20240731 set a city from a geojson file, plot monthly district PV with different efficiencies
This commit is contained in:
Andrea Gabaldon Moreno
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0ce76bef13
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1488c9dbce
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@ -16,12 +16,12 @@ import json
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input_files_path = (Path(__file__).parent / 'input_files')
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output_path = (Path(__file__).parent / 'out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
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geojson_file_path = output_path / 'updated_buildings_with_all_data.geojson'
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with open(geojson_file_path , 'r') as f:
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building_type_data = json.load(f)
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with open(geojson_file_path_baseline, 'r') as f:
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building_type_data_baseline = json.load(f)
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# Create city object from GeoJSON file
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city = GeometryFactory('geojson',
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@ -110,37 +110,30 @@ enrich_buildings_with_geojson_data (building_type_data, city)
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# building.appliances_electrical_demand[cte.YEAR] = [max(monthly_values) / cte.WATTS_HOUR_TO_JULES]
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print('test')
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 1 gas'
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# def to_dict(building, total_floor_area):
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# return {
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# 'roof_area': building.floor_area,
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# 'total_floor_area': total_floor_area,
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# 'year_of_construction' : building.year_of_construction,
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# 'type_function':building.function,
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# 'beam_kWh_per_m2': sum(building.beam[cte.HOUR])/ (3.6e6),
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# 'diffuse_kWh_per_m2': sum(building.diffuse[cte.HOUR])/ (3.6e6),
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# 'direct_normal_kWh_per_m2': sum(building.direct_normal[cte.HOUR])/ (3.6e6),
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# 'average_storey_height_meters': building.average_storey_height,
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# 'max_height_meters_meters': building.max_height,
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# 'global_horizontal_kWh_per_m2': sum(building.global_horizontal[cte.HOUR])/ (3.6e6),
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# 'appliances_peak_load_kW':building.appliances_peak_load[cte.YEAR][0]/ (1e3),
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# 'domestic_hot_water_peak_load_kW': building.domestic_hot_water_peak_load[cte.YEAR][0]/ (1e3),
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# 'heating_peak_load_kW': building.heating_peak_load[cte.YEAR][0]/ (1e3),
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# 'cooling_peak_load_kW': building.cooling_peak_load[cte.YEAR][0]/ (1e3),
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# 'lighting_peak_load_kW': building.lighting_peak_load[cte.YEAR][0]/ (1e3),
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# 'heating_demand_kWh_per_m2' : sum(building.heating_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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# 'cooling_demand_kWh_per_m2' : sum(building.cooling_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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# 'domestic_hot_water_heat_demand_kWh_per_m2': sum(building.domestic_hot_water_heat_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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# 'appliances_electrical_demand_kWh_per_m2':sum(building.appliances_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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# 'lighting_electrical_demand_kWh_per_m2': sum(building.lighting_electrical_demand[cte.HOUR])/ (3.6e6 * total_floor_area),
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# 'heating_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_demand[cte.HOUR]],
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# 'cooling_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
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# 'domestic_hot_water_heat_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
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# 'appliances_electrical_demand_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
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# 'lighting_electrical_demand_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
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# }
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# buildings_dic={}
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EnergySystemsFactory('montreal_custom', city).enrich()
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for building in city.buildings:
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energy_systems = building.energy_systems
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for energy_system in energy_systems:
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generation_units = energy_system.generation_systems
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if cte.HEATING in energy_system.demand_types:
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for generation_unit in generation_units:
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generation_unit.heat_efficiency = 0.96
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def to_dict(building, total_floor_area):
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return {
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'roof_area': building.floor_area,
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'total_floor_area': total_floor_area,
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'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
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# 'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
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# 'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
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# 'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
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# 'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
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}
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buildings_dic={}
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for building in city.buildings:
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@ -149,23 +142,9 @@ for building in city.buildings:
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for thermal_zone in building.thermal_zones_from_internal_zones:
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total_floor_area += thermal_zone.total_floor_area
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print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
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building.energy_systems_archetype_name = 'system 1 gas'
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EnergySystemsFactory('montreal_custom', city).enrich()
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print('test')
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for building in city.buildings:
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energy_systems = building.energy_systems
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for energy_system in energy_systems:
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generation_units = energy_system.generation_systems
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if cte.HEATING in energy_system.demand_types:
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for generation_unit in generation_units:
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generation_unit.heat_efficiency = 0.96
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# for building in city.buildings:
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# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
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# print(building.name)
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79
plot.py
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79
plot.py
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@ -0,0 +1,79 @@
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import pandas as pd
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import matplotlib.pyplot as plt
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# Load the CSV file
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file_path = r'C:\Users\a_gabald\Desktop\solar_different_efficiencies.csv' # Replace with your file path
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data = pd.read_csv(file_path)
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# Assuming the CSV has a datetime column, parse it
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data['datetime'] = pd.date_range(start='1/1/2023', periods=8760, freq='h')
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# Set the datetime column as the index
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data.set_index('datetime', inplace=True)
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# Resample the data to monthly data
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monthly_data = data.resample('ME').sum()
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# Extract data for different efficiencies
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monthly_16_horizontal = monthly_data['district MWh Horizontal_16%']
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monthly_18_horizontal = monthly_data['district MWh Horizontal_18%']
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monthly_14_horizontal = monthly_data['district MWh Horizontal_14%']
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monthly_16_tilted = monthly_data['district MWh tilted_16%']
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monthly_18_tilted = monthly_data['district MWh tilted_18%']
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monthly_14_tilted = monthly_data['district MWh tilted_14%']
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# Combine the data into a single DataFrame for easier plotting
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combined_data_horizontal = pd.DataFrame({
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'Month': monthly_data.index.month,
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'Horizontal_16%': monthly_16_horizontal,
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'Horizontal_18%': monthly_18_horizontal,
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'Horizontal_14%': monthly_14_horizontal
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})
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combined_data_tilted = pd.DataFrame({
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'Month': monthly_data.index.month,
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'Tilted_16%': monthly_16_tilted,
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'Tilted_18%': monthly_18_tilted,
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'Tilted_14%': monthly_14_tilted
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})
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# Function to plot box and whisker plot
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import numpy as np
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# Function to plot box and whisker plot
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import matplotlib.pyplot as plt
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# Function to plot error bars
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def plot_error_bars(data, title, ylabel):
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fig, ax = plt.subplots(figsize=(14, 8))
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months = range(1, 13)
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means_16 = []
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err_low = []
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err_high = []
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for month in months:
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month_data = data[data['Month'] == month]
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mean_16 = month_data.iloc[:, 1].mean()
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low_14 = month_data.iloc[:, 3].mean()
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high_18 = month_data.iloc[:, 2].mean()
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means_16.append(mean_16)
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err_low.append(mean_16 - low_14)
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err_high.append(high_18 - mean_16)
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ax.errorbar(months, means_16, yerr=[err_low, err_high], fmt='o', capsize=5, capthick=2, ecolor='gray')
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ax.set_xticks(months)
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ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
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ax.set_xlabel('Month')
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ax.set_ylabel(ylabel)
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ax.set_title(title)
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plt.show()
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# Plot for Horizontal
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plot_error_bars(combined_data_horizontal, 'Monthly District MWh (Horizontal)', 'MWh')
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# Plot for Tilted
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plot_error_bars(combined_data_tilted, 'Monthly District MWh (Tilted)', 'MWh')
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