mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
changing simulation methodology to use corrected floor area
This commit is contained in:
Khalim Conn-Kowlessar
parent
91b9530578
commit
292da782a0
10 changed files with 105 additions and 127 deletions
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@ -70,6 +70,10 @@ class Property:
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# Contains the solar panel optimisation results from the Google Solar API
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solar_panel_configuration = None
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# If true, indicates the floor area has actually been given to us by the owner, and we should use this figure
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# instead of the one in the EPC, when we simulate
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owner_floor_area = False
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def __init__(
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self,
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id,
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@ -241,6 +245,10 @@ class Property:
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insulation_wall_area = kwargs.get("insulation_wall_area", None)
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insulation_wall_area = float(insulation_wall_area) if insulation_wall_area not in [None, ""] else None
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# We allow for the asset owner to provide us with total floor area, in the event of it being incorrect
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floor_area = kwargs.get("floor_area", None)
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floor_area = float(floor_area) if floor_area not in [None, ""] else None
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return {
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"n_bathrooms": n_bathrooms,
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"n_bedrooms": n_bedrooms,
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@ -248,12 +256,15 @@ class Property:
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"insulation_floor_area": insulation_floor_area,
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"insulation_wall_area": insulation_wall_area,
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"building_id": kwargs.get("building_id", None),
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"floor_area": floor_area
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}
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def parse_kwargs(self, kwargs):
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# We extract the elements from kwargs that we recognise. Anything additional is ignored
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for arg, val in kwargs.items():
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if val is not None:
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if arg == "floor_area":
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self.owner_floor_area = True
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setattr(self, arg, val)
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def create_base_difference_epc_record(self, cleaned_lookup: dict):
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@ -263,14 +274,7 @@ class Property:
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It will be the same starting and ending EPC, as we don't have the expected EPC yet
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"""
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# difference_record = self.epc_record - self.epc_record
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# TODO: change these lower and replace in the settings file
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# print(
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# "CHANGE THE LATEST FIELD TO REMOVE NUMBER HABITABLE ROOMS IF WE WANT TO USE STARTING/ENDING"
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# )
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fixed_data_col_names = MANDATORY_FIXED_FEATURES + LATEST_FIELD
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# print("NEED TO CHANGE THE DASH TO LOWER CASE")
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fixed_data_col_names = [
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x.lower().replace("_", "-") for x in fixed_data_col_names
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]
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@ -281,8 +285,6 @@ class Property:
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if k in fixed_data_col_names
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}
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# difference_record.append_fixed_data(fixed_data)
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difference_record = self.epc_record.create_EPCDifferenceRecord(
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self.epc_record, fixed_data
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)
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@ -291,10 +293,11 @@ class Property:
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datasets=[difference_record], cleaned_lookup=cleaned_lookup
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)
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# TODO: adjust the base difference record with the previously calculated u values + features
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# estimated_perimeter is different to the perimeter in the epc record
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# self.base_difference_record.df
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# If we have variables that have been given to us by the landlord that we know are correct, whereas the EPC
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# may not be, we use them
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if self.owner_floor_area is not None:
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self.base_difference_record.df["total_floor_area_ending"] = self.floor_area
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self.base_difference_record.df["estimated_perimeter_ending"] = self.perimeter
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def simulate_all_representative_recommendations(
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self, property_representative_recommendations,
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@ -1254,7 +1257,10 @@ class Property:
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# If the property is in a conservation area, is listed or is a heriage building, solar panels
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# become a difficult measure to generally get through planning restrictions and so we do not recommend
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# solar panels
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if self.restricted_measures:
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if self.is_listed or self.is_heritage:
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# If the property is in a conservation area, we can still recommend solar panels
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# but they need to be done in a way that is sympathetic to the building. E.g. the panels
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# may be installed such that they are not visible from the street
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return False
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is_valid_property_type = self.data["property-type"] in ["House", "Bungalow", "Maisonette"]
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@ -19,6 +19,7 @@ class MaterialType(enum.Enum):
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flat_roof_insulation = "flat_roof_insulation"
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room_roof_insulation = "room_roof_insulation"
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windows_glazing = "windows_glazing"
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cavity_wall_extraction = "cavity_wall_extraction"
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iwi_wall_demolition = "iwi_wall_demolition"
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iwi_vapour_barrier = "iwi_vapour_barrier"
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@ -445,16 +445,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
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)
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# Set up model api and warm up the lambdas
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model_api = ModelApi(
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portfolio_id=body.portfolio_id,
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timestamp=created_at,
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prediction_buckets=get_prediction_buckets()
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)
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await model_api.async_warm_up_lambdas(
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model_prefies=model_api.KWH_MODEL_PREFIXES + model_api.MODEL_PREFIXES
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)
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input_properties = []
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for config in tqdm(plan_input):
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# We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
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@ -549,6 +539,17 @@ async def trigger_plan(body: PlanTriggerRequest):
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if not input_properties:
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return Response(status_code=204)
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# Set up model api and warm up the lambdas
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model_api = ModelApi(
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portfolio_id=body.portfolio_id,
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timestamp=created_at,
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prediction_buckets=get_prediction_buckets(),
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max_retries=1
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)
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await model_api.async_warm_up_lambdas(
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model_prefies=model_api.KWH_MODEL_PREFIXES + model_api.MODEL_PREFIXES
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)
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# The materials data could be cached or local so we don't need to make
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# consistent requests to the backend for
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# the same data
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@ -699,7 +700,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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property_instance.current_energy_bill = property_current_energy_bill
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# Insert the predictions into the recommendations and run the optimiser
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for p in input_properties:
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if not recommendations.get(p.id):
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continue
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@ -712,8 +712,13 @@ async def trigger_plan(body: PlanTriggerRequest):
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else:
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current_sap_points = int(p.data["current-energy-efficiency"])
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target_sap_points = epc_to_sap_lower_bound(body.goal_value)
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sap_gain = CostOptimiser.calculate_sap_gain_with_slack(target_sap_points - current_sap_points)
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ventilation_impact = next(
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(r[0]["sap_points"] for r in recommendations[p.id] if r[0]["type"] == "mechanical_ventilation"),
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0
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)
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sap_gain = CostOptimiser.calculate_sap_gain_with_slack(
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epc_to_sap_lower_bound(body.goal_value) - current_sap_points
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) + abs(ventilation_impact)
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if not body.optimise:
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if body.goal != "Increasing EPC":
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@ -778,6 +783,10 @@ async def trigger_plan(body: PlanTriggerRequest):
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final_recommendations = [
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rec for recommendations_by_type in final_recommendations for rec in recommendations_by_type
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]
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# Get defaults
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defaults = [r for r in final_recommendations if r["default"]]
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sum([r['sap_points'] for r in defaults])
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recommendations[p.id] = final_recommendations
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# when we have buildings, we tweak our solar PV recommendations as if one unit needs it, we apply it to all
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@ -39,6 +39,7 @@ class ModelApi:
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timestamp,
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prediction_buckets,
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base_url="https://api.dev.hestia.homes",
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max_retries=2,
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):
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"""
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This class handles the communication with the Model APIs. These models include SAP change, heat demain change
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@ -54,6 +55,8 @@ class ModelApi:
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self.timestamp = timestamp
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self.prediction_buckets = prediction_buckets
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self.max_retries = max_retries
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@staticmethod
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def predictions_template():
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return {
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@ -295,15 +298,33 @@ class ModelApi:
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async def run_batches():
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for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
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predictions_dict = await self.predict_all_async(
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df=data.iloc[chunk:chunk + batch_size],
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bucket=bucket,
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model_prefixes=model_prefixes,
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extract_ids=extract_ids
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)
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for key, scored in predictions_dict.items():
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all_predictions[key] = pd.concat([all_predictions[key], scored])
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attempts = 0
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success = False
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while attempts <= self.max_retries and not success:
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try:
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predictions_dict = await self.predict_all_async(
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df=data.iloc[chunk:chunk + batch_size],
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bucket=bucket,
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model_prefixes=model_prefixes,
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extract_ids=extract_ids
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)
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for key, scored in predictions_dict.items():
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all_predictions[key] = pd.concat([all_predictions[key], scored])
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success = True
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except Exception as e:
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attempts += 1
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logger.error(
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f"Batch {chunk}-{chunk + batch_size} failed (Attempt {attempts}/{self.max_retries}). "
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f"Error: {e}"
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)
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if attempts > self.max_retries:
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logger.error(
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f"Skipping batch {chunk}-{chunk + batch_size} after {self.max_retries} failed attempts."
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)
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# Check if there is an existing event loop
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try:
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@ -11,7 +11,7 @@ import inspect
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src_file_path = inspect.getfile(lambda: None)
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DATA_DIRECTORY = Path(src_file_path).parent / "local_data" / "20240917 Hestia Materials.xlsx"
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DATA_DIRECTORY = Path(src_file_path).parent / "local_data" / "20250316 Domna Materials.xlsx"
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# Environment file is at the same level as this file
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ENV_FILE = Path(src_file_path).parent / "etl" / "costs" / ".env"
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dotenv.load_dotenv(ENV_FILE)
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@ -91,6 +91,7 @@ def app():
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lel_costs = pd.read_excel(DATA_DIRECTORY, sheet_name="low_energy_lighting", header=0)
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flat_roof_costs = pd.read_excel(DATA_DIRECTORY, sheet_name="flat_roof_insulation", header=0)
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window_costs = pd.read_excel(DATA_DIRECTORY, sheet_name="window_glazing", header=0)
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rir_insulation_costs = pd.read_excel(DATA_DIRECTORY, sheet_name="room_roof_insulation", header=0)
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# Form a single table to be uploaded
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costs = pd.concat(
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@ -104,7 +105,8 @@ def app():
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ewi_costs,
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lel_costs,
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flat_roof_costs,
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window_costs
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window_costs,
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rir_insulation_costs,
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]
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)
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@ -101,10 +101,10 @@ INSTALLER_ASHP_COSTS = [
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BOILER_UPGRADE_SCHEME_ASHP_VALUE = 7500
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INSTALLER_SOLAR_BATTERY_COSTS = [
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{'capacity_kwh': 5, 'description': 'Battery Add on', 'cost': 2700.00, 'installer': 'CEG'},
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{'capacity_kwh': 10, 'description': 'Battery Add on', 'cost': 4300.00, 'installer': 'CEG'},
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{'capacity_kwh': 5, 'description': 'Battery Retrofit existing system', 'cost': 4250.00, 'installer': 'CEG'},
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{'capacity_kwh': 10, 'description': 'Battery Retrofit Existing system', 'cost': 5950.00, 'installer': 'CEG'}
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{'capacity_kwh': 5, 'description': 'Battery Add on', 'cost': 2030.40, 'installer': 'CEG'},
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# {'capacity_kwh': 10, 'description': 'Battery Add on', 'cost': 4300.00, 'installer': 'CEG'},
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# {'capacity_kwh': 5, 'description': 'Battery Retrofit existing system', 'cost': 4250.00, 'installer': 'CEG'},
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# {'capacity_kwh': 10, 'description': 'Battery Retrofit Existing system', 'cost': 5950.00, 'installer': 'CEG'}
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]
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# This is based on https://www.checkatrade.com/blog/cost-guides/cost-smart-thermostat/
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@ -149,7 +149,7 @@ CONDENSING_BOILER_COSTS = {
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ELECTRIC_BOILER_COSTS = 1800
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# Assumes 1 hours to remove each heater (including re-decorating)
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ROOM_HEATER_REMOVAL_COST = 50
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ROOM_HEATER_REMOVAL_COST = 25
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ROOM_HEATER_REMOVAL_LABOUR_HOURS = 3
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# This is a cost quoted by Jim for a system flush - existig system will run more efficiently
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@ -461,6 +461,7 @@ class Recommendations:
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:param property_instance: Instance of the Property class, for the home associated to property_id
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:param all_predictions: dictionary of predictions from the model apis
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:param recommendations: dictionary of recommendations for the property
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:param representative_recommendations: dictionary of representative recommendations for the property
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:return:
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"""
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@ -52,6 +52,10 @@ class RoofRecommendations:
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part for part in materials if part["type"] == "flat_roof_insulation"
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]
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self.room_roof_insulation_materials = [
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part for part in materials if part["type"] == "room_roof_insulation"
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]
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# Extract the insulation thickness from the roof, which is used throughout this method
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self.insulation_thickness = convert_thickness_to_numeric(
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self.property.roof["insulation_thickness"],
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@ -496,29 +500,22 @@ class RoofRecommendations:
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:return:
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"""
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# TODO: We temporarilty use costs from SCIS for RIR insulation. The costing was £180/m2 floor
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roof_roof_insulation_materials = [
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{
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"type": "room_roof_insulation",
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"measure_type": "room_roof_insulation",
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"description": "Insulating the ceiling of the roof roof and re-decorate",
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"depths": [100],
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"depth_unit": "mm",
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"r_value_per_mm": 0.038,
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"thermal_conductivity": 0.022,
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"cost": [180],
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}
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]
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# We have a list of materials that can be used for room roof insulation
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# We will iterate over these materials and recommend them based on the current u-value of the roof
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# and the cost of the materials
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rir_non_invasive_recommendation = next(
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(x for x in self.property.non_invasive_recommendations if x["type"] == "room_roof_insulation"), {}
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)
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insulation_materials = pd.DataFrame(self.room_roof_insulation_materials)
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# lowest_selected_u_value = None
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recommendations = []
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for material in roof_roof_insulation_materials:
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for depth, cost_per_unit in zip(material["depths"], material["cost"]):
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part_u_value = r_value_per_mm_to_u_value(depth, material["r_value_per_mm"])
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for _, material_group in insulation_materials.groupby("description"):
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for material in material_group.itertuples():
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part_u_value = r_value_per_mm_to_u_value(material.depth, material.r_value_per_mm)
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_, new_u_value = calculate_u_value_uplift(u_value, part_u_value)
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new_u_value = math.ceil(new_u_value * 100.0) / 100.0
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@ -526,7 +523,7 @@ class RoofRecommendations:
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# We allow a small tolerance for error so we don't discount the recommendation entirely
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estimated_cost = (
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cost_per_unit * self.property.insulation_floor_area if
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material.total_cost * self.property.insulation_floor_area if
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rir_non_invasive_recommendation.get("cost") is None else
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rir_non_invasive_recommendation.get("cost")
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)
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@ -9,12 +9,6 @@ class SecondaryHeating:
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system.
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"""
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# The list of existing heating systems that are accepted
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ACCEPTED_MAINHEAT_DESCRIPTIONS = ["Boiler and radiators, mains gas", "Electric storage heaters"]
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ACCEPTED_SECONDHEAT_DESCRIPTIONS = ["Room heaters, electric", 'Portable electric heaters (assumed)']
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# These are the heaters where works are required to remove them
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FIXED_HEATER_DESCRIPTIONS = ["Room heaters, electric"]
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def __init__(self, property_instance: Property):
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self.property = property_instance
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self.costs = Costs(self.property)
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@ -25,18 +19,10 @@ class SecondaryHeating:
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# Reset
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self.recommendation = []
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if self.property.main_heating["clean_description"] not in self.ACCEPTED_MAINHEAT_DESCRIPTIONS:
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return
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# TODO: We need to clean secondary data
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if self.property.data['secondheat-description'] not in self.ACCEPTED_SECONDHEAT_DESCRIPTIONS:
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return
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if self.property.data['secondheat-description'] in self.FIXED_HEATER_DESCRIPTIONS:
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# We have an associated cost otherwise, there is no cost
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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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else:
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n_rooms = 0
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n_rooms = self.property.data["number-heated-rooms"]
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costs = self.costs.heater_removal(n_rooms=n_rooms)
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@ -7,14 +7,6 @@ from recommendations.recommendation_utils import override_costs, estimate_pitche
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class SolarPvRecommendations:
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# Solar panel specs based on Eurener 400s solar panels
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# https://midsummerwholesale.co.uk/buy/eurener/eurener-400w-mepv-zebra-ab-half-cut-mono
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# Approximate area of the solar panels
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SOLAR_PANEL_AREA = 1.79
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# Wattage per panel - this is based on the average wattage of a solar panel being between 250w and 420w
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# This was previously set to 250w, but has been upped to 400 based on the systems used by Cotswolrd Energy Group
|
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SOLAR_PANEL_WATTAGE = 400
|
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# For domestic properties, we don't recommend a solar PV system with wattage outside of these
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# bounds
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MAX_SYSTEM_WATTAGE = 6000
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|
|
@ -65,46 +57,6 @@ class SolarPvRecommendations:
|
|||
|
||||
return trimmed_list
|
||||
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def mds_recommend(self, phase=None, solar_pv_percentage=0.5):
|
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# For specific usage within the mds report
|
||||
|
||||
solar_pv_roof_area = self.property.get_solar_pv_roof_area(solar_pv_percentage)
|
||||
|
||||
number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
|
||||
solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
|
||||
|
||||
solar_panel_wattage = np.clip(
|
||||
a=solar_panel_wattage, a_min=self.MIN_SYSTEM_WATTAGE, a_max=self.MAX_SYSTEM_WATTAGE
|
||||
)
|
||||
|
||||
# We now have a property which is potentially suitable for solar PV
|
||||
roof_coverage_percent = round(solar_pv_percentage * 100)
|
||||
# Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
|
||||
# of solar PV installations
|
||||
cost_result = self.costs.solar_pv(wattage=solar_panel_wattage, has_battery=False)
|
||||
kw = np.floor(solar_panel_wattage / 100) / 10
|
||||
|
||||
description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
|
||||
f"anel system on {round(roof_coverage_percent)}% the roof.")
|
||||
|
||||
return [
|
||||
{
|
||||
"phase": phase,
|
||||
"parts": [],
|
||||
"type": "solar_pv",
|
||||
"description": description,
|
||||
"starting_u_value": None,
|
||||
"new_u_value": None,
|
||||
"sap_points": None,
|
||||
"already_installed": False,
|
||||
**cost_result,
|
||||
# This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
|
||||
# back up here
|
||||
"photo_supply": roof_coverage_percent,
|
||||
"has_battery": False
|
||||
}
|
||||
]
|
||||
|
||||
def recommend_building_analysis(self, phase):
|
||||
"""
|
||||
This recommendation approach handles the case of producing solar PV recommendations at the building level,
|
||||
|
|
@ -258,11 +210,14 @@ class SolarPvRecommendations:
|
|||
)
|
||||
kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
|
||||
if has_battery:
|
||||
description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
|
||||
f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
|
||||
description = (
|
||||
f"Install a {kw} kilowatt-peak (kWp) solar panel system, with a battery."
|
||||
)
|
||||
else:
|
||||
description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
|
||||
f"anel system on {round(roof_coverage_percent)}% the roof.")
|
||||
description = f"Install a {kw} kilowatt-peak (kWp) solar panel system."
|
||||
|
||||
if self.property.in_conservation_area:
|
||||
description += " Property is in a consevation area - please check with local planning authority."
|
||||
|
||||
already_installed = "solar_pv" in self.property.already_installed
|
||||
if already_installed:
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue