341 lines
15 KiB
Python
341 lines
15 KiB
Python
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import os
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import hub.helpers.constants as cte
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import matplotlib.pyplot as plt
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import random
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import matplotlib.colors as mcolors
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from matplotlib import cm
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from report_creation import LatexReport
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class EnergySystemAnalysisReport:
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def __init__(self, city, output_path):
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self.city = city
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self.output_path = output_path
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self.content = []
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self.report = LatexReport('energy_system_analysis_report.tex')
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def building_energy_info(self):
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table_data = [
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["Building Name", "Year of Construction", "function", "Yearly Heating Demand (MWh)",
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"Yearly Cooling Demand (MWh)", "Yearly DHW Demand (MWh)", "Yearly Electricity Demand (MWh)"]
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]
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intensity_table_data = [["Building Name", "Total Floor Area m2", "Heating Demand Intensity kWh/m2",
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"Cooling Demand Intensity kWh/m2", "Electricity Intensity kWh/m2"]]
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for building in self.city.buildings:
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total_floor_area = 0
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for zone in building.thermal_zones_from_internal_zones:
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total_floor_area += zone.total_floor_area
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building_data = [
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building.name,
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str(building.year_of_construction),
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building.function,
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str(format(building.heating_demand[cte.YEAR][0] / 1e6, '.2f')),
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str(format(building.cooling_demand[cte.YEAR][0] / 1e6, '.2f')),
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str(format(building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1e6, '.2f')),
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str(format((building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) / 1e6, '.2f')),
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]
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intensity_data = [
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building.name,
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str(format(total_floor_area, '.2f')),
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str(format(building.heating_demand[cte.YEAR][0] / (1e3 * total_floor_area), '.2f')),
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str(format(building.cooling_demand[cte.YEAR][0] / (1e3 * total_floor_area), '.2f')),
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str(format(
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(building.lighting_electrical_demand[cte.YEAR][0] + building.appliances_electrical_demand[cte.YEAR][0]) /
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(1e3 * total_floor_area), '.2f'))
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]
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table_data.append(building_data)
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intensity_table_data.append(intensity_data)
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self.report.add_table(table_data, caption='City Buildings Energy Demands')
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self.report.add_table(intensity_table_data, caption='Energy Intensity Information')
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def base_case_charts(self):
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save_directory = self.output_path
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def autolabel(bars, ax):
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for bar in bars:
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height = bar.get_height()
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ax.annotate('{:.1f}'.format(height),
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), # 3 points vertical offset
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textcoords="offset points",
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ha='center', va='bottom')
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def create_hvac_demand_chart(building_names, yearly_heating_demand, yearly_cooling_demand):
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fig, ax = plt.subplots()
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bar_width = 0.35
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index = range(len(building_names))
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bars1 = ax.bar(index, yearly_heating_demand, bar_width, label='Yearly Heating Demand (MWh)')
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bars2 = ax.bar([i + bar_width for i in index], yearly_cooling_demand, bar_width,
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label='Yearly Cooling Demand (MWh)')
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ax.set_xlabel('Building Name')
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ax.set_ylabel('Energy Demand (MWh)')
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ax.set_title('Yearly HVAC Demands')
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ax.set_xticks([i + bar_width / 2 for i in index])
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ax.set_xticklabels(building_names, rotation=45, ha='right')
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ax.legend()
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autolabel(bars1, ax)
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autolabel(bars2, ax)
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fig.tight_layout()
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plt.savefig(save_directory / 'hvac_demand_chart.jpg')
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plt.close()
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def create_bar_chart(title, ylabel, data, filename, bar_color=None):
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fig, ax = plt.subplots()
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bar_width = 0.35
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index = range(len(building_names))
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if bar_color is None:
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# Generate a random color
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bar_color = random.choice(list(mcolors.CSS4_COLORS.values()))
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bars = ax.bar(index, data, bar_width, label=ylabel, color=bar_color)
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ax.set_xlabel('Building Name')
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ax.set_ylabel('Energy Demand (MWh)')
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ax.set_title(title)
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ax.set_xticks([i + bar_width / 2 for i in index])
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ax.set_xticklabels(building_names, rotation=45, ha='right')
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ax.legend()
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autolabel(bars, ax)
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fig.tight_layout()
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plt.savefig(save_directory / filename)
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plt.close()
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building_names = [building.name for building in self.city.buildings]
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yearly_heating_demand = [building.heating_demand[cte.YEAR][0] / 1e6 for building in self.city.buildings]
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yearly_cooling_demand = [building.cooling_demand[cte.YEAR][0] / 1e6 for building in self.city.buildings]
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yearly_dhw_demand = [building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1e6 for building in
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self.city.buildings]
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yearly_electricity_demand = [(building.lighting_electrical_demand[cte.YEAR][0] +
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building.appliances_electrical_demand[cte.YEAR][0]) / 1e6 for building in
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self.city.buildings]
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create_hvac_demand_chart(building_names, yearly_heating_demand, yearly_cooling_demand)
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create_bar_chart('Yearly DHW Demands', 'Energy Demand (MWh)', yearly_dhw_demand, 'dhw_demand_chart.jpg', )
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create_bar_chart('Yearly Electricity Demands', 'Energy Demand (MWh)', yearly_electricity_demand,
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'electricity_demand_chart.jpg')
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def maximum_monthly_hvac_chart(self):
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save_directory = self.output_path
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months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October',
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'November', 'December']
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for building in self.city.buildings:
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maximum_monthly_heating_load = []
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maximum_monthly_cooling_load = []
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fig, axs = plt.subplots(1, 2, figsize=(12, 6)) # Create a figure with 2 subplots side by side
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for demand in building.heating_peak_load[cte.MONTH]:
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maximum_monthly_heating_load.append(demand / 3.6e6)
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for demand in building.cooling_peak_load[cte.MONTH]:
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maximum_monthly_cooling_load.append(demand / 3.6e6)
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# Plot maximum monthly heating load
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axs[0].bar(months, maximum_monthly_heating_load, color='red') # Plot on the first subplot
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axs[0].set_title('Maximum Monthly Heating Load')
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axs[0].set_xlabel('Month')
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axs[0].set_ylabel('Load (kW)')
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axs[0].tick_params(axis='x', rotation=45)
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# Plot maximum monthly cooling load
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axs[1].bar(months, maximum_monthly_cooling_load, color='blue') # Plot on the second subplot
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axs[1].set_title('Maximum Monthly Cooling Load')
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axs[1].set_xlabel('Month')
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axs[1].set_ylabel('Load (kW)')
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axs[1].tick_params(axis='x', rotation=45)
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plt.tight_layout() # Adjust layout to prevent overlapping
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plt.savefig(save_directory / f'{building.name}_monthly_maximum_hvac_loads.jpg')
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plt.close()
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def load_duration_curves(self):
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save_directory = self.output_path
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for building in self.city.buildings:
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heating_demand = [demand / 1000 for demand in building.heating_demand[cte.HOUR]]
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cooling_demand = [demand / 1000 for demand in building.cooling_demand[cte.HOUR]]
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heating_demand_sorted = sorted(heating_demand, reverse=True)
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cooling_demand_sorted = sorted(cooling_demand, reverse=True)
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plt.style.use('ggplot')
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# Create figure and axis objects with 1 row and 2 columns
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fig, axs = plt.subplots(1, 2, figsize=(12, 6))
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# Plot sorted heating demand
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axs[0].plot(heating_demand_sorted, color='red', linewidth=2, label='Heating Demand')
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axs[0].set_xlabel('Hour', fontsize=14)
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axs[0].set_ylabel('Heating Demand', fontsize=14)
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axs[0].set_title('Heating Load Duration Curve', fontsize=16)
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axs[0].grid(True)
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axs[0].legend(loc='upper right', fontsize=12)
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# Plot sorted cooling demand
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axs[1].plot(cooling_demand_sorted, color='blue', linewidth=2, label='Cooling Demand')
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axs[1].set_xlabel('Hour', fontsize=14)
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axs[1].set_ylabel('Cooling Demand', fontsize=14)
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axs[1].set_title('Cooling Load Duration Curve', fontsize=16)
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axs[1].grid(True)
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axs[1].legend(loc='upper right', fontsize=12)
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# Adjust layout
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plt.tight_layout()
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# Save the plot
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plt.savefig(save_directory / f'{building.name}_load_duration_curve.jpg')
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# Close the plot to release memory
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plt.close()
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def individual_building_info(self, building):
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table_data = [
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["Maximum Monthly HVAC Demands",
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f"\\includegraphics[width=1\\linewidth]{{{building.name}_monthly_maximum_hvac_loads.jpg}}"],
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["Load Duration Curve", f"\\includegraphics[width=1\\linewidth]{{{building.name}_load_duration_curve.jpg}}"],
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]
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self.report.add_table(table_data, caption=f'{building.name} Information', first_column_width=1.5)
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def building_existing_system_info(self, building):
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existing_archetype = building.energy_systems_archetype_name
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fuels = []
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system_schematic = "-"
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heating_system = "-"
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cooling_system = "-"
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dhw = "-"
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electricity = "Grid"
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hvac_ec = format((building.heating_consumption[cte.YEAR][0] + building.cooling_consumption[cte.YEAR][0])/1e6, '.2f')
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dhw_ec = format(building.domestic_hot_water_consumption[cte.YEAR][0]/1e6, '.2f')
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on_site_generation = "-"
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yearly_operational_cost = "-"
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life_cycle_cost = "-"
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for energy_system in building.energy_systems:
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if cte.HEATING and cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
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heating_system = energy_system.name
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dhw = energy_system.name
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elif cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
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dhw = energy_system.name
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elif cte.HEATING in energy_system.demand_types:
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heating_system = energy_system.name
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elif cte.COOLING in energy_system.demand_types:
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cooling_system = energy_system.name
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for generation_system in energy_system.generation_systems:
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fuels.append(generation_system.fuel_type)
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if generation_system.system_type == cte.PHOTOVOLTAIC:
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electricity = "Grid-tied PV"
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energy_system_table_data = [
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["Detail", "Existing System", "Proposed System"],
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["Energy System Archetype", existing_archetype, "-"],
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["System Schematic", system_schematic, system_schematic],
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["Heating System", heating_system, "-"],
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["Cooling System", cooling_system, "-"],
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["DHW System", dhw, "-"],
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["Electricity", electricity, "-"],
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["Fuel(s)", str(fuels), "-"],
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["HVAC Energy Consumption (MWh)", hvac_ec, "-"],
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["DHW Energy Consumption (MWH)", dhw_ec, "-"],
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["Yearly Operational Cost (CAD)", yearly_operational_cost, "-"],
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["Life Cycle Cost (CAD)", life_cycle_cost, "-"]
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]
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self.report.add_table(energy_system_table_data, caption= f'Building {building.name} Energy System Characteristics')
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def building_fuel_consumption_breakdown(self, building):
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save_directory = self.output_path
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# Initialize variables to store fuel consumption breakdown
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fuel_breakdown = {
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"Heating": {"Gas": 0, "Electricity": 0},
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"Domestic Hot Water": {"Gas": 0, "Electricity": 0},
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"Cooling": {"Electricity": 0},
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"Appliance": building.appliances_electrical_demand[cte.YEAR][0] / 1e6,
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"Lighting": building.lighting_electrical_demand[cte.YEAR][0] / 1e6
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}
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# Iterate through energy systems of the building
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for energy_system in building.energy_systems:
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for demand_type in energy_system.demand_types:
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for generation_system in energy_system.generation_systems:
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consumption = 0
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if demand_type == cte.HEATING:
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consumption = building.heating_consumption[cte.YEAR][0] / 1e6
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elif demand_type == cte.DOMESTIC_HOT_WATER:
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consumption = building.domestic_hot_water_consumption[cte.YEAR][0] / 1e6
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elif demand_type == cte.COOLING:
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consumption = building.cooling_consumption[cte.YEAR][0] / 1e6
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if generation_system.fuel_type == cte.ELECTRICITY:
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fuel_breakdown[demand_type]["Electricity"] += consumption
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else:
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fuel_breakdown[demand_type]["Gas"] += consumption
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electricity_labels = ['Appliance', 'Lighting']
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electricity_sizes = [fuel_breakdown['Appliance'], fuel_breakdown['Lighting']]
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if fuel_breakdown['Heating']['Electricity'] > 0:
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electricity_labels.append('Heating')
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electricity_sizes.append(fuel_breakdown['Heating']['Electricity'])
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if fuel_breakdown['Cooling']['Electricity'] > 0:
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electricity_labels.append('Cooling')
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electricity_sizes.append(fuel_breakdown['Cooling']['Electricity'])
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if fuel_breakdown['Domestic Hot Water']['Electricity'] > 0:
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electricity_labels.append('Domestic Hot Water')
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electricity_sizes.append(fuel_breakdown['Domestic Hot Water']['Electricity'])
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# Data for bar chart
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gas_labels = ['Heating', 'Domestic Hot Water']
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gas_sizes = [fuel_breakdown['Heating']['Gas'], fuel_breakdown['Domestic Hot Water']['Gas']]
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# Set the style
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plt.style.use('ggplot')
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# Create plot grid
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fig, axs = plt.subplots(1, 2, figsize=(12, 6))
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# Plot pie chart for electricity consumption breakdown
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colors = cm.get_cmap('tab20c', len(electricity_labels))
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axs[0].pie(electricity_sizes, labels=electricity_labels,
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autopct=lambda pct: f"{pct:.1f}%\n({pct / 100 * sum(electricity_sizes):.2f})",
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startangle=90, colors=[colors(i) for i in range(len(electricity_labels))])
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axs[0].set_title('Electricity Consumption Breakdown')
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# Plot bar chart for natural gas consumption breakdown
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colors = cm.get_cmap('Paired', len(gas_labels))
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axs[1].bar(gas_labels, gas_sizes, color=[colors(i) for i in range(len(gas_labels))])
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axs[1].set_ylabel('Consumption (MWh)')
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axs[1].set_title('Natural Gas Consumption Breakdown')
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# Add grid to bar chart
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axs[1].grid(axis='y', linestyle='--', alpha=0.7)
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# Add a title to the entire figure
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||
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plt.suptitle('Building Energy Consumption Breakdown', fontsize=16, fontweight='bold')
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||
|
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# Adjust layout
|
||
|
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plt.tight_layout()
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|
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||
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# Save the plot as a high-quality image
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||
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plt.savefig(save_directory / f'{building.name}_energy_consumption_breakdown.png', dpi=300)
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|
|
plt.close()
|
||
|
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||
|
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def create_report(self):
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os.chdir(self.output_path)
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||
|
|
self.report.add_section('Current Status')
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||
|
|
self.building_energy_info()
|
||
|
|
self.base_case_charts()
|
||
|
|
self.report.add_image('hvac_demand_chart.jpg', caption='Yearly HVAC Demands')
|
||
|
|
self.report.add_image('dhw_demand_chart.jpg', caption='Yearly DHW Demands')
|
||
|
|
self.report.add_image('electricity_demand_chart.jpg', caption='Yearly Electricity Demands')
|
||
|
|
self.maximum_monthly_hvac_chart()
|
||
|
|
self.load_duration_curves()
|
||
|
|
for building in self.city.buildings:
|
||
|
|
self.individual_building_info(building)
|
||
|
|
self.building_existing_system_info(building)
|
||
|
|
self.building_fuel_consumption_breakdown(building)
|
||
|
|
self.report.add_image(f'{building.name}_energy_consumption_breakdown.png',
|
||
|
|
caption=f'Building {building.name} Consumption by source and sector breakdown')
|
||
|
|
self.report.save_report()
|
||
|
|
self.report.compile_to_pdf()
|
||
|
|
|
||
|
|
|
||
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