diff --git a/main_results_in_geojson.py b/main_results_in_geojson.py index ed14e54d..33c0dd0f 100644 --- a/main_results_in_geojson.py +++ b/main_results_in_geojson.py @@ -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) diff --git a/plot.py b/plot.py new file mode 100644 index 00000000..ac850980 --- /dev/null +++ b/plot.py @@ -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')