diff --git a/backend/Property.py b/backend/Property.py index 6a84fc09..f196f49b 100644 --- a/backend/Property.py +++ b/backend/Property.py @@ -490,7 +490,7 @@ class Property: for rec_id in rec_ids: sim_epc = self.simulation_epcs[rec_id].copy() rec_impact = [x for x in impact_summary if x["recommendation_id"] == rec_id][0] - # We update all of the features that should have an impact on the kwh model + # We update all features that should have an impact on the kwh model sim_epc.update( { diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py index acd49e05..5525b7a0 100644 --- a/recommendations/Recommendations.py +++ b/recommendations/Recommendations.py @@ -499,8 +499,16 @@ class Recommendations: return predicted_appliances_cost_reduction, predicted_appliances_kwh_reduction @staticmethod - def _check_ventilation_out_of_bounds(sap_impact, ventilation_sap_limit): - return (sap_impact < ventilation_sap_limit) or (sap_impact >= 0) + def _check_ventilation_out_of_bounds(sap_impact: float, ventilation_sap_limit: float) -> bool: + """ + Checks if the SAP impact of a ventilation recommendation is out of bounds, which would indicate that the + recommendation is not appropriate. + :param sap_impact: The SAP impact of the ventilation recommendation, which is typically negative or zero + :param ventilation_sap_limit: The SAP limit for ventilation recommendations, which is typically a negative + number. E.g. -4 + :return: + """ + return (sap_impact < ventilation_sap_limit) or (sap_impact > 0) @staticmethod def _adjust_ventilation_sap(sap_impact, ventilation_sap_limit): @@ -691,7 +699,8 @@ class Recommendations: previous_phase_values: dict, current_phase_values: dict, adjustments: list, - property_instance, + property_instance: Property, + model_predicted_sap: float, ): # For the moment, we cap the number of SAP points that can be achieved by LEDs at 2 if rec["type"] == "low_energy_lighting": @@ -785,7 +794,6 @@ class Recommendations: # Update the current phase values current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"] - elif rec["type"] == "loft_insulation": # When we have a loft insulation recommendation, where there is an extension and the existing # amount of loft insulation is already good, we limit the SAP points @@ -831,6 +839,27 @@ class Recommendations: # Update the current phase values current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"] + elif rec["measure_type"] in ["roomstat_programmer_trvs", "time_temperature_zone_control"]: + # We trim the SAP point recommendations based on the minimum of the predicted and the survey SAP + # points + predicted_difference = model_predicted_sap - previous_phase_values["sap_prediction"] + proposed_impact = property_phase_impact["sap"] + numerically_the_same = np.isclose(proposed_impact, predicted_difference) + + if predicted_difference > 0 and (predicted_difference < proposed_impact) and not numerically_the_same: + # We constrain the impact based on what the model predicts. + # We update the proposed impact to be the predicted difference + adjustments.append( + { + "recommendation_id": rec["recommendation_id"], + "phase": rec["phase"], + # If we've made an adjustment, it will be negative + "sap_adjustment": property_phase_impact["sap"] - predicted_difference, + } + ) + property_phase_impact["sap"] = predicted_difference + # Update the current phase values + current_phase_values["sap"] = previous_phase_values["sap"] + property_phase_impact["sap"] return property_phase_impact, current_phase_values, adjustments @@ -963,7 +992,8 @@ class Recommendations: previous_phase_values=previous_phase_values, current_phase_values=current_phase_values, adjustments=adjustments, - property_instance=property_instance + property_instance=property_instance, + model_predicted_sap=phase_energy_efficiency_metrics["sap_change"], ) # Insert this information into the recommendation. diff --git a/recommendations/SecondaryHeating.py b/recommendations/SecondaryHeating.py index ee7eae1c..ef0fc2d2 100644 --- a/recommendations/SecondaryHeating.py +++ b/recommendations/SecondaryHeating.py @@ -18,6 +18,9 @@ class SecondaryHeating: def recommend(self, phase: int): # Reset self.recommendation = [] + if self.property.epc_record.secondheat_description in ["None", None]: + # No secondary heating system, so no recommendation to remove it + return if self.property.data['number-habitable-rooms'] > self.property.data['number-heated-rooms']: n_rooms = self.property.data['number-habitable-rooms'] - self.property.data['number-heated-rooms'] diff --git a/recommendations/tests/test_recommendations.py b/recommendations/tests/test_recommendations.py index e3bcbb2f..747b0b2e 100644 --- a/recommendations/tests/test_recommendations.py +++ b/recommendations/tests/test_recommendations.py @@ -373,7 +373,7 @@ def test_filter_phase_adjustment(input_data, expected): "sap_impact, limit, expected", [ (1.0, -4, True), # positive SAP not allowed - (0.0, -4, True), # zero not allowed + (0.0, -4, False), # zero is allowed (-1.0, -4, False), # valid range (-3.9, -4, False), # valid range (-4.0, -4, False), # exact lower bound allowed @@ -1476,7 +1476,9 @@ def test_lighting_and_loft_adjustment_combined(property_instance, heat_demand_pr assert adjustments2 == [ {'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)}, - {'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)} + {'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)}, + {'recommendation_id': '5_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0)}, + {'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)} ] @@ -1499,7 +1501,8 @@ def test_mechanical_ventilation_sap_floor(property_instance): previous_phase_values=previous_phase_values, current_phase_values=current_phase_values, adjustments=adjustments, - property_instance=property_instance + property_instance=property_instance, + model_predicted_sap=0 ) ) @@ -1538,7 +1541,8 @@ def test_mechanical_ventilation_no_floor_adjustment(property_instance): previous_phase_values=previous_phase_values, current_phase_values=current_phase_values, adjustments=adjustments, - property_instance=property_instance + property_instance=property_instance, + model_predicted_sap=0 ) ) @@ -1570,7 +1574,8 @@ def test_mechanical_ventilation_exactly_one_no_adjustment(property_instance): previous_phase_values=previous_phase_values, current_phase_values=current_phase_values, adjustments=adjustments, - property_instance=property_instance + property_instance=property_instance, + model_predicted_sap=0 ) ) @@ -1578,3 +1583,182 @@ def test_mechanical_ventilation_exactly_one_no_adjustment(property_instance): assert updated_adjustments == [] assert updated_current["sap"] == 1.0 assert updated_impact["sap"] == -1.0 + + +def test_mechanical_ventilation_sap_zero_no_adjustment(property_instance): + # Test when SAP = 0 + rec = { + "type": "mechanical_ventilation", + "recommendation_id": "mv_test", + "phase": 1, + } + + previous_phase_values = {'phase': 0, 'representative': True, 'recommendation_id': '0_phase=0', + 'measure_type': 'flat_roof_insulation', 'sap': 68.0, 'carbon': np.float64(0.5), + 'heat_demand': np.float64(300.1), 'sap_prediction': np.float64(71.7)} + current_phase_values = {'sap': 68.0, 'carbon': np.float64(0.5), 'heat_demand': np.float64(307.0)} + property_phase_impact = {'sap': 0, 'carbon': 0, 'heat_demand': np.float64(-6.899999999999977)} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec=rec, + property_phase_impact=property_phase_impact, + previous_phase_values=previous_phase_values, + current_phase_values=current_phase_values, + adjustments=adjustments, + property_instance=property_instance, + model_predicted_sap=0 + ) + ) + + # SAP is already at 0 → no adjustment expected + assert updated_adjustments == [] + assert updated_current["sap"] == 68.0 + assert updated_impact["sap"] == 0 + + +def test_mv_valid_negative_no_adjustment(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 70.0} + current = {"sap": 67.0} + impact = {"sap": -3.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert updated_adjustments == [] + assert updated_current["sap"] == 67.0 + assert updated_impact["sap"] == -3.0 + + +def test_mv_zero_impact_allowed(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 68.0, "sap_prediction": 71.7} + current = {"sap": 68.0} + impact = {"sap": 0.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert updated_adjustments == [] + assert updated_current["sap"] == 68.0 + assert updated_impact["sap"] == 0.0 + + +def test_mv_positive_impact_corrected(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 60.0} + current = {"sap": 61.0} + impact = {"sap": 1.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert len(updated_adjustments) == 1 + assert updated_current["sap"] == previous["sap"] + updated_impact["sap"] + assert updated_impact["sap"] <= 0 + + +def test_mv_below_lower_bound_corrected(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 70.0} + current = {"sap": 64.0} + impact = {"sap": -6.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert len(updated_adjustments) == 1 + assert updated_impact["sap"] >= -4 + + +def test_mv_floor_triggered(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 2.0} + current = {"sap": 0.5} + impact = {"sap": -1.5, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert updated_current["sap"] == 1.0 + assert updated_adjustments[0]["sap_adjustment"] > 0 + + +def test_mv_exactly_one_no_floor(property_instance): + rec = {"type": "mechanical_ventilation", "recommendation_id": "mv", "phase": 1} + + previous = {"sap": 2.0} + current = {"sap": 1.0} + impact = {"sap": -1.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert updated_adjustments == [] + assert updated_current["sap"] == 1.0 + + +def test_lighting_no_cap(property_instance): + rec = {"type": "low_energy_lighting", "recommendation_id": "led", "phase": 1, + "co2_equivalent_savings": 0} + + previous = {"sap": 60.0, "carbon": 2.0} + current = {"sap": 61.0, "carbon": 2.0} + impact = {"sap": 1.0, "carbon": 0, "heat_demand": 0} + adjustments = [] + + updated_impact, updated_current, updated_adjustments = ( + Recommendations._apply_measure_specific_rules( + rec, impact, previous, current, adjustments, property_instance, 0 + ) + ) + + assert updated_adjustments == [] + + +def test_filter_phase_adjustments(): + example_adjustments = [ + {'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)}, + {'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)}, + {'recommendation_id': '5_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0)}, + {'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)} + ] + + res = Recommendations._filter_phase_adjustment(example_adjustments) + + assert res == [ + {'recommendation_id': '0_phase=0', 'phase': 0, 'sap_adjustment': np.float64(1.7)}, + {'recommendation_id': '4_phase=2', 'phase': 2, 'sap_adjustment': np.float64(4.0)}, + {'recommendation_id': '6_phase=3', 'phase': 3, 'sap_adjustment': np.float64(1.0000000000000027)} + ]