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updating the simulation epcs
This commit is contained in:
Khalim Conn-Kowlessar
parent
25c07fdc52
commit
9a62184ab5
3 changed files with 52 additions and 59 deletions
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@ -460,8 +460,8 @@ class Property:
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sim_epc.update(
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{
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"heating-cost-current": phase_impact["unadjusted_heating_cost"],
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"hot-water-cost-current": phase_impact["unadjusted_hot_water_cost"],
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"heating-cost-current": phase_impact["epc_heating_cost"],
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"hot-water-cost-current": phase_impact["epc_hot_water_cost"],
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# CO₂ emissions per square metre floor area per year in kg/m². Since CO₂ emissions are in tonnes
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# per year, we multiply by 1000 to get kg/m²
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"co2-emiss-curr-per-floor-area": round(
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@ -470,9 +470,9 @@ class Property:
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"co2-emissions-current": phase_impact["carbon"],
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"current-energy-rating": sap_to_epc(phase_impact["sap"]),
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"current-energy-efficiency": int(np.floor(phase_impact["sap"])),
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"current-energy-cost": phase_impact["unadjusted_energy_cost"],
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"energy-consumption-current": phase_impact["heat_demand"],
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"lighting-cost-current": phase_impact["unadjusted_lighting_cost"],
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"lighting-cost-current": phase_impact["epc_lighting_cost"],
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"phase": phase
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}
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)
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updated_simulation_epcs.append(sim_epc)
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@ -872,6 +872,9 @@ async def trigger_plan(body: PlanTriggerRequest):
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]
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recommendations[property_id] = final_recommendations
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# We call the API with the scoring epcs
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scoring_epcs = pd.DataFrame(scoring_epcs)
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# 1) the property data
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# 2) the property details (epc)
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# 3) the recommendations
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@ -1,4 +1,5 @@
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import pandas as pd
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import numpy as np
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from backend.Property import Property
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from typing import List
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from itertools import groupby
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@ -395,14 +396,6 @@ class Recommendations:
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property_recommendations = recommendations[property_instance.id].copy()
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# We calculate the impact by phase
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phase_impact = {
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prefix: property_predictions[prefix + "_predictions"].groupby("phase")["predictions"].median().reset_index()
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for prefix in [
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"sap_change", "heat_demand", "carbon_change", "lighting_cost", "heating_cost", "hot_water_cost"
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]
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}
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# TODO: should fabric upgrades have an impact on hot water costs/kwh?
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# TODO: Generally, the costing models are just increasing. Maybe they're including something in the model
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# that they shouldn't e.g. SAP, carbon, heat demand etc?
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@ -446,48 +439,24 @@ class Recommendations:
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}
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else:
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previous_phase_values = {
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"sap": (
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phase_impact["sap_change"][phase_impact["sap_change"]["phase"] == (rec["phase"] - 1)]
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["predictions"].values[0]
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),
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"carbon": (
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phase_impact["carbon_change"][phase_impact["carbon_change"]["phase"] == (rec["phase"] - 1)]
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["predictions"].values[0]
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),
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"heat_demand": (
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phase_impact["heat_demand"][phase_impact["heat_demand"]["phase"] == (rec["phase"] - 1)]
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["predictions"].values[0]
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),
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}
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if rec["type"] == "low_energy_lighting":
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# Heating and hot water costs shouldn't change
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# {'unadjusted_heating_cost': 501.8528134938132, 'unadjusted_hot_water_cost':
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# 171.22534405283452, 'unadjusted_lighting_cost': 127.2}
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previous_phase_unadjusted_costs = {
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"unadjusted_heating_cost": phase_cost["heating_cost"]["predictions"].values[0],
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"unadjusted_hot_water_cost": phase_cost["hot_water_cost"]["predictions"].values[0],
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"unadjusted_lighting_cost": phase_impact["lighting_cost"][
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phase_impact["lighting_cost"]["phase"] == (rec["phase"] - 1)
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]["predictions"].values[0]
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}
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previous_phase_values_multiple = [x for x in impact_summary if x["phase"] == (rec["phase"] - 1)]
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if len(previous_phase_values_multiple) != 1:
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# Take an average of each of the previous phases
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keys_to_median = [
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"sap", "carbon", "heat_demand", "epc_heating_cost", "epc_hot_water_cost",
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"epc_lighting_cost"
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]
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previous_phase_values = {}
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for key in keys_to_median:
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values = [item[key] for item in previous_phase_values_multiple]
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previous_phase_values[key] = np.median(values)
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else:
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# update heating and hot water costs
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previous_phase_unadjusted_costs = {
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"unadjusted_heating_cost": phase_impact["heating_cost"][
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phase_impact["heating_cost"]["phase"] == (rec["phase"] - 1)
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]["predictions"].values[0],
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"unadjusted_hot_water_cost": phase_impact["hot_water_cost"][
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phase_impact["hot_water_cost"]["phase"] == (rec["phase"] - 1)
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]["predictions"].values[0],
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"unadjusted_lighting_cost": phase_cost["lighting_cost"]["predictions"].values[0]
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}
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previous_phase_values = previous_phase_values_multiple[0]
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# We extract the values for the current phase
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# TODO: For things like lighting costs for heating and hot water recommendations, we should actually
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# update phase_cost since the phase cost should be the same as the previous phase
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current_phase_values = {
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"sap": phase_energy_efficiency_metrics["sap_change"],
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"carbon": phase_energy_efficiency_metrics["carbon_change"],
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@ -510,6 +479,27 @@ class Recommendations:
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current_phase_values.update(current_phase_costs)
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increasing_variables = ["sap"]
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decreasing_variables = [
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"carbon", "heat_demand", "epc_heating_cost", "epc_hot_water_cost", "epc_lighting_cost"
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]
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# For increasing variables, the new value needs to be higher than the previous, otherwise we set it to
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# the previous
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# For decreasing variables, the new value should be lower than the previous, otherwise we set it to
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# the previous
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# In either case, we adjudge the recommendation to have had no/negligible impact
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for v in increasing_variables:
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current_phase_values[v] = (
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current_phase_values[v] if current_phase_values[v] > previous_phase_values[v] else
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previous_phase_values[v]
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)
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for v in previous_phase_values:
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if v in decreasing_variables:
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current_phase_values[v] = (
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current_phase_values[v] if current_phase_values[v] < previous_phase_values[v] else
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previous_phase_values[v]
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)
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property_phase_impact = {
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# Increasing
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"sap": current_phase_values["sap"] - previous_phase_values["sap"],
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@ -518,19 +508,19 @@ class Recommendations:
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# Decreasing
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"heat_demand": previous_phase_values["heat_demand"] - current_phase_values["heat_demand"],
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# Decreasing
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"unadjusted_heating_cost": (
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previous_phase_values["unadjusted_heating_cost"] -
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current_phase_values["unadjusted_heating_cost"]
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"epc_heating_cost": (
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previous_phase_values["epc_heating_cost"] -
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current_phase_values["epc_heating_cost"]
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),
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# Decreasing
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"unadjusted_hot_water_cost": (
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previous_phase_values["unadjusted_hot_water_cost"] -
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current_phase_values["unadjusted_hot_water_cost"]
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"epc_hot_water_cost": (
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previous_phase_values["epc_hot_water_cost"] -
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current_phase_values["epc_hot_water_cost"]
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),
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# Decreasing
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"unadjusted_lighting_cost": (
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previous_phase_values["unadjusted_lighting_cost"] -
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current_phase_values["unadjusted_lighting_cost"]
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"epc_lighting_cost": (
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previous_phase_values["epc_lighting_cost"] -
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current_phase_values["epc_lighting_cost"]
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)
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}
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