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20240731 set a city from a geojson file, plot monthly district PV with different efficiencies

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Andrea Gabaldon Moreno 2024-07-31 15:59:22 -04:00
parent 0ce76bef13
commit 1488c9dbce
2 changed files with 104 additions and 46 deletions
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71
main_results_in_geojson.py
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@ -16,12 +16,12 @@ import json
input_files_path = (Path(__file__).parent / 'input_files')
output_path = (Path(__file__).parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
with open(geojson_file_path , 'r') as f:
building_type_data = json.load(f)
with open(geojson_file_path_baseline, 'r') as f:
building_type_data_baseline = json.load(f)
# Create city object from GeoJSON file
city = GeometryFactory('geojson',
@ -110,37 +110,30 @@ enrich_buildings_with_geojson_data (building_type_data, city)
# building.appliances_electrical_demand[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
print('test')
for building in city.buildings:
building.energy_systems_archetype_name = 'system 1 gas'
# def to_dict(building, total_floor_area):
# return {
# 'roof_area': building.floor_area,
# 'total_floor_area': total_floor_area,
# 'year_of_construction' : building.year_of_construction,
# 'type_function':building.function,
# 'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
# 'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
# 'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
# 'average_storey_height_meters': building.average_storey_height,
# 'max_height_meters_meters': building.max_height,
# 'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
# 'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
# 'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
# 'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
# 'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
# 'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
# 'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
# 'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
# 'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
# 'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
# 'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
# 'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
# 'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
# 'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
# 'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
# 'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
# }
# buildings_dic={}
EnergySystemsFactory('montreal_custom', city).enrich()
for building in city.buildings:
energy_systems = building.energy_systems
for energy_system in energy_systems:
generation_units = energy_system.generation_systems
if cte.HEATING in energy_system.demand_types:
for generation_unit in generation_units:
generation_unit.heat_efficiency = 0.96
def to_dict(building, total_floor_area):
return {
'roof_area': building.floor_area,
'total_floor_area': total_floor_area,
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
# 'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
# 'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
# 'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
# 'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
}
buildings_dic={}
for building in city.buildings:
@ -149,23 +142,9 @@ for building in city.buildings:
for thermal_zone in building.thermal_zones_from_internal_zones:
total_floor_area += thermal_zone.total_floor_area
print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
building.energy_systems_archetype_name = 'system 1 gas'
EnergySystemsFactory('montreal_custom', city).enrich()
print('test')
for building in city.buildings:
energy_systems = building.energy_systems
for energy_system in energy_systems:
generation_units = energy_system.generation_systems
if cte.HEATING in energy_system.demand_types:
for generation_unit in generation_units:
generation_unit.heat_efficiency = 0.96
# for building in city.buildings:
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
# print(building.name)

79
plot.py Normal file
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@ -0,0 +1,79 @@
import pandas as pd
import matplotlib.pyplot as plt
# Load the CSV file
file_path = r'C:\Users\a_gabald\Desktop\solar_different_efficiencies.csv' # Replace with your file path
data = pd.read_csv(file_path)
# Assuming the CSV has a datetime column, parse it
data['datetime'] = pd.date_range(start='1/1/2023', periods=8760, freq='h')
# Set the datetime column as the index
data.set_index('datetime', inplace=True)
# Resample the data to monthly data
monthly_data = data.resample('ME').sum()
# Extract data for different efficiencies
monthly_16_horizontal = monthly_data['district MWh Horizontal_16%']
monthly_18_horizontal = monthly_data['district MWh Horizontal_18%']
monthly_14_horizontal = monthly_data['district MWh Horizontal_14%']
monthly_16_tilted = monthly_data['district MWh tilted_16%']
monthly_18_tilted = monthly_data['district MWh tilted_18%']
monthly_14_tilted = monthly_data['district MWh tilted_14%']
# Combine the data into a single DataFrame for easier plotting
combined_data_horizontal = pd.DataFrame({
'Month': monthly_data.index.month,
'Horizontal_16%': monthly_16_horizontal,
'Horizontal_18%': monthly_18_horizontal,
'Horizontal_14%': monthly_14_horizontal
})
combined_data_tilted = pd.DataFrame({
'Month': monthly_data.index.month,
'Tilted_16%': monthly_16_tilted,
'Tilted_18%': monthly_18_tilted,
'Tilted_14%': monthly_14_tilted
})
# Function to plot box and whisker plot
import numpy as np
# Function to plot box and whisker plot
import matplotlib.pyplot as plt
# Function to plot error bars
def plot_error_bars(data, title, ylabel):
fig, ax = plt.subplots(figsize=(14, 8))
months = range(1, 13)
means_16 = []
err_low = []
err_high = []
for month in months:
month_data = data[data['Month'] == month]
mean_16 = month_data.iloc[:, 1].mean()
low_14 = month_data.iloc[:, 3].mean()
high_18 = month_data.iloc[:, 2].mean()
means_16.append(mean_16)
err_low.append(mean_16 - low_14)
err_high.append(high_18 - mean_16)
ax.errorbar(months, means_16, yerr=[err_low, err_high], fmt='o', capsize=5, capthick=2, ecolor='gray')
ax.set_xticks(months)
ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
ax.set_xlabel('Month')
ax.set_ylabel(ylabel)
ax.set_title(title)
plt.show()
# Plot for Horizontal
plot_error_bars(combined_data_horizontal, 'Monthly District MWh (Horizontal)', 'MWh')
# Plot for Tilted
plot_error_bars(combined_data_tilted, 'Monthly District MWh (Tilted)', 'MWh')