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Updating optimiser to only optimise solar recommendations that include the battery

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This commit is contained in:
Khalim Conn-Kowlessar 2024-04-10 18:46:51 +01:00
parent e0e60f8c98
commit 505fe0736b
3 changed files with 11 additions and 11 deletions
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7
backend/app/plan/router.py
View file

@ -75,7 +75,7 @@ async def trigger_plan(body: PlanTriggerRequest):
logger.info("Connecting to db")
session = sessionmaker(bind=db_engine)()
created_at = datetime.now().isoformat()
# TODO: We should store the trigger file path in the database with the plan so we can track the file that
# triggered the plan
@ -242,7 +242,7 @@ async def trigger_plan(body: PlanTriggerRequest):
expected_adjusted_energy=expected_adjusted_energy
)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal, body.housing_type)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
current_sap_points = int(property_instance.data["current-energy-efficiency"])
target_sap_points = epc_to_sap_lower_bound(body.goal_value)
@ -279,9 +279,6 @@ async def trigger_plan(body: PlanTriggerRequest):
if ventilation_rec:
selected_recommendations.add(ventilation_rec["recommendation_id"])
# We check if the selected recommendation is wall ventilation and if so, we make sure
# mechanical ventilation is selected
# We'll use the set of selected recommendations to filter the recommendations to upload
final_recommendations = [
[

3
recommendations/SolarPvRecommendations.py
View file

@ -122,6 +122,7 @@ class SolarPvRecommendations:
**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": 100 * roof_coverage
"photo_supply": 100 * roof_coverage,
"has_battery": has_battery
}
)

12
recommendations/optimiser/optimiser_functions.py
View file

@ -1,17 +1,13 @@
def prepare_input_measures(property_recommendations, goal, housing_type):
def prepare_input_measures(property_recommendations, goal):
"""
Basic function to convert recommendations_to_upload to a format that is
suitable for the optimiser - large
:param property_recommendations: object containing the recommendations, created in the plan trigger api
:param goal: goal to be optimised for, should be one of the keys in gain_map. E.g. if the gain is SAP points,
the goal should reflect that desired gain
:param housing_type: type of housing the recommendations are for - should be one of "Social" or "Private"
:return: Nested list of input measures
"""
if housing_type not in ["Social", "Private"]:
raise ValueError("Invalid housing type - investigate me")
goal_map = {
"Increase EPC": "sap_points"
}
@ -22,6 +18,12 @@ def prepare_input_measures(property_recommendations, goal, housing_type):
input_measures = []
for recs in property_recommendations:
if recs[0]["type"] == "solar_pv":
# if the recommendation is a solar recommendation without a battery, we exclude it from the optimisation.
# That will ensure that the optimiser only considers solar recommendations with batteries, so we don't
# under-report the potential cost
recs = [r for r in recs if recs["has_battery"]]
input_measures.append(
[
{