to remove it
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return
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if self.property.data['number-habitable-rooms'] > self.property.data['number-heated-rooms']:
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n_rooms = self.property.data['number-habitable-rooms'] - self.property.data['number-heated-rooms']
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@ -373,7 +373,7 @@ def test_filter_phase_adjustment(input_data, expected):
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"sap_impact, limit, expected",
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[
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(1.0, -4, True), # positive SAP not allowed
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(0.0, -4, True), # zero not allowed
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(0.0, -4, False), # zero is allowed
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(-1.0, -4, False), # valid range
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(-3.9, -4, False), # valid range
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(-4.0, -4, False), # exact lower bound allowed
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@ -1476,7 +1476,9 @@ def test_lighting_and_loft_adjustment_combined(property_instance, heat_demand_pr
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assert adjustments2 == [
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{'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)},
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{'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)}
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{'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)},
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{'recommendation_id': '5_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0)},
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{'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)}
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]
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@ -1499,7 +1501,8 @@ def test_mechanical_ventilation_sap_floor(property_instance):
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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property_instance=property_instance,
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model_predicted_sap=0
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)
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)
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@ -1538,7 +1541,8 @@ def test_mechanical_ventilation_no_floor_adjustment(property_instance):
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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property_instance=property_instance,
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model_predicted_sap=0
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)
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)
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@ -1570,7 +1574,8 @@ def test_mechanical_ventilation_exactly_one_no_adjustment(property_instance):
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance
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property_instance=property_instance,
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model_predicted_sap=0
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)
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)
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@ -1578,3 +1583,182 @@ def test_mechanical_ventilation_exactly_one_no_adjustment(property_instance):
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assert updated_adjustments == []
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assert updated_current["sap"] == 1.0
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assert updated_impact["sap"] == -1.0
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def test_mechanical_ventilation_sap_zero_no_adjustment(property_instance):
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# Test when SAP = 0
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rec = {
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"type": "mechanical_ventilation",
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"recommendation_id": "mv_test",
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"phase": 1,
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}
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previous_phase_values = {'phase': 0, 'representative': True, 'recommendation_id': '0_phase=0',
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'measure_type': 'flat_roof_insulation', 'sap': 68.0, 'carbon': np.float64(0.5),
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'heat_demand': np.float64(300.1), 'sap_prediction': np.float64(71.7)}
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current_phase_values = {'sap': 68.0, 'carbon': np.float64(0.5), 'heat_demand': np.float64(307.0)}
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property_phase_impact = {'sap': 0, 'carbon': 0, 'heat_demand': np.float64(-6.899999999999977)}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec=rec,
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property_phase_impact=property_phase_impact,
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previous_phase_values=previous_phase_values,
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current_phase_values=current_phase_values,
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adjustments=adjustments,
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property_instance=property_instance,
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model_predicted_sap=0
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)
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)
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# SAP is already at 0 → no adjustment expected
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assert updated_adjustments == []
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assert updated_current["sap"] == 68.0
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assert updated_impact["sap"] == 0
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def test_mv_valid_negative_no_adjustment(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 70.0}
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current = {"sap": 67.0}
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impact = {"sap": -3.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert updated_adjustments == []
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assert updated_current["sap"] == 67.0
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assert updated_impact["sap"] == -3.0
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def test_mv_zero_impact_allowed(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 68.0, "sap_prediction": 71.7}
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current = {"sap": 68.0}
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impact = {"sap": 0.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert updated_adjustments == []
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assert updated_current["sap"] == 68.0
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assert updated_impact["sap"] == 0.0
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def test_mv_positive_impact_corrected(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 60.0}
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current = {"sap": 61.0}
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impact = {"sap": 1.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert len(updated_adjustments) == 1
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assert updated_current["sap"] == previous["sap"] + updated_impact["sap"]
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assert updated_impact["sap"] <= 0
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def test_mv_below_lower_bound_corrected(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 70.0}
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current = {"sap": 64.0}
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impact = {"sap": -6.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert len(updated_adjustments) == 1
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assert updated_impact["sap"] >= -4
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def test_mv_floor_triggered(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 2.0}
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current = {"sap": 0.5}
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impact = {"sap": -1.5, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert updated_current["sap"] == 1.0
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assert updated_adjustments[0]["sap_adjustment"] > 0
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def test_mv_exactly_one_no_floor(property_instance):
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rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1}
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previous = {"sap": 2.0}
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current = {"sap": 1.0}
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impact = {"sap": -1.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert updated_adjustments == []
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assert updated_current["sap"] == 1.0
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def test_lighting_no_cap(property_instance):
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rec = {"type": "low_energy_lighting", "recommendation_id": "led", "phase": 1,
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"co2_equivalent_savings": 0}
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previous = {"sap": 60.0, "carbon": 2.0}
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current = {"sap": 61.0, "carbon": 2.0}
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impact = {"sap": 1.0, "carbon": 0, "heat_demand": 0}
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adjustments = []
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updated_impact, updated_current, updated_adjustments = (
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Recommendations._apply_measure_specific_rules(
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rec, impact, previous, current, adjustments, property_instance, 0
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)
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)
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assert updated_adjustments == []
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def test_filter_phase_adjustments():
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example_adjustments = [
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{'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)},
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{'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)},
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{'recommendation_id': '5_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0)},
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{'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)}
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]
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res = Recommendations._filter_phase_adjustment(example_adjustments)
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assert res == [
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{'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)},
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{'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)},
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{'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)}
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]
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