s_ranjbar
05d88e2461
fix: simulation models and cop curves modified and finalized fix: all system simulation models are fixed
86 lines
4.6 KiB
Python
86 lines
4.6 KiB
Python
from pathlib import Path
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import subprocess
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from scripts.ep_run_enrich import energy_plus_workflow
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.results_factory import ResultFactory
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from scripts.energy_system_retrofit_report import EnergySystemRetrofitReport
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from scripts.geojson_creator import process_geojson
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from scripts import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from scripts.energy_system_sizing import SystemSizing
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from scripts.solar_angles import CitySolarAngles
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from scripts.pv_sizing_and_simulation import PVSizingSimulation
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from scripts.energy_system_retrofit_results import consumption_data, cost_data
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.costs.cost import Cost
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from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
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import hub.helpers.constants as cte
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from hub.exports.exports_factory import ExportsFactory
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from scripts.pv_feasibility import pv_feasibility
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import matplotlib.pyplot as plt
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import numpy as np
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# Specify the GeoJSON file path
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data = {}
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input_files_path = (Path(__file__).parent / 'input_files')
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input_files_path.mkdir(parents=True, exist_ok=True)
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# geojson_file = process_geojson(x=-73.58001358793511, y=45.496445294438715, diff=0.0001)
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geojson_file_path = input_files_path / 'test_geojson1.geojson'
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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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energy_plus_output_path = output_path / 'energy_plus_outputs'
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energy_plus_output_path.mkdir(parents=True, exist_ok=True)
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simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
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simulation_results_path.mkdir(parents=True, exist_ok=True)
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sra_output_path = output_path / 'sra_outputs'
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sra_output_path.mkdir(parents=True, exist_ok=True)
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cost_analysis_output_path = output_path / 'cost_analysis'
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cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
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city = GeometryFactory(file_type='geojson',
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path=geojson_file_path,
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height_field='height',
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year_of_construction_field='year_of_construction',
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function_field='function',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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# ConstructionFactory('nrcan', city).enrich()
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# UsageFactory('nrcan', city).enrich()
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# WeatherFactory('epw', city).enrich()
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# energy_plus_workflow(city, energy_plus_output_path)
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# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
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# city.buildings[0].function = cte.COMMERCIAL
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# ConstructionFactory('nrcan', city).enrich()
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# UsageFactory('nrcan', city).enrich()
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# energy_plus_workflow(city, energy_plus_output_path)
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# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
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# city.buildings[0].function = cte.MEDIUM_OFFICE
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# ConstructionFactory('nrcan', city).enrich()
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# UsageFactory('nrcan', city).enrich()
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# energy_plus_workflow(city, energy_plus_output_path)
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# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
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# categories = list(data.keys())
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# values = list(data.values())
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# # Plotting
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# fig, ax = plt.subplots(figsize=(10, 6), dpi=96)
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# fig.suptitle('Impact of different usages on yearly heating demand', fontsize=16, weight='bold', alpha=.8)
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# ax.bar(categories, values, color=['#2196f3', '#ff5a5f', '#4caf50'], width=0.6, zorder=2)
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# ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
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# ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
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# ax.set_xlabel('Building Type', fontsize=12, labelpad=10)
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# ax.set_ylabel('Energy Consumption (MWh)', fontsize=14, labelpad=10)
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# ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
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# ax.set_xticks(np.arange(len(categories)))
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# ax.set_xticklabels(categories, rotation=45, ha='right')
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# ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=0)
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# ax.spines[['top', 'left', 'bottom']].set_visible(False)
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# ax.spines['right'].set_linewidth(1.1)
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# # Set a white background
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# fig.patch.set_facecolor('white')
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# # Adjust the margins around the plot area
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# plt.subplots_adjust(left=0.1, right=0.9, top=0.85, bottom=0.25)
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# # Save the plot
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# plt.savefig('plot_nrcan.png', bbox_inches='tight')
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# plt.close()
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print('test') |