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Added in the sequential scoring code
This commit is contained in:
Khalim Conn-Kowlessar
parent
925a6c1887
commit
0f60082ba1
4 changed files with 207 additions and 114 deletions
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@ -150,158 +150,195 @@ class Property:
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# self.base_difference_record.df
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def adjust_difference_record_with_recommendations(self, property_recommendations):
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def adjust_difference_record_with_recommendations(
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self, property_recommendations,
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property_representative_recommendations
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):
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"""
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This method will adjust the difference record, based on the recommendations made for the property
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In order to score the measures, we need to consider the phase of the retrofit.
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:param property_recommendations: dictionary of recommendations for the property
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:param property_representative_recommendations: dictionary of representative recommendations for the property
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"""
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self.recommendations_scoring_data = []
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phases = sorted([r[0]["phase"] for r in property_recommendations if r[0]["phase"] is not None])
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for phase in phases:
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property_recommendations_by_phase = [r for r in property_recommendations if r[0]["phase"] == phase][0]
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previous_phases = [p for p in phases if p < phase]
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previous_phase_representatives = [
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r for r in property_representative_recommendations if r["phase"] in previous_phases
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]
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recommendation_record = self.base_difference_record.df.to_dict("records")[0].copy()
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for rec in property_recommendations_by_phase:
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# We simulate the impact of the recommendation at this current phase, and all of the prior phases
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for recommendations_by_type in property_recommendations:
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for i, rec in enumerate(recommendations_by_type):
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recommendation_record = self.base_difference_record.df.to_dict("records")[0].copy()
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scoring_dict = self.create_recommendation_scoring_data(
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property_id=self.id, recommendation_record=recommendation_record, recommendation=rec,
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property_id=self.id,
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recommendation_record=recommendation_record,
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recommendations=previous_phase_representatives + [rec],
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primary_recommendation_id=rec["recommendation_id"]
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)
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self.recommendations_scoring_data.append(scoring_dict)
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@staticmethod
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def create_recommendation_scoring_data(property_id, recommendation_record, recommendation: dict):
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def create_recommendation_scoring_data(
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property_id, recommendation_record, recommendations: list, primary_recommendation_id: int
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):
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"""
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This function will iterate through a list of recommendations and apply a simulation for each recommendation
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This allows us to later multiple measures and see the impact of the measures on the property
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:param property_id: The id of the property
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:param recommendation_record: The record of the property, which will be updated
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:param recommendations: The list of recommendations to apply
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:param primary_recommendation_id: The id of the primary recommendation, which is used to identify the record
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:return: The updated recommendation record
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"""
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output = recommendation_record.copy()
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for col in [
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"walls_insulation_thickness", "floor_insulation_thickness", "roof_insulation_thickness"
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]:
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if recommendation_record[col] is None:
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recommendation_record[col] = "none"
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if output[col] is None:
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output[col] = "none"
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# We update the description to indicate it's insulated
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if recommendation["type"] in ["internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"]:
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# The upgrade made here is to the u-value of the walls and the description of the
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# insulation thickness
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recommendation_record["walls_thermal_transmittance_ending"] = recommendation["new_u_value"]
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recommendation_record["walls_insulation_thickness_ending"] = "above average"
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recommendation_record["walls_energy_eff_ending"] = "Good"
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for recommendation in recommendations:
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# For the list of recommendations we have, we iteratively update the output
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# Note: often when the wall is insulatied, the internal/external insulation is not noted so we should
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# test the impact of using these booleans
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if recommendation["type"] == "external_wall_insulation":
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recommendation_record["external_insulation"] = True
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recommendation_record["internal_insulation"] = False
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# We update the description to indicate it's insulated
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if recommendation["type"] in [
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"internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"
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]:
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# The upgrade made here is to the u-value of the walls and the description of the
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# insulation thickness
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output["walls_thermal_transmittance_ending"] = recommendation["new_u_value"]
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output["walls_insulation_thickness_ending"] = "above average"
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output["walls_energy_eff_ending"] = "Good"
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if recommendation["type"] == "internal_wall_insulation":
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recommendation_record["external_insulation"] = False
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recommendation_record["internal_insulation"] = True
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# Note: often when the wall is insulatied, the internal/external insulation is not noted so we should
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# test the impact of using these booleans
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if recommendation["type"] == "external_wall_insulation":
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output["external_insulation"] = True
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output["internal_insulation"] = False
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else:
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if recommendation_record["walls_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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if recommendation["type"] == "internal_wall_insulation":
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output["external_insulation"] = False
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output["internal_insulation"] = True
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if recommendation_record["walls_insulation_thickness_ending"] is None:
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recommendation_record["walls_insulation_thickness_ending"] = "none"
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# When making a recommendation for the wall, we will also update the ventilation
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if output["mechanical_ventilation_ending"] == 'natural':
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output["mechanical_ventilation_ending"] = 'mechanical, extract only'
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# Update description to indicate it's insulate
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if recommendation["type"] in [
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"solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation"
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]:
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if len(recommendation["parts"]) > 1:
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raise NotImplementedError("Have more than 1 floor insulation part - handle this case")
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else:
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if output["walls_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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# recommendation_record["floor_thermal_transmittance_ending"] = recommendation["new_u_value"]
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# We don't really see above average for this in the training data
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recommendation_record["floor_insulation_thickness_ending"] = "average"
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# This is rarely ever populated in the training data
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# recommendation_record["floor_energy_eff_ending"] = "Good"
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else:
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if recommendation_record["floor_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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if output["walls_insulation_thickness_ending"] is None:
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output["walls_insulation_thickness_ending"] = "none"
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if recommendation_record["floor_insulation_thickness_ending"] is None:
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recommendation_record["floor_insulation_thickness_ending"] = "none"
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# Update description to indicate it's insulate
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if recommendation["type"] in [
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"solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation"
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]:
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if len(recommendation["parts"]) > 1:
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raise NotImplementedError("Have more than 1 floor insulation part - handle this case")
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if recommendation["type"] in ["loft_insulation", "room_roof_insulation", "flat_roof_insulation"]:
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recommendation_record["roof_thermal_transmittance_ending"] = recommendation["new_u_value"]
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# output["floor_thermal_transmittance_ending"] = recommendation["new_u_value"]
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# We don't really see above average for this in the training data
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output["floor_insulation_thickness_ending"] = "average"
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# This is rarely ever populated in the training data
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# output["floor_energy_eff_ending"] = "Good"
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else:
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if output["floor_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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parts = recommendation["parts"]
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if len(parts) != 1:
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raise ValueError("More than one part for roof insulation - investiage me")
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if output["floor_insulation_thickness_ending"] is None:
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output["floor_insulation_thickness_ending"] = "none"
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# This is based on the values we have in the training data
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valid_numeric_values = [
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12, 25, 50, 75, 100, 150, 200, 250, 270, 300, 350, 400
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]
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if recommendation["type"] in ["loft_insulation", "room_roof_insulation", "flat_roof_insulation"]:
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output["roof_thermal_transmittance_ending"] = recommendation["new_u_value"]
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proposed_depth = int(parts[0]["depth"])
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if proposed_depth not in valid_numeric_values:
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# Take the nearest value for scoring
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proposed_depth = min(valid_numeric_values, key=lambda x: abs(x - proposed_depth))
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parts = recommendation["parts"]
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if len(parts) != 1:
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raise ValueError("More than one part for roof insulation - investiage me")
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recommendation_record["roof_insulation_thickness_ending"] = str(proposed_depth)
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if recommendation["type"] == "loft_insulation":
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if proposed_depth >= 270:
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recommendation_record["roof_energy_eff_ending"] = "Very Good"
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# This is based on the values we have in the training data
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valid_numeric_values = [
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12, 25, 50, 75, 100, 150, 200, 250, 270, 300, 350, 400
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]
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proposed_depth = int(parts[0]["depth"])
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if proposed_depth not in valid_numeric_values:
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# Take the nearest value for scoring
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proposed_depth = min(valid_numeric_values, key=lambda x: abs(x - proposed_depth))
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output["roof_insulation_thickness_ending"] = str(proposed_depth)
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if recommendation["type"] == "loft_insulation":
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if proposed_depth >= 270:
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output["roof_energy_eff_ending"] = "Very Good"
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else:
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output["roof_energy_eff_ending"] = "Good"
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else:
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recommendation_record["roof_energy_eff_ending"] = "Good"
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output["roof_energy_eff_ending"] = "Very Good"
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else:
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recommendation_record["roof_energy_eff_ending"] = "Very Good"
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else:
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# Fill missing roof u-values - this fill is not based on recommended upgrades
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if recommendation_record["roof_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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# Fill missing roof u-values - this fill is not based on recommended upgrades
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if output["roof_thermal_transmittance_ending"] is None:
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raise ValueError("We should not have a None value for the u value")
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if recommendation_record["roof_insulation_thickness_ending"] is None:
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recommendation_record["roof_insulation_thickness_ending"] = "none"
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if output["roof_insulation_thickness_ending"] is None:
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output["roof_insulation_thickness_ending"] = "none"
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if recommendation["type"] == "mechanical_ventilation":
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recommendation_record["mechanical_ventilation_ending"] = 'mechanical, extract only'
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if recommendation["type"] == "sealing_open_fireplace":
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output["number_open_fireplaces_ending"] = 0
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if recommendation["type"] == "sealing_open_fireplace":
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recommendation_record["number_open_fireplaces_ending"] = 0
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if recommendation["type"] == "low_energy_lighting":
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output["low_energy_lighting_ending"] = 100
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output["lighting_energy_eff_starting"] = "Very Good"
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if recommendation["type"] == "low_energy_lighting":
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recommendation_record["low_energy_lighting_ending"] = 100
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recommendation_record["lighting_energy_eff_starting"] = "Very Good"
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if recommendation["type"] == "windows_glazing":
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output["multi_glaze_proportion_ending"] = 100
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output["windows_energy_eff_ending"] = "Average"
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if recommendation["type"] == "windows_glazing":
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recommendation_record["multi_glaze_proportion_ending"] = 100
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recommendation_record["windows_energy_eff_ending"] = "Average"
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is_secondary_glazing = recommendation["is_secondary_glazing"]
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is_secondary_glazing = recommendation["is_secondary_glazing"]
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if output["glazing_type_ending"] == "multiple":
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pass
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elif output["glazing_type_ending"] == "single":
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output["glazing_type_ending"] = "secondary" if is_secondary_glazing else "double"
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elif output["glazing_type_ending"] == "double":
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output["glazing_type_ending"] = "multiple" if is_secondary_glazing else "double"
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elif output["glazing_type_ending"] == "secondary":
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output["glazing_type_ending"] = "secondary" if is_secondary_glazing else "multiple"
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elif output["glazing_type_ending"] in ["triple", "high performance"]:
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output["glazing_type_ending"] = "multiple"
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else:
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raise ValueError("Invalid glazing type - implement me")
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if recommendation_record["glazing_type_ending"] == "multiple":
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pass
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elif recommendation_record["glazing_type_ending"] == "single":
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recommendation_record["glazing_type_ending"] = "secondary" if is_secondary_glazing else "double"
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elif recommendation_record["glazing_type_ending"] == "double":
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recommendation_record["glazing_type_ending"] = "multiple" if is_secondary_glazing else "double"
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elif recommendation_record["glazing_type_ending"] == "secondary":
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recommendation_record["glazing_type_ending"] = "secondary" if is_secondary_glazing else "multiple"
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elif recommendation_record["glazing_type_ending"] in ["triple", "high performance"]:
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recommendation_record["glazing_type_ending"] = "multiple"
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else:
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raise ValueError("Invalid glazing type - implement me")
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if is_secondary_glazing:
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output["glazed_type_ending"] = "secondary glazing"
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else:
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output["glazed_type_ending"] = "double glazing installed during or after 2002 "
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if is_secondary_glazing:
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recommendation_record["glazed_type_ending"] = "secondary glazing"
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else:
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recommendation_record["glazed_type_ending"] = "double glazing installed during or after 2002 "
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if recommendation["type"] == "solar_pv":
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output["photo_supply_ending"] = recommendation["photo_supply"]
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if recommendation["type"] == "solar_pv":
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recommendation_record["photo_supply_ending"] = recommendation["photo_supply"]
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if recommendation["type"] not in [
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"mechanical_ventilation", "sealing_open_fireplace", "low_energy_lighting",
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"internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",
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"loft_insulation", "room_roof_insulation", "flat_roof_insulation",
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"solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation",
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"windows_glazing", "solar_pv"
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]:
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raise NotImplementedError("Implement me")
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if recommendation["type"] not in [
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"mechanical_ventilation", "sealing_open_fireplace", "low_energy_lighting",
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"internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",
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"loft_insulation", "room_roof_insulation", "flat_roof_insulation",
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"solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation",
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"windows_glazing", "solar_pv"
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]:
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raise NotImplementedError("Implement me")
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output['id'] = "+".join([str(property_id), str(primary_recommendation_id)])
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recommendation_record['id'] = "+".join([str(property_id), str(recommendation["recommendation_id"])])
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return recommendation_record
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return output
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def get_components(self, cleaned, photo_supply_lookup, floor_area_decile_thresholds):
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"""
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@ -136,22 +136,32 @@ async def trigger_plan(body: PlanTriggerRequest):
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recommendations = {}
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recommendations_scoring_data = []
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representive_recommendations = {}
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for p in input_properties:
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# Property recommendations
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p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
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# TODO: For the private customer, we should probably NOT allow floor insulation, because it often requires
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# decanting the tenant
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recommender = Recommendations(property_instance=p, materials=materials)
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property_recommendations = recommender.recommend()
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property_recommendations, property_representative_recommendations = recommender.recommend()
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if not property_recommendations:
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continue
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recommendations[p.id] = property_recommendations
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representive_recommendations[p.id] = property_representative_recommendations
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p.create_base_difference_epc_record(cleaned_lookup=cleaned)
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p.adjust_difference_record_with_recommendations(property_recommendations)
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p.adjust_difference_record_with_recommendations(
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property_recommendations, property_representative_recommendations
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)
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p.recommendations_scoring_data[0]["id"]
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p.recommendations_scoring_data[0]["walls_thermal_transmittance"]
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p.recommendations_scoring_data[0]["walls_thermal_transmittance_ending"]
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p.recommendations_scoring_data[0]["walls_thermal_transmittance_ending"]
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recommendations_scoring_data.extend(p.recommendations_scoring_data)
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@ -118,6 +118,7 @@ class FloorRecommendations(Definitions):
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if self.property.floor["is_suspended"]:
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# Given the U-value, we recommend underfloor insulation
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self.recommend_floor_insulation(
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phase=phase,
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u_value=u_value,
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insulation_materials=self.suspended_floor_insulation_materials,
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non_insulation_materials=self.suspended_floor_non_insulation_materials
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@ -107,7 +107,52 @@ class Recommendations:
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# We insert temporary ids into the recommendations which is important for the optimiser later
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property_recommendations = self.insert_temp_recommendation_id(property_recommendations)
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return property_recommendations
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# We also need to create the representative recommendations for each recommendation type
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property_representative_recommendations = self.create_representative_recommendations(property_recommendations)
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return property_recommendations, property_representative_recommendations
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@staticmethod
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def create_representative_recommendations(property_recommendations):
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"""
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This method will create a representative recommendation for each recommendation type
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In order to create a representative recommendation, we choose the recommendation that has:
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1) Where a U-value is available, has the best U-value to cost ratio
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2) Where SAP points are available, has the best SAP points to cost ratio
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We don't include mechanical ventilation in the representative recommendations, since we don't attribute a
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SAP impact to this recommendation
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:return:
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"""
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property_representative_recommendations = []
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for recommendations_by_type in property_recommendations:
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if recommendations_by_type[0].get("type") == "mechanical_ventilation":
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continue
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has_u_value = recommendations_by_type[0].get("new_u_value") is not None
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has_sap_points = recommendations_by_type[0].get("sap_points") is not None
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if has_u_value:
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# We sort by the cost per U-value improvement - the lower the better
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recommendations_by_type.sort(
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key=lambda x: x["total"] / x["starting_u_value"] - x["new_u_value"]
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)
|
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elif not has_u_value and has_sap_points:
|
||||
# Sort the options by the cost per SAP point improvement - the lower the better
|
||||
recommendations_by_type.sort(
|
||||
key=lambda x: x["total"] / x["sap_points"]
|
||||
)
|
||||
else:
|
||||
# Sort the options by cost - the lower the better
|
||||
recommendations_by_type.sort(
|
||||
key=lambda x: x["total"]
|
||||
)
|
||||
|
||||
property_representative_recommendations.append(recommendations_by_type[0])
|
||||
|
||||
return property_representative_recommendations
|
||||
|
||||
@staticmethod
|
||||
def insert_temp_recommendation_id(property_recommendations):
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue