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Added pruning of solar panel options to prevent systems much too large or much too small

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Khalim Conn-Kowlessar 2024-04-05 15:00:24 +01:00
parent 02e72c5695
commit 4134fdbb75
1 changed files with 38 additions and 8 deletions
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46
recommendations/SolarPvRecommendations.py
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@ -8,6 +8,9 @@ class SolarPvRecommendations:
# Wattage per panel - this is based on the average wattage of a solar panel being between 250w and 420w
SOLAR_PANEL_WATTAGE = 250
MAX_SYSTEM_WATTAGE = 4200
MIN_SYSTEM_WATTAGE = 2500
def __init__(self, property_instance):
"""
:param property_instance: Instance of the Property class, for the home associated to property_id
@ -18,6 +21,19 @@ class SolarPvRecommendations:
self.recommendation = []
@staticmethod
def trim_solar_wattage_options(scenarios_with_wattage):
# Initialize the list with the first element, assuming the list is not empty
trimmed_list = [scenarios_with_wattage[0]]
# Iterate over the list starting from the second element
for scenario in scenarios_with_wattage[1:]:
# Compare the second element (index 1) of the current tuple with the last tuple in the trimmed list
if scenario[1] > trimmed_list[-1][1]:
trimmed_list.append(scenario)
return trimmed_list
def recommend(self, phase):
"""
We check if a property is potentially suitable for solar PV based on the following criteria:
@ -46,26 +62,40 @@ class SolarPvRecommendations:
self.property.solar_pv_percentage - 0.1, self.property.solar_pv_percentage,
self.property.solar_pv_percentage + 0.1
]
# We make sure we haven't gone too low or high
roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 1]
# We make sure we haven't gone too low or high - we allow no more than 60% coverage
roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
# If we only have two scenarios, we add a coverage scenario 10% less than the smallest
if len(roof_coverage_scenarios) == 2:
roof_coverage_scenarios.insert(0, roof_coverage_scenarios[0] - 0.1)
battery_scenarios = [False, True]
# I now produce the cross product of the scenarios
scenarios = [(roof, battery) for roof in roof_coverage_scenarios for battery in battery_scenarios]
for roof_coverage, has_battery in scenarios:
scenarios_with_wattage = []
for roof_coverage in roof_coverage_scenarios:
# We now have a property which is potentially suitable for solar PV
solar_pv_roof_area = self.property.get_solar_pv_roof_area(roof_coverage)
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
)
scenarios_with_wattage.append((roof_coverage, solar_panel_wattage))
# We trim the scenarios, so that we don't have duplicate wattages
scenarios_with_wattage = self.trim_solar_wattage_options(scenarios_with_wattage)
# Produce the cross product of the scenarios
scenarios = [
(roof, wattage, battery) for roof, wattage in scenarios_with_wattage for battery in battery_scenarios
]
# We deduce the wattage of the solar panels based on the roof coverage
for roof_coverage, solar_panel_wattage, has_battery in scenarios:
# We now have a property which is potentially suitable for solar PV
roof_coverage_percent = round(roof_coverage * 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=has_battery)
kw = np.floor(solar_panel_wattage / 100) / 10
if has_battery: