mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
recommendation fuel wip
This commit is contained in:
Khalim Conn-Kowlessar
parent
4ebd516d2e
commit
aa391966ef
2 changed files with 56 additions and 76 deletions
|
|
@ -444,14 +444,17 @@ async def trigger_plan(body: PlanTriggerRequest):
|
|||
|
||||
kwh_client = KwhData(bucket=get_settings().DATA_BUCKET, read_consumption_data=True)
|
||||
|
||||
model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
|
||||
model_api = ModelApi(
|
||||
portfolio_id=body.portfolio_id,
|
||||
timestamp=created_at,
|
||||
prediction_buckets=get_prediction_buckets()
|
||||
)
|
||||
|
||||
epcs_for_scoring = kwh_client.transform(data=kwh_client.prepare_epc(input_properties), cleaned=cleaned)
|
||||
|
||||
kwh_preds = model_api.predict_all(
|
||||
df=epcs_for_scoring,
|
||||
bucket=get_settings().DATA_BUCKET,
|
||||
prediction_buckets=get_prediction_buckets(),
|
||||
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
|
||||
extract_ids=False
|
||||
)
|
||||
|
|
@ -687,11 +690,57 @@ async def trigger_plan(body: PlanTriggerRequest):
|
|||
kwh_simulation_predictions = model_api.predict_all(
|
||||
df=scoring_epcs,
|
||||
bucket=get_settings().DATA_BUCKET,
|
||||
prediction_buckets=get_prediction_buckets(),
|
||||
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
|
||||
)
|
||||
|
||||
# TODO: Costing model, which should include today's costs!
|
||||
# We now insert into the recommendations
|
||||
for property_id in recommendations.keys():
|
||||
property_recommendations = recommendations[property_id]
|
||||
property_instance = [p for p in input_properties if p.id == property_id][0]
|
||||
|
||||
kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
|
||||
kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_id)
|
||||
].merge(
|
||||
kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
|
||||
columns=["property_id", "recommendation_id", "phase"]
|
||||
),
|
||||
how="inner",
|
||||
on="id",
|
||||
suffixes=("_heating", "_hotwater")
|
||||
)
|
||||
|
||||
property_kwh = property_instance.energy_consumption_estimates["unadjusted"]
|
||||
|
||||
kwh_impact_table = pd.concat(
|
||||
[
|
||||
pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": None,
|
||||
"predictions_heating": property_kwh["heating"],
|
||||
"predictions_hotwater": property_kwh["hot_water"],
|
||||
}
|
||||
]
|
||||
),
|
||||
kwh_impact_table
|
||||
]
|
||||
)
|
||||
# We adjust the predictions with the UCL model
|
||||
for k in ["heating", "hotwater"]:
|
||||
kwh_impact_table[f"adjusted_{k}"] = kwh_impact_table[f"predictions_{k}"].apply(
|
||||
lambda x: AnnualBillSavings.adjust_energy_to_metered(
|
||||
epc_energy=x, current_epc_rating=property_instance.data["current-energy-rating"]
|
||||
)
|
||||
)
|
||||
|
||||
kwh_impact_table["heating_fuel"] = property_instance.heating_energy_source
|
||||
kwh_impact_table["hotwater_fuel"] = property_instance.hot_water_energy_source
|
||||
|
||||
# We now deduce if any of the recommendations result in a change of fuel type
|
||||
for recs in property_recommendations:
|
||||
for rec in recs:
|
||||
print(rec["description_simulation"])
|
||||
|
||||
# Insert the predictions into the recommendations and run the optimiser
|
||||
# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
|
||||
|
|
@ -754,70 +803,6 @@ async def trigger_plan(body: PlanTriggerRequest):
|
|||
]
|
||||
recommendations[p.id] = final_recommendations
|
||||
|
||||
# We now insert into the recommendations
|
||||
for property_id in recommendations.keys():
|
||||
property_recommendations = recommendations[property_id]
|
||||
property_instance = [p for p in input_properties if p.id == property_id][0]
|
||||
# The predicted kwhs are without appliances
|
||||
consumption = property_instance.energy_consumption_estimates["adjusted"]
|
||||
# Starting consumption is the sum of the consumption values, without appliances
|
||||
starting_heating = consumption["heating"]
|
||||
starting_hotwater = consumption["hot_water"]
|
||||
property_kwh_predictions = {
|
||||
k: kwh_simulation_predictions[k][kwh_simulation_predictions[k]["property_id"] == str(property_id)]
|
||||
for k in ['heating_kwh_predictions', 'hotwater_kwh_predictions']
|
||||
}
|
||||
# We adjust the predictions
|
||||
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
|
||||
for k in ["heating_kwh_predictions", "hotwater_kwh_predictions"]:
|
||||
property_kwh_predictions[k]["adjusted"] = property_kwh_predictions[k]["predictions"].apply(
|
||||
lambda x: AnnualBillSavings.adjust_energy_to_metered(
|
||||
epc_energy=x, current_epc_rating=property_instance.data["current-energy-rating"]
|
||||
)
|
||||
)
|
||||
|
||||
# For each recommendation, we difference the predictions
|
||||
property_kwh_predictions["heating_kwh_predictions"]["savings"] = np.diff(
|
||||
property_kwh_predictions["heating_kwh_predictions"]["adjusted"], prepend=starting_heating
|
||||
)
|
||||
property_kwh_predictions["hotwater_kwh_predictions"]["savings"] = np.diff(
|
||||
property_kwh_predictions["hotwater_kwh_predictions"]["adjusted"], prepend=starting_hotwater
|
||||
)
|
||||
|
||||
for recommendations_by_type in property_recommendations:
|
||||
for rec in recommendations_by_type:
|
||||
# In the case of mechanical ventilation, there is no impact, and for low energy lighting we
|
||||
# calculate the savings inside of the recommendation itself
|
||||
if rec["type"] in ["mechanical_ventilation", "low_energy_lighing"]:
|
||||
continue
|
||||
|
||||
heating_kwh_savings = property_kwh_predictions["heating_kwh_predictions"][
|
||||
(
|
||||
property_kwh_predictions["heating_kwh_predictions"]["recommendation_id"] ==
|
||||
rec["recommendation_id"]
|
||||
)
|
||||
]["savings"].values[0]
|
||||
# This should be negative
|
||||
if heating_kwh_savings > 0:
|
||||
print("Positive heating kwh savings")
|
||||
# TODO: Raise an exception to investigate
|
||||
# raise Exception("Positive heating kwh savings")
|
||||
|
||||
hot_water_kwh_savings = property_kwh_predictions["hotwater_kwh_predictions"][
|
||||
(
|
||||
property_kwh_predictions["hotwater_kwh_predictions"]["recommendation_id"] ==
|
||||
rec["recommendation_id"]
|
||||
)
|
||||
]["savings"].values[0]
|
||||
|
||||
# This should be negative
|
||||
if hot_water_kwh_savings > 0:
|
||||
print("Positive hot water kwh savings")
|
||||
# TODO: Raise an exception to investigate
|
||||
# raise Exception("Positive hot water kwh savings")
|
||||
|
||||
rec["kwh_savings"] = abs(heating_kwh_savings + hot_water_kwh_savings)
|
||||
|
||||
# 1) the property data
|
||||
# 2) the property details (epc)
|
||||
# 3) the recommendations
|
||||
|
|
@ -1154,12 +1139,6 @@ async def build_mds(body: MdsRequest):
|
|||
for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
|
||||
predictions_dict = model_api.predict_all(
|
||||
df=recommendations_scoring_data.iloc[chunk:chunk + SCORING_BATCH_SIZE],
|
||||
bucket=get_settings().DATA_BUCKET,
|
||||
prediction_buckets={
|
||||
"sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
|
||||
"heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
|
||||
"carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET
|
||||
}
|
||||
)
|
||||
|
||||
# Append the predictions to the predictions dictionary
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ class ModelApi:
|
|||
self,
|
||||
portfolio_id,
|
||||
timestamp,
|
||||
prediction_buckets,
|
||||
base_url="https://api.dev.hestia.homes",
|
||||
):
|
||||
"""
|
||||
|
|
@ -46,6 +47,7 @@ class ModelApi:
|
|||
self.base_url = base_url
|
||||
self.portfolio_id = portfolio_id
|
||||
self.timestamp = timestamp
|
||||
self.prediction_buckets = prediction_buckets
|
||||
|
||||
@staticmethod
|
||||
def predictions_template():
|
||||
|
|
@ -125,7 +127,7 @@ class ModelApi:
|
|||
else:
|
||||
return None
|
||||
|
||||
def predict_all(self, df, bucket, prediction_buckets, model_prefixes=None, extract_ids=True) -> dict:
|
||||
def predict_all(self, df, bucket, model_prefixes=None, extract_ids=True) -> dict:
|
||||
|
||||
"""
|
||||
For each model prefix, this method will upload the scoring data to s3 and then make a request to the
|
||||
|
|
@ -134,7 +136,6 @@ class ModelApi:
|
|||
a dictionary of panaas dataframes
|
||||
:param df: Pandas dataframe with scoring data to be uploaded to s3
|
||||
:param bucket: Name of the bucket in s3 to upload to
|
||||
:param prediction_buckets: Dictionary containing the prediction buckets for each model prefix
|
||||
:param model_prefixes: List of model prefixes to generate predictions for. If None, all model prefixes will be
|
||||
used
|
||||
:param extract_ids: Boolean to determine if the property_id and recommendation_id should be extracted from the
|
||||
|
|
@ -152,7 +153,7 @@ class ModelApi:
|
|||
"s3://{DATA_BUCKET}/".format(DATA_BUCKET=bucket) + file_location, model_prefix
|
||||
)
|
||||
|
||||
predictions_bucket = prediction_buckets[model_prefix]
|
||||
predictions_bucket = self.prediction_buckets[model_prefix]
|
||||
|
||||
# Retrieve the predictions
|
||||
predictions_df = pd.DataFrame(
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue