20240808 final model, archetype 13 is changed by commenting the lines of the curve coefficients and to include as input t_out = 15; main processes 4 scenarios
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cluster_demands.xlsx
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cluster_demands.xlsx
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clusters.py
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clusters.py
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@ -0,0 +1,124 @@
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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def extract_building_data(scenario, demand_types):
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"""
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Extracts energy demand data for each building from the scenario dictionary.
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Args:
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scenario (dict): Scenario dictionary containing building data.
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demand_types (list): List of demand types to extract.
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Returns:
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pd.DataFrame: DataFrame with buildings as rows and demand types as columns.
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"""
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data = []
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building_ids = []
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for building_id, demand_data in scenario.items():
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building_row = []
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for demand_type in demand_types:
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building_row.extend(demand_data[demand_type])
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data.append(building_row)
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building_ids.append(building_id)
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return pd.DataFrame(data, index=building_ids,
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columns=[f"{demand_type}_{i}" for demand_type in demand_types for i in range(8760)])
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# Plot the data
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print('test')
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def cluster_buildings(scenario, demand_types, n_clusters=4,n_components=2):
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"""
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Clusters buildings based on their energy demand.
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Args:
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scenario (dict): Scenario dictionary containing building data.
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demand_types (list): List of demand types to use for clustering.
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n_clusters (int): Number of clusters to form.
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Returns:
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pd.DataFrame: DataFrame with building IDs and their corresponding cluster labels.
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"""
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# Extract building data
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building_data = extract_building_data(scenario, demand_types)
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# Standardize the data
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scaler = StandardScaler()
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building_data_scaled = scaler.fit_transform(building_data)
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# Optional: Dimensionality reduction with PCA
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pca = PCA(n_components)
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building_data_pca = pca.fit_transform(building_data_scaled)
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# Clustering with K-Means
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kmeans = KMeans(n_clusters=n_clusters, random_state=42)
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kmeans.fit(building_data_pca)
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# Assign cluster labels to buildings
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clusters = kmeans.labels_
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# Create a DataFrame with building IDs and cluster labels
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clustered_buildings = pd.DataFrame({
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'building_id': building_data.index,
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'cluster': clusters
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})
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return clustered_buildings
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# Example usage
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scenario_data = scenario['efficient with PV+4Pipe+DHW'] # Use 'baseline' or 'efficient with PV'
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demand_types = [
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'heating_consumption_kWh'
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]
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# Cluster buildings
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clustered_buildings = cluster_buildings(scenario_data, demand_types, n_clusters=4,n_components=20)
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# Visualize clusters
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plt.figure(figsize=(10, 6))
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plt.scatter(clustered_buildings['building_id'], clustered_buildings['cluster'], c=clustered_buildings['cluster'],
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cmap='viridis')
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plt.xlabel('Building ID')
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plt.ylabel('Cluster')
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plt.title('Building Clusters Based on Energy Demand')
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plt.colorbar(label='Cluster')
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# Save the plot
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plt.savefig(os.path.join(output_path, 'clusters.png'))
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plt.close()
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# Plot the data
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print('test')
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output_path_clusters = output_path / 'clustered_buildings_4.xlsx'
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clustered_buildings.to_excel(output_path_clusters, index=False)
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#clusters made in QGIS
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clusters_path=output_path/ "clusters" / 'updated_buildings_with_clusters.geojson'
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with open(clusters_path, 'r') as f:
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clusters = json.load(f)
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def extract_cluster(key, clusters):
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for idx, feature in enumerate(clusters['features']):
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if str(feature['properties']['id']) == str(key):
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cluster=feature['properties']['Cluster_ID']
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return cluster
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cluster_dic={}
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scenario_data = scenario['efficient with PV+4Pipe+DHW']
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for key, data in scenario_data.items():
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cluster_dic[key]=extract_cluster(key, clusters)
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#
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# cluster_df = pd.DataFrame.from_dict(cluster_dic, orient='index')
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# cluster_df.to_csv(output_path/'clusters.csv')
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16
comparing.py
16
comparing.py
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@ -52,14 +52,6 @@ percentage_data = {
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1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
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1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
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1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
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1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
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}
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}
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# Define the demand types to be plotted
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demand_types = [
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'heating_demand_kWh',
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'cooling_demand_kWh',
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'domestic_hot_water_heat_demand_kWh',
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'appliances_electrical_demand_kWh',
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'lighting_electrical_demand_kWh'
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]
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# # Function to extract demand data from the GeoJSON
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# # Function to extract demand data from the GeoJSON
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# def extract_demand_data(geojson_data, demand_type, period_start, period_end):
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# def extract_demand_data(geojson_data, demand_type, period_start, period_end):
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@ -122,6 +114,14 @@ demand_types = [
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# plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}')
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# plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}')
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#
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#
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# Define the demand types to be plotted
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demand_types = [
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'heating_demand_kWh',
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'cooling_demand_kWh',
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'domestic_hot_water_heat_demand_kWh',
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'appliances_electrical_demand_kWh',
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'lighting_electrical_demand_kWh'
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]
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# Function to extract and sum demand data from the GeoJSON
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# Function to extract and sum demand data from the GeoJSON
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def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end):
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def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end):
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@ -810,37 +810,37 @@ class Building(CityObject):
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Get total electricity produced onsite in J
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Get total electricity produced onsite in J
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return: dict
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return: dict
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"""
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"""
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orientation_losses_factor = {cte.MONTH: {'north': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# orientation_losses_factor = {cte.MONTH: {'north': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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'east': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# 'east': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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'south': [2.137931, 1.645503, 1.320946, 1.107817, 0.993213, 0.945175,
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# 'south': [2.137931, 1.645503, 1.320946, 1.107817, 0.993213, 0.945175,
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0.967949, 1.065534, 1.24183, 1.486486, 1.918033, 2.210526],
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# 0.967949, 1.065534, 1.24183, 1.486486, 1.918033, 2.210526],
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'west': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},
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# 'west': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},
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cte.YEAR: {'north': [0],
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# cte.YEAR: {'north': [0],
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'east': [0],
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# 'east': [0],
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'south': [1.212544],
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# 'south': [1.212544],
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'west': [0]}
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# 'west': [0]}
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}
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# }
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#
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# Add other systems whenever new ones appear
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# # Add other systems whenever new ones appear
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if self.energy_systems is None:
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# if self.energy_systems is None:
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return self._onsite_electrical_production
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# return self._onsite_electrical_production
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for energy_system in self.energy_systems:
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# for energy_system in self.energy_systems:
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for generation_system in energy_system.generation_systems:
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# for generation_system in energy_system.generation_systems:
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if generation_system.system_type == cte.PHOTOVOLTAIC:
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# if generation_system.system_type == cte.PHOTOVOLTAIC:
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if generation_system.electricity_efficiency is not None:
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# if generation_system.electricity_efficiency is not None:
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_efficiency = float(generation_system.electricity_efficiency)
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# _efficiency = float(generation_system.electricity_efficiency)
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else:
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# else:
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_efficiency = 0
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# _efficiency = 0
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self._onsite_electrical_production = {}
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# self._onsite_electrical_production = {}
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for _key in self.roofs[0].global_irradiance.keys():
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# for _key in self.roofs[0].global_irradiance.keys():
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_results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
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# _results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
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for surface in self.roofs:
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# for surface in self.roofs:
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if _key in orientation_losses_factor:
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# if _key in orientation_losses_factor:
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_results = [x + y * _efficiency * surface.perimeter_area
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# _results = [x + y * _efficiency * surface.perimeter_area
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* surface.solar_collectors_area_reduction_factor * z
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# * surface.solar_collectors_area_reduction_factor * z
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for x, y, z in zip(_results, surface.global_irradiance[_key],
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# for x, y, z in zip(_results, surface.global_irradiance[_key],
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orientation_losses_factor[_key]['south'])]
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# orientation_losses_factor[_key]['south'])]
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self._onsite_electrical_production[_key] = _results
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# self._onsite_electrical_production[_key] = _results
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return self._onsite_electrical_production
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return self._onsite_electrical_production
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@property
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@property
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<demand>cooling</demand>
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<demand>cooling</demand>
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</demands>
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</demands>
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<components>
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<components>
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<generation_id>23</generation_id>
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<generation_id>25</generation_id>
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<generation_id>16</generation_id>
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<generation_id>17</generation_id>
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</components>
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</components>
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</system>
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</system>
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<system>
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<system>
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662
main.py
662
main.py
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@ -14,13 +14,14 @@ import subprocess
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from hub.imports.results_factory import ResultFactory
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from hub.imports.results_factory import ResultFactory
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
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from scripts.solar_angles import CitySolarAngles
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import hub.helpers.constants as cte
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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 hub.exports.exports_factory import ExportsFactory
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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.radiation_tilted import RadiationTilted
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import pandas as pd
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import geopandas as gpd
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import geopandas as gpd
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import json
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import json
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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# Specify the GeoJSON file path
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# Specify the GeoJSON file path
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
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geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
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geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data.geojson'
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geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data_2024.geojson'
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with open(geojson_file_path_baseline , 'r') as f:
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with open(geojson_file_path_baseline , 'r') as f:
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building_type_data = json.load(f)
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building_type_data = json.load(f)
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with open(geojson_file_path_2024, 'r') as f:
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with open(geojson_file_path_2024, 'r') as f:
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year_of_construction_field='year_built',
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year_of_construction_field='year_built',
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function_field='building_type',
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function_field='building_type',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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#%%-----------------------------------------------
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#%%----------------------------------------------
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# Enrich city data
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# Enrich city data
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#%% # -----------------------------------------------
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#%% # ----------------------------------------------
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ConstructionFactory('nrcan', city).enrich()
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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WeatherFactory('epw', city).enrich()
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# #energy plus is not going to be processed here, as demand has been obtained before
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# #energy plus is not going to be processed here, as demand has been obtained before
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# energy_plus_workflow(city)
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# energy_plus_workflow(city)
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#%%-----------------------------------------------
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# """SRA"""
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#%% # -----------------------------------------------
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ExportsFactory('sra', city, sra_output_path).export()
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sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', city, sra_output_path).enrich()
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solar_angles = CitySolarAngles(city.name,
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city.latitude,
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city.longitude,
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tilt_angle=45,
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surface_azimuth_angle=180).calculate
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for building in city.buildings:
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ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
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RadiationTilted(building,
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solar_angles,
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tilt_angle=45,
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ghi=ghi).enrich()
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# building_names = []
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# for building in city.buildings:
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# building_names.append(building.name)
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#
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# df = pd.DataFrame(columns=building_names)
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# df1 = pd.DataFrame(columns=building_names)
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# print('test')
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# for building in city.buildings:
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# # if building.name in selected_buildings_list:
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# df[f'{building.name}'] = building.roofs[0].global_irradiance[cte.HOUR]
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# df1[f'{building.name}'] = building.roofs[0].global_irradiance_tilted[cte.HOUR]
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#
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# df.to_csv('solar_radiation_horizontal_selected_buildings.csv')
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# df1.to_csv('solar_radiation_tilted_selected_buildings.csv')
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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#"""Enrich city with geojson file data"""
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#"""Enrich city with geojson file data"""
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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@ -169,50 +137,616 @@ print('test')
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#%% # -----------------------------------------------
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#%% # -----------------------------------------------
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for building in city.buildings:
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for building in city.buildings:
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building.energy_systems_archetype_name = 'system 1 electricity pv'
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building.energy_systems_archetype_name = 'system 1 electricity'
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EnergySystemsFactory('montreal_custom', city).enrich()
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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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def baseline_to_dict(building):
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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:
|
|
||||||
# for generation_unit in generation_units:
|
|
||||||
# generation_unit.heat_efficiency = 0.96
|
|
||||||
def to_dict(building, total_floor_area):
|
|
||||||
return {
|
return {
|
||||||
'roof_area': building.floor_area,
|
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
|
||||||
'total_floor_area': total_floor_area,
|
'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_consumption[cte.HOUR]],
|
||||||
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
|
'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[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]],
|
'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]]
|
'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
|
||||||
}
|
}
|
||||||
buildings_dic={}
|
buildings_dic={}
|
||||||
|
for building in city.buildings:
|
||||||
|
buildings_dic[building.name]=baseline_to_dict(building)
|
||||||
|
scenario={}
|
||||||
|
|
||||||
|
scenario['baseline']=buildings_dic
|
||||||
|
print("Scenario 1: Baseline is performed successfully")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
del city
|
||||||
|
del buildings_dic
|
||||||
|
del building_type_data
|
||||||
|
|
||||||
|
#%%-----------------------------------------------
|
||||||
|
# Scenario 2
|
||||||
|
#%% # -----------------------------------------------
|
||||||
|
|
||||||
|
# Create city object from GeoJSON file
|
||||||
|
city = GeometryFactory('geojson',
|
||||||
|
path=geojson_file_path_2024,
|
||||||
|
height_field='maximum_roof_height',
|
||||||
|
year_of_construction_field='year_built',
|
||||||
|
function_field='building_type',
|
||||||
|
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||||
|
#%%-----------------------------------------------
|
||||||
|
# Enrich city data
|
||||||
|
#%% # -----------------------------------------------
|
||||||
|
ConstructionFactory('nrcan', city).enrich()
|
||||||
|
UsageFactory('nrcan', city).enrich()
|
||||||
|
WeatherFactory('epw', city).enrich()
|
||||||
|
|
||||||
|
enrich_buildings_with_geojson_data (building_type_data_2024, city)
|
||||||
|
|
||||||
|
def to_dict(building,hourly_pv):
|
||||||
|
return {
|
||||||
|
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
|
||||||
|
'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_consumption[cte.HOUR]],
|
||||||
|
'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[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]],
|
||||||
|
'hourly_pv_kWh': [x /(cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_pv]
|
||||||
|
}
|
||||||
|
buildings_dic={}
|
||||||
|
|
||||||
|
|
||||||
for building in city.buildings:
|
for building in city.buildings:
|
||||||
|
building.energy_systems_archetype_name = 'system 1 electricity pv'
|
||||||
|
|
||||||
total_floor_area = 0
|
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||||
for thermal_zone in building.thermal_zones_from_internal_zones:
|
# #%%-----------------------------------------------
|
||||||
total_floor_area += thermal_zone.total_floor_area
|
# # """SRA"""
|
||||||
|
# #%% # -----------------------------------------------
|
||||||
|
ExportsFactory('sra', city, output_path).export()
|
||||||
|
sra_path = (output_path / f'{city.name}_sra.xml').resolve()
|
||||||
|
subprocess.run(['sra', str(sra_path)])
|
||||||
|
ResultFactory('sra', city, output_path).enrich()
|
||||||
|
solar_angles = CitySolarAngles(city.name,
|
||||||
|
city.latitude,
|
||||||
|
city.longitude,
|
||||||
|
tilt_angle=45,
|
||||||
|
surface_azimuth_angle=180).calculate
|
||||||
|
df = pd.DataFrame()
|
||||||
|
df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
|
||||||
|
'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
|
||||||
|
for building in city.buildings:
|
||||||
|
ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
|
||||||
|
pv_sizing_simulation = PVSizingSimulation(building,
|
||||||
|
solar_angles,
|
||||||
|
tilt_angle=45,
|
||||||
|
module_height=1,
|
||||||
|
module_width=2,
|
||||||
|
ghi=ghi)
|
||||||
|
pv_sizing_simulation.pv_output()
|
||||||
|
yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
|
||||||
|
yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
|
||||||
|
yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
|
||||||
|
yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
|
||||||
|
yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
|
||||||
|
roof_area = building.roofs[0].perimeter_area
|
||||||
|
used_roof = pv_sizing_simulation.available_space()
|
||||||
|
number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
|
||||||
|
yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / (3.6e6)
|
||||||
|
hourly_pv = building.onsite_electrical_production[cte.HOUR]
|
||||||
|
df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
|
||||||
|
used_roof, number_of_pv_panels, yearly_pv]
|
||||||
|
buildings_dic[building.name]=to_dict(building,hourly_pv)
|
||||||
|
|
||||||
|
# %%-----------------------------------------------
|
||||||
|
# """South facing facades"""
|
||||||
|
# %% # -----------------------------------------------
|
||||||
|
# Function to convert radians to degrees
|
||||||
|
import math
|
||||||
|
def radians_to_degrees(radians):
|
||||||
|
return radians * (180 / math.pi)
|
||||||
|
# Step 1: Create the walls_id dictionary
|
||||||
|
walls_id={}
|
||||||
|
|
||||||
|
for building in city.buildings:
|
||||||
|
ids = {}
|
||||||
|
for walls in building.walls:
|
||||||
|
id=walls.id
|
||||||
|
azimuth_degree=radians_to_degrees(float(walls.azimuth))
|
||||||
|
if azimuth_degree>90.0 or azimuth_degree <float(-90.0):
|
||||||
|
ids[id]= {
|
||||||
|
'azimuth': azimuth_degree,
|
||||||
|
'global_irradiance': walls.global_irradiance[cte.HOUR],
|
||||||
|
'area': walls.perimeter_area
|
||||||
|
}
|
||||||
|
walls_id[building.name] = ids
|
||||||
|
|
||||||
|
# Step 2: Calculate pv_on_facade for each wall
|
||||||
|
for building_id, ids in walls_id.items():
|
||||||
|
for wall_id, wall_data in ids.items():
|
||||||
|
if 'global_irradiance' in wall_data:
|
||||||
|
ghi = [x / cte.WATTS_HOUR_TO_JULES/1000 for x in wall_data['global_irradiance']]
|
||||||
|
wall_data['pv_on_facade'] = [x * 0.6 * wall_data['area']*0.22 for x in ghi]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
# for building in city.buildings:
|
walls_dic = output_path / 'walls_id.json'
|
||||||
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
with open(walls_dic , 'w') as json_file:
|
||||||
# print(building.name)
|
json.dump(walls_id, json_file, indent=4)
|
||||||
# total_floor_area = 0
|
|
||||||
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
import pandas as pd
|
||||||
# total_floor_area += thermal_zone.total_floor_area
|
#### EXPORT
|
||||||
# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
# Convert walls_id dictionary to a DataFrame
|
||||||
|
# Convert walls_id dictionary to DataFrames for static and hourly data
|
||||||
|
# def convert_walls_id_to_dfs(walls_id):
|
||||||
|
# static_data = {}
|
||||||
|
# hourly_data = {}
|
||||||
|
#
|
||||||
|
# for building_id, ids in walls_id.items():
|
||||||
|
# for wall_id, wall_data in ids.items():
|
||||||
|
# # Static data
|
||||||
|
# static_data[f"{building_id}_{wall_id}_azimuth"] = wall_data.get('azimuth', None)
|
||||||
|
# static_data[f"{building_id}_{wall_id}_area"] = wall_data.get('area', None)
|
||||||
|
#
|
||||||
|
# if 'pv_on_facade' in wall_data:
|
||||||
|
# hourly_data[f"{building_id}_{wall_id}_pv_on_facade"] = wall_data['pv_on_facade']
|
||||||
|
#
|
||||||
|
# # Create DataFrames
|
||||||
|
# static_df = pd.DataFrame([static_data])
|
||||||
|
# hourly_df = pd.DataFrame(hourly_data)
|
||||||
|
#
|
||||||
|
# return static_df, hourly_df
|
||||||
|
|
||||||
|
|
||||||
|
# output_path_walls_id_dic =output_path / 'walls_id_data.xlsx'
|
||||||
|
#
|
||||||
|
# static_df, hourly_df = convert_walls_id_to_dfs(walls_id)
|
||||||
|
# with pd.ExcelWriter(output_path_walls_id_dic) as writer:
|
||||||
|
# static_df.to_excel(writer, sheet_name='Static Data', index=False)
|
||||||
|
# hourly_df.to_excel(writer, sheet_name='Hourly Data', index=False)
|
||||||
|
|
||||||
|
# print(f"Data successfully exported to {output_path}")
|
||||||
|
# # Save the DataFrame to an Excel file
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
df.to_csv(output_path / 'pv.csv')
|
||||||
|
|
||||||
|
scenario['efficient with PV']=buildings_dic
|
||||||
|
print("Scenario 2: efficient with PV run successfully")
|
||||||
|
|
||||||
|
# #%%-----------------------------------------------
|
||||||
|
# # Scenario 3
|
||||||
|
# #%% # -----------------------------------------------
|
||||||
|
#
|
||||||
# for building in city.buildings:
|
# for building in city.buildings:
|
||||||
# building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
# building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
||||||
# EnergySystemsFactory('montreal_future', city).enrich()
|
# EnergySystemsFactory('montreal_future', city).enrich()
|
||||||
|
# buildings_dic = {}
|
||||||
# for building in city.buildings:
|
# for building in city.buildings:
|
||||||
# EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
# EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
||||||
|
# buildings_dic[building.name] = to_dict(building, hourly_pv)
|
||||||
|
# scenario['efficient with PV+4Pipe+DHW']=buildings_dic
|
||||||
|
# print("Scenario 3: efficient with PV+4Pipe+DHW run successfully")
|
||||||
|
#
|
||||||
|
# def extract_HP_size(building):
|
||||||
|
# dic={
|
||||||
|
# # Heat Pump Rated Heating and Cooling Output
|
||||||
|
# 'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
|
||||||
|
# 'hp_cooling_output': building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
|
||||||
|
# # Boiler Rated Heat Output
|
||||||
|
# 'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
|
||||||
|
# # TES characteristics
|
||||||
|
# 'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
|
||||||
|
# 'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
|
||||||
|
# # DHW HP
|
||||||
|
# 'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
|
||||||
|
# # DHW TES Characteristics
|
||||||
|
# 'dhw_tes_volume': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
|
||||||
|
# 'dhw_tes_height': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
|
||||||
|
# }
|
||||||
|
#
|
||||||
|
#
|
||||||
|
# return dic
|
||||||
|
# HPs={}
|
||||||
|
# for building in city.buildings:
|
||||||
|
# HPs[building.name]=extract_HP_size(building)
|
||||||
|
|
||||||
|
clusters=pd.read_csv(output_path/'clusters.csv')
|
||||||
|
|
||||||
|
|
||||||
|
# Step 2: Extract the demand data for each building
|
||||||
|
def extract_building_demand(city):
|
||||||
|
building_demand = {}
|
||||||
|
for building in city.buildings:
|
||||||
|
demands = {
|
||||||
|
'heating_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.heating_demand[cte.HOUR]],
|
||||||
|
'cooling_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.cooling_demand[cte.HOUR]],
|
||||||
|
'domestic_hot_water_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
||||||
|
'appliances_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.appliances_electrical_demand[cte.HOUR]],
|
||||||
|
'lighting_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.lighting_electrical_demand[cte.HOUR]]
|
||||||
|
}
|
||||||
|
building_demand[building.name] = demands
|
||||||
|
return building_demand
|
||||||
|
|
||||||
|
# Step 3: Sum the demand types for each cluster
|
||||||
|
def sum_demands_by_cluster(building_demand, clusters, demand_types):
|
||||||
|
cluster_demands = {cluster: {demand_type: [0] * 8760 for demand_type in demand_types} for cluster in clusters['cluster'].unique()}
|
||||||
|
|
||||||
|
for _, row in clusters.iterrows():
|
||||||
|
building_id = str(row['id'])
|
||||||
|
cluster = row['cluster']
|
||||||
|
if building_id in building_demand:
|
||||||
|
for demand_type in demand_types:
|
||||||
|
cluster_demands[cluster][demand_type] = [sum(x) for x in zip(cluster_demands[cluster][demand_type], building_demand[building_id][demand_type])]
|
||||||
|
|
||||||
|
return cluster_demands
|
||||||
|
|
||||||
|
|
||||||
|
def plot_demands_by_cluster(cluster_demands, demand_types, output_folder):
|
||||||
|
import os
|
||||||
|
os.makedirs(output_folder, exist_ok=True)
|
||||||
|
|
||||||
|
for cluster, demands in cluster_demands.items():
|
||||||
|
plt.figure(figsize=(15, 10))
|
||||||
|
for demand_type in demand_types:
|
||||||
|
plt.plot(demands[demand_type], label=demand_type)
|
||||||
|
|
||||||
|
plt.title(f'Summed Demands for Cluster {cluster}')
|
||||||
|
plt.xlabel('Hour of the Year')
|
||||||
|
plt.ylabel('Demand (kWh)')
|
||||||
|
plt.legend(loc='upper right')
|
||||||
|
plt.grid(True)
|
||||||
|
plt.tight_layout()
|
||||||
|
plt.savefig(os.path.join(output_folder, f'cluster_{cluster}_summed_demands.png'))
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
|
||||||
|
# Example usage
|
||||||
|
demand_types = [
|
||||||
|
'heating_demand',
|
||||||
|
'cooling_demand',
|
||||||
|
'domestic_hot_water_demand',
|
||||||
|
'appliances_electrical_demand',
|
||||||
|
'lighting_electrical_demand'
|
||||||
|
]
|
||||||
|
|
||||||
|
# Extract the building demand data
|
||||||
|
building_demand = extract_building_demand(city)
|
||||||
|
cluster_demands = sum_demands_by_cluster(building_demand, clusters, demand_types)
|
||||||
|
# Create a DataFrame to export the results
|
||||||
|
cluster_demands_df = {f"{cluster}_{demand_type}": data for cluster, demands in cluster_demands.items() for
|
||||||
|
demand_type, data in demands.items()}
|
||||||
|
cluster_demands_df = pd.DataFrame(cluster_demands_df)
|
||||||
|
|
||||||
|
# Save the results to an Excel file
|
||||||
|
|
||||||
|
cluster_demands_df.to_excel(output_path/'cluster_demands.xlsx', index=False)
|
||||||
|
|
||||||
|
print(f"Clustered demand data successfully exported to {output_path}")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#%%-----------------------------------------------
|
||||||
|
# Scenario 4
|
||||||
|
#%% # -----------------------------------------------
|
||||||
|
|
||||||
|
del city
|
||||||
|
del buildings_dic
|
||||||
|
|
||||||
|
|
||||||
|
geojson_file_path_clusters= output_path / 'new.geojson'
|
||||||
|
|
||||||
|
with open(geojson_file_path_clusters , 'r') as f:
|
||||||
|
building_type_data_new = json.load(f)
|
||||||
|
|
||||||
|
# Create city object from GeoJSON file
|
||||||
|
city = GeometryFactory('geojson',
|
||||||
|
path=geojson_file_path_clusters,
|
||||||
|
height_field='maximum_roof_height',
|
||||||
|
year_of_construction_field='year_built',
|
||||||
|
function_field='building_type',
|
||||||
|
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||||
|
#%%-----------------------------------------------
|
||||||
|
# Enrich city data
|
||||||
|
#%% # -----------------------------------------------
|
||||||
|
ConstructionFactory('nrcan', city).enrich()
|
||||||
|
UsageFactory('nrcan', city).enrich()
|
||||||
|
WeatherFactory('epw', city).enrich()
|
||||||
|
|
||||||
|
buildings_clusters={
|
||||||
|
1651: 4,
|
||||||
|
1662: 0,
|
||||||
|
1667: 1,
|
||||||
|
1674: 2,
|
||||||
|
1688: 3
|
||||||
|
}
|
||||||
|
|
||||||
|
for building_id in buildings_clusters:
|
||||||
|
cluster=buildings_clusters[building_id]
|
||||||
|
for idx, feature in enumerate(building_type_data_new['features']):
|
||||||
|
if feature['properties']['id'] == str(building_id):
|
||||||
|
building_type_data_new['features'][idx]['properties']['heating_demand_kWh']=cluster_demands[cluster]['heating_demand']
|
||||||
|
building_type_data_new['features'][idx]['properties']['cooling_demand_kWh'] = cluster_demands[cluster]['cooling_demand']
|
||||||
|
building_type_data_new['features'][idx]['properties']['domestic_hot_water_heat_demand_kWh'] = cluster_demands[cluster]['domestic_hot_water_demand']
|
||||||
|
building_type_data_new['features'][idx]['properties']['appliances_electrical_demand_kWh'] = cluster_demands[cluster]['appliances_electrical_demand']
|
||||||
|
building_type_data_new['features'][idx]['properties']['lighting_electrical_demand_kWh'] = cluster_demands[cluster]['lighting_electrical_demand']
|
||||||
|
|
||||||
|
enrich_buildings_with_geojson_data (building_type_data_new, city)
|
||||||
|
|
||||||
|
|
||||||
|
for building in city.buildings:
|
||||||
|
building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
||||||
|
EnergySystemsFactory('montreal_future', city).enrich()
|
||||||
|
buildings_dic = {}
|
||||||
|
for building in city.buildings:
|
||||||
|
EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
||||||
|
buildings_dic[building.name] = to_dict(building, hourly_pv)
|
||||||
|
scenario['efficient with PV+4Pipe+DHW']=buildings_dic
|
||||||
|
print("Scenario 4: efficient with PV+4Pipe+DHW run successfully for Clusters")
|
||||||
|
|
||||||
|
def extract_HP_size(building):
|
||||||
|
dic={
|
||||||
|
# Heat Pump Rated Heating and Cooling Output
|
||||||
|
'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
|
||||||
|
'hp_cooling_output': building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
|
||||||
|
# Boiler Rated Heat Output
|
||||||
|
'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
|
||||||
|
# TES characteristics
|
||||||
|
'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
|
||||||
|
'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
|
||||||
|
# DHW HP
|
||||||
|
'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
|
||||||
|
# DHW TES Characteristics
|
||||||
|
'dhw_tes_volume': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
|
||||||
|
'dhw_tes_height': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
return dic
|
||||||
|
HPs={}
|
||||||
|
for building in city.buildings:
|
||||||
|
HPs[building.name]=extract_HP_size(building)
|
||||||
|
|
||||||
|
#%%-------------------------------------------------------
|
||||||
|
#""""EXPORTERS"""
|
||||||
|
#%%-------------------------------------------------------
|
||||||
|
|
||||||
|
|
||||||
|
# Convert the dictionary to a DataFrame
|
||||||
|
df = pd.DataFrame.from_dict(HPs, orient='index')
|
||||||
|
|
||||||
|
# Save the DataFrame to an Excel file
|
||||||
|
output_path_HPs =output_path/ 'HPs_data_sc4.xlsx'
|
||||||
|
df.to_excel(output_path_HPs, index_label='building_id')
|
||||||
|
|
||||||
|
print(f"Data successfully exported to {output_path}")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#%%-------------------------------------------------------
|
||||||
|
#""""EXPORTERS"""
|
||||||
|
#%%-------------------------------------------------------
|
||||||
|
|
||||||
|
|
||||||
|
# Convert the dictionary to a DataFrame
|
||||||
|
df = pd.DataFrame.from_dict(HPs, orient='index')
|
||||||
|
|
||||||
|
# Save the DataFrame to an Excel file
|
||||||
|
output_path_HPs =output_path/ 'HPs_data.xlsx'
|
||||||
|
df.to_excel(output_path_HPs, index_label='building_id')
|
||||||
|
|
||||||
|
print(f"Data successfully exported to {output_path}")
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
districts_demands={}
|
||||||
|
def extract_and_sum_demand_data(scenario, demand_types):
|
||||||
|
|
||||||
|
|
||||||
|
# Conversion factor constant
|
||||||
|
conversion_factor = 1 / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||||
|
|
||||||
|
# Loop through each scenario
|
||||||
|
for scenario_key, buildings in scenario.items():
|
||||||
|
# Loop through each building in the scenario
|
||||||
|
# Initialize an empty dictionary to store the district demand sums
|
||||||
|
district_demand = {demand_type: [0] * 8760 for demand_type in demand_types}
|
||||||
|
district_demand['hourly_pv_kWh']= [0] * 8760
|
||||||
|
for building_id, building_data in buildings.items():
|
||||||
|
# Loop through each demand type and sum up the data
|
||||||
|
for demand_type in demand_types:
|
||||||
|
if demand_type in building_data:
|
||||||
|
district_demand[demand_type] = [sum(x) for x in zip(district_demand[demand_type], building_data[demand_type])]
|
||||||
|
|
||||||
|
# If PV data is available and relevant
|
||||||
|
if scenario_key == "efficient with PV":
|
||||||
|
district_demand['hourly_pv_kWh'] = [sum(x) for x in zip(district_demand['hourly_pv_kWh'], building_data['hourly_pv_kWh'])]
|
||||||
|
if scenario_key == 'efficient with PV+4Pipe+DHW':
|
||||||
|
district_demand['hourly_pv_kWh'] = districts_demands["efficient with PV"]['hourly_pv_kWh']
|
||||||
|
districts_demands[scenario_key]=district_demand
|
||||||
|
|
||||||
|
return districts_demands
|
||||||
|
|
||||||
|
# Example usage
|
||||||
|
# Assuming 'scenario' is a dictionary with the required structure and 'cte' is defined somewhere with WATTS_HOUR_TO_JULES constant
|
||||||
|
demand_types = [
|
||||||
|
'heating_consumption_kWh',
|
||||||
|
'cooling_consumption_kWh',
|
||||||
|
'domestic_hot_water_consumption_kWh',
|
||||||
|
'appliances_consumption_kWh',
|
||||||
|
'lighting_consumption_kWh',
|
||||||
|
# 'hourly_pv_kWh' # Include this only if you want to consider PV data
|
||||||
|
]
|
||||||
|
|
||||||
|
# # Call the function with your scenario data
|
||||||
|
district_demand = extract_and_sum_demand_data(scenario, demand_types)
|
||||||
|
#
|
||||||
|
# """"EXPORTERS"""
|
||||||
|
# import pandas as pd
|
||||||
|
#
|
||||||
|
#
|
||||||
|
# Export the DataFrame to an Excel file
|
||||||
|
excel_file_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\districts_balance.xlsx'
|
||||||
|
# df.to_excel(excel_file_path, index=True, index_label='Building')
|
||||||
|
|
||||||
|
# Create an Excel writer object
|
||||||
|
with pd.ExcelWriter(excel_file_path, engine='xlsxwriter') as writer:
|
||||||
|
for scenarios,demands in district_demand.items():
|
||||||
|
# Convert demands to a DataFrame
|
||||||
|
df_demands = pd.DataFrame(demands)
|
||||||
|
# Convert building_id to string and check its length
|
||||||
|
sheet_name = str(scenarios)
|
||||||
|
if len(sheet_name) > 31:
|
||||||
|
sheet_name = sheet_name[:31] # Truncate to 31 characters if necessary
|
||||||
|
# Write the DataFrame to a specific sheet named after the building_id
|
||||||
|
df_demands.to_excel(writer, sheet_name=sheet_name, index=False)
|
||||||
|
|
||||||
|
|
||||||
|
print("district balance data is exported successfully")
|
||||||
|
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
# Assuming your scenario dictionary is already defined as follows:
|
||||||
|
# scenario = {
|
||||||
|
# 'baseline': { ... },
|
||||||
|
# 'efficient with PV': { ... }
|
||||||
|
# }
|
||||||
|
|
||||||
|
|
||||||
|
def dict_to_df_col_wise(building_data):
|
||||||
|
"""
|
||||||
|
Converts a dictionary of building data to a DataFrame.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
building_data (dict): Dictionary containing building data where keys are building ids and values are dictionaries
|
||||||
|
with hourly data for various demand types.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
pd.DataFrame: DataFrame with columns for each building and demand type.
|
||||||
|
"""
|
||||||
|
# Create a dictionary to hold DataFrames for each demand type
|
||||||
|
df_dict= {}
|
||||||
|
|
||||||
|
# Loop over each building
|
||||||
|
for building_id, data in building_data.items():
|
||||||
|
# Create a DataFrame for this building's data
|
||||||
|
building_df = pd.DataFrame(data)
|
||||||
|
|
||||||
|
# Rename columns to include building_id
|
||||||
|
building_df.columns = [f"{building_id}_{col}" for col in building_df.columns]
|
||||||
|
|
||||||
|
# Add this DataFrame to the dictionary
|
||||||
|
df_dict[building_id] = building_df
|
||||||
|
|
||||||
|
# Concatenate all building DataFrames column-wise
|
||||||
|
result_df = pd.concat(df_dict.values(), axis=1)
|
||||||
|
|
||||||
|
return result_df
|
||||||
|
|
||||||
|
# Create DataFrames for each scenario
|
||||||
|
baseline_df = dict_to_df_col_wise(scenario['baseline'])
|
||||||
|
efficient_with_pv_df = dict_to_df_col_wise(scenario['efficient with PV'])
|
||||||
|
efficient_with_pv_hps = dict_to_df_col_wise(scenario['efficient with PV+4Pipe+DHW'])
|
||||||
|
|
||||||
|
|
||||||
|
# Write the DataFrames to an Excel file with two separate sheets
|
||||||
|
with pd.ExcelWriter(r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\scenario_data.xlsx') as writer:
|
||||||
|
baseline_df.to_excel(writer, sheet_name='baseline', index=True)
|
||||||
|
efficient_with_pv_df.to_excel(writer, sheet_name='efficient with PV', index=True)
|
||||||
|
efficient_with_pv_hps.to_excel(writer, sheet_name='efficient with HPs_2', index=True)
|
||||||
|
|
||||||
|
print("hourly data has been successfully exported per building to scenario_data.xlsx")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def convert_hourly_to_monthly(hourly_data):
|
||||||
|
"""
|
||||||
|
Converts hourly data to monthly data by summing up the values for each month.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
hourly_data (list): List of hourly data (length 8760).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list: List of monthly data (length 12).
|
||||||
|
"""
|
||||||
|
hourly_series = pd.Series(hourly_data, index=pd.date_range(start='1/1/2023', periods=8760, freq='H'))
|
||||||
|
monthly_data = hourly_series.resample('M').sum()
|
||||||
|
return monthly_data.tolist()
|
||||||
|
|
||||||
|
import os
|
||||||
|
def plot_stacked_demands_vs_pv(district_demand, demand_types, output_path, pv_type='hourly_pv_kWh'):
|
||||||
|
"""
|
||||||
|
Plots the stacked monthly demand for each scenario and compares it to the PV data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
district_demand (dict): Dictionary with scenario keys and demand data.
|
||||||
|
demand_types (list): List of demand types to plot.
|
||||||
|
output_path (str): Path to save the plots.
|
||||||
|
pv_type (str): The PV data type to compare against.
|
||||||
|
"""
|
||||||
|
os.makedirs(output_path, exist_ok=True)
|
||||||
|
|
||||||
|
for scenario_key, demand_data in district_demand.items():
|
||||||
|
# Convert hourly data to monthly data for each demand type
|
||||||
|
monthly_data = {demand_type: convert_hourly_to_monthly(demand_data[demand_type]) for demand_type in
|
||||||
|
demand_types}
|
||||||
|
monthly_pv = convert_hourly_to_monthly(demand_data.get(pv_type, [0] * 8760))
|
||||||
|
|
||||||
|
# Create a DataFrame for easier plotting
|
||||||
|
combined_data = pd.DataFrame(monthly_data)
|
||||||
|
combined_data['Month'] = range(1, 13)
|
||||||
|
combined_data['PV'] = monthly_pv
|
||||||
|
|
||||||
|
# Plotting
|
||||||
|
fig, ax1 = plt.subplots(figsize=(14, 8))
|
||||||
|
|
||||||
|
# Plot stacked demands
|
||||||
|
combined_data.set_index('Month', inplace=True)
|
||||||
|
combined_data[demand_types].plot(kind='bar', stacked=True, ax=ax1, colormap='tab20')
|
||||||
|
|
||||||
|
ax1.set_xlabel('Month')
|
||||||
|
ax1.set_ylabel('Energy Demand (kWh)')
|
||||||
|
ax1.set_title(f'Monthly Energy Demand and PV Generation for {scenario_key}')
|
||||||
|
|
||||||
|
# Plot PV data on the secondary y-axis
|
||||||
|
ax2 = ax1.twinx()
|
||||||
|
ax2.plot(combined_data.index, combined_data['PV'], color='black', linestyle='-', marker='o',
|
||||||
|
label='PV Generation')
|
||||||
|
ax2.set_ylabel('PV Generation (kWh)')
|
||||||
|
|
||||||
|
# Add legends
|
||||||
|
ax1.legend(loc='upper left')
|
||||||
|
ax2.legend(loc='upper right')
|
||||||
|
|
||||||
|
ax1.set_xticks(combined_data.index)
|
||||||
|
ax1.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
|
||||||
|
|
||||||
|
# Save the plot
|
||||||
|
plt.savefig(os.path.join(output_path, f'{scenario_key}_monthly_demand_vs_pv.png'))
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
|
||||||
|
# Example usage
|
||||||
|
# district_demand = extract_and_sum_demand_data(scenario, demand_types)
|
||||||
|
|
||||||
|
# Specify the demand types and PV type
|
||||||
|
demand_types = [
|
||||||
|
'heating_consumption_kWh',
|
||||||
|
'cooling_consumption_kWh',
|
||||||
|
'domestic_hot_water_consumption_kWh',
|
||||||
|
'appliances_consumption_kWh',
|
||||||
|
'lighting_consumption_kWh'
|
||||||
|
]
|
||||||
|
|
||||||
|
# Plot the data
|
||||||
|
plot_stacked_demands_vs_pv(district_demand, demand_types, output_path)
|
||||||
|
# Plot the data
|
||||||
|
print('test')
|
||||||
|
import csv
|
10
processor.py
10
processor.py
|
@ -3,16 +3,18 @@ from pathlib import Path
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import geopandas as gpd
|
import geopandas as gpd
|
||||||
|
|
||||||
|
#%%-----------------------------------------------
|
||||||
|
# """This code takes different results from energy plus with different building uses and obtains a unique geojson that combines all of them"""
|
||||||
|
#%% # -----------------------------------------------
|
||||||
#define output path
|
#define output path
|
||||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||||
output_path.mkdir(parents=True, exist_ok=True)
|
output_path.mkdir(parents=True, exist_ok=True)
|
||||||
# Define output folders
|
# Define output folders
|
||||||
output_folders = ['building_type', 'building_type_2', 'building_type_3']
|
output_folders = ['building_type', 'building_type_2', 'building_type_3']
|
||||||
#output paths containing energy+ results are read
|
#output paths containing energy+ results are read
|
||||||
path1=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson'
|
path1=output_path / '1990_and_2019'/'building_type' / 'updated_buildings.geojson'
|
||||||
path2=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_2\updated_buildings.geojson'
|
path2=output_path / '1990_and_2019'/ 'building_type_2' / 'updated_buildings.geojson'
|
||||||
path3=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_3\updated_buildings.geojson'
|
path3=output_path / '1990_and_2019'/'building_type_3' / 'updated_buildings.geojson'
|
||||||
with open(path1, 'r') as f:
|
with open(path1, 'r') as f:
|
||||||
building_type_data = json.load(f)
|
building_type_data = json.load(f)
|
||||||
with open(path2, 'r') as f:
|
with open(path2, 'r') as f:
|
||||||
|
|
|
@ -4,8 +4,8 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
|
||||||
Copyright © 2022 Concordia CERC group
|
Copyright © 2022 Concordia CERC group
|
||||||
Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
|
Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
|
||||||
"""
|
"""
|
||||||
|
from scripts.system_simulation_models.archetype13_electric import Archetype13
|
||||||
from scripts.system_simulation_models.archetype13 import Archetype13
|
# from scripts.system_simulation_models.archetype13 import Archetype13
|
||||||
from scripts.system_simulation_models.archetype13_stratified_tes import Archetype13Stratified
|
from scripts.system_simulation_models.archetype13_stratified_tes import Archetype13Stratified
|
||||||
from scripts.system_simulation_models.archetype1 import Archetype1
|
from scripts.system_simulation_models.archetype1 import Archetype1
|
||||||
from scripts.system_simulation_models.archetypes14_15 import Archetype14_15
|
from scripts.system_simulation_models.archetypes14_15 import Archetype14_15
|
||||||
|
|
|
@ -16,7 +16,8 @@ class Archetype1:
|
||||||
self._domestic_hot_water_peak_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
|
self._domestic_hot_water_peak_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
|
||||||
self._hourly_heating_demand = [0] + [demand / 3600 for demand in building.heating_demand[cte.HOUR]]
|
self._hourly_heating_demand = [0] + [demand / 3600 for demand in building.heating_demand[cte.HOUR]]
|
||||||
self._hourly_cooling_demand = [demand / 3600 for demand in building.cooling_demand[cte.HOUR]]
|
self._hourly_cooling_demand = [demand / 3600 for demand in building.cooling_demand[cte.HOUR]]
|
||||||
self._hourly_dhw_demand = building.domestic_hot_water_heat_demand[cte.HOUR]
|
self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
|
||||||
|
building.domestic_hot_water_heat_demand[cte.HOUR]]
|
||||||
self._output_path = output_path
|
self._output_path = output_path
|
||||||
self._t_out = [0] + building.external_temperature[cte.HOUR]
|
self._t_out = [0] + building.external_temperature[cte.HOUR]
|
||||||
self.results = {}
|
self.results = {}
|
||||||
|
@ -27,9 +28,9 @@ class Archetype1:
|
||||||
heat_pump = self._hvac_system.generation_systems[0]
|
heat_pump = self._hvac_system.generation_systems[0]
|
||||||
boiler = self._hvac_system.generation_systems[1]
|
boiler = self._hvac_system.generation_systems[1]
|
||||||
thermal_storage = heat_pump.energy_storage_systems[0]
|
thermal_storage = heat_pump.energy_storage_systems[0]
|
||||||
heat_pump.nominal_heat_output = round(0.5 * self._heating_peak_load / 3600)
|
heat_pump.nominal_heat_output = round(0.5 * self._heating_peak_load)
|
||||||
heat_pump.nominal_cooling_output = round(self._cooling_peak_load / 3600)
|
heat_pump.nominal_cooling_output = round(self._cooling_peak_load)
|
||||||
boiler.nominal_heat_output = round(0.5 * self._heating_peak_load / 3600)
|
boiler.nominal_heat_output = round(0.5 * self._heating_peak_load)
|
||||||
thermal_storage.volume = round(
|
thermal_storage.volume = round(
|
||||||
(self._heating_peak_load * storage_factor * cte.WATTS_HOUR_TO_JULES) /
|
(self._heating_peak_load * storage_factor * cte.WATTS_HOUR_TO_JULES) /
|
||||||
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
||||||
|
@ -60,7 +61,7 @@ class Archetype1:
|
||||||
(t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
|
(t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
|
||||||
hp_electricity, boiler_gas, boiler_consumption, t_sup_boiler, heating_consumption) = [variables[name] for name in
|
hp_electricity, boiler_gas, boiler_consumption, t_sup_boiler, heating_consumption) = [variables[name] for name in
|
||||||
variable_names]
|
variable_names]
|
||||||
t_tank[0] = 55
|
t_tank[0] = 30
|
||||||
dt = 3600
|
dt = 3600
|
||||||
hp_heating_cap = hp.nominal_heat_output
|
hp_heating_cap = hp.nominal_heat_output
|
||||||
hp_efficiency = float(hp.heat_efficiency)
|
hp_efficiency = float(hp.heat_efficiency)
|
||||||
|
|
|
@ -22,12 +22,13 @@ class Archetype13:
|
||||||
self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
|
self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
|
||||||
building.domestic_hot_water_heat_demand[cte.HOUR]]
|
building.domestic_hot_water_heat_demand[cte.HOUR]]
|
||||||
self._output_path = output_path
|
self._output_path = output_path
|
||||||
self._t_out = building.external_temperature[cte.HOUR]
|
self._t_out = [15]*8760
|
||||||
|
# building.external_temperature[cte.HOUR]
|
||||||
self.results = {}
|
self.results = {}
|
||||||
self.dt = 900
|
self.dt = 900
|
||||||
|
|
||||||
def hvac_sizing(self):
|
def hvac_sizing(self,storage_factor=3):
|
||||||
storage_factor = 3
|
storage_factor = storage_factor
|
||||||
heat_pump = self._hvac_system.generation_systems[1]
|
heat_pump = self._hvac_system.generation_systems[1]
|
||||||
boiler = self._hvac_system.generation_systems[0]
|
boiler = self._hvac_system.generation_systems[0]
|
||||||
thermal_storage = boiler.energy_storage_systems[0]
|
thermal_storage = boiler.energy_storage_systems[0]
|
||||||
|
@ -39,8 +40,8 @@ class Archetype13:
|
||||||
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
||||||
return heat_pump, boiler, thermal_storage
|
return heat_pump, boiler, thermal_storage
|
||||||
|
|
||||||
def dhw_sizing(self):
|
def dhw_sizing(self,storage_factor=3):
|
||||||
storage_factor = 3
|
storage_factor = storage_factor
|
||||||
dhw_hp = self._dhw_system.generation_systems[0]
|
dhw_hp = self._dhw_system.generation_systems[0]
|
||||||
dhw_hp.nominal_heat_output = 0.7 * self._domestic_hot_water_peak_load
|
dhw_hp.nominal_heat_output = 0.7 * self._domestic_hot_water_peak_load
|
||||||
dhw_hp.source_temperature = self._t_out
|
dhw_hp.source_temperature = self._t_out
|
||||||
|
@ -51,7 +52,7 @@ class Archetype13:
|
||||||
|
|
||||||
def heating_system_simulation(self):
|
def heating_system_simulation(self):
|
||||||
hp, boiler, tes = self.hvac_sizing()
|
hp, boiler, tes = self.hvac_sizing()
|
||||||
cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
# cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||||
demand = [0] + [x for x in self._hourly_heating_demand for _ in range(number_of_ts)]
|
demand = [0] + [x for x in self._hourly_heating_demand for _ in range(number_of_ts)]
|
||||||
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
||||||
|
@ -102,12 +103,13 @@ class Archetype13:
|
||||||
t_tank_fahrenheit = 1.8 * t_tank[i + 1] + 32
|
t_tank_fahrenheit = 1.8 * t_tank[i + 1] + 32
|
||||||
t_out_fahrenheit = 1.8 * t_out[i + 1] + 32
|
t_out_fahrenheit = 1.8 * t_out[i + 1] + 32
|
||||||
if q_hp[i + 1] > 0:
|
if q_hp[i + 1] > 0:
|
||||||
hp_cop[i + 1] = (1 / (cop_curve_coefficients[0] +
|
hp_cop[i + 1] =4.07
|
||||||
cop_curve_coefficients[1] * t_tank_fahrenheit +
|
# (1 / (cop_curve_coefficients[0] +
|
||||||
cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
|
# cop_curve_coefficients[1] * t_tank_fahrenheit +
|
||||||
cop_curve_coefficients[3] * t_out_fahrenheit +
|
# cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
|
||||||
cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
# cop_curve_coefficients[3] * t_out_fahrenheit +
|
||||||
cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_efficiency
|
# cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||||
|
# cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_efficiency
|
||||||
hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
|
hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
|
||||||
else:
|
else:
|
||||||
hp_cop[i + 1] = 0
|
hp_cop[i + 1] = 0
|
||||||
|
@ -119,7 +121,7 @@ class Archetype13:
|
||||||
elif demand[i + 1] > 0.5 * self._heating_peak_load / self.dt:
|
elif demand[i + 1] > 0.5 * self._heating_peak_load / self.dt:
|
||||||
q_boiler[i + 1] = 0.5 * boiler_heating_cap
|
q_boiler[i + 1] = 0.5 * boiler_heating_cap
|
||||||
boiler_energy_consumption[i + 1] = q_boiler[i + 1] / boiler_efficiency
|
boiler_energy_consumption[i + 1] = q_boiler[i + 1] / boiler_efficiency
|
||||||
boiler_gas_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (boiler_efficiency * cte.NATURAL_GAS_LHV)
|
# boiler_gas_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (boiler_efficiency * cte.NATURAL_GAS_LHV)
|
||||||
t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
|
t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
|
||||||
# storage discharging
|
# storage discharging
|
||||||
if demand[i + 1] == 0:
|
if demand[i + 1] == 0:
|
||||||
|
@ -178,7 +180,7 @@ class Archetype13:
|
||||||
|
|
||||||
def cooling_system_simulation(self):
|
def cooling_system_simulation(self):
|
||||||
hp = self.hvac_sizing()[0]
|
hp = self.hvac_sizing()[0]
|
||||||
eer_curve_coefficients = [float(coefficient) for coefficient in hp.cooling_efficiency_curve.coefficients]
|
# eer_curve_coefficients = [float(coefficient) for coefficient in hp.cooling_efficiency_curve.coefficients]
|
||||||
cooling_efficiency = float(hp.cooling_efficiency)
|
cooling_efficiency = float(hp.cooling_efficiency)
|
||||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||||
demand = [0] + [x for x in self._hourly_cooling_demand for _ in range(number_of_ts)]
|
demand = [0] + [x for x in self._hourly_cooling_demand for _ in range(number_of_ts)]
|
||||||
|
@ -216,12 +218,13 @@ class Archetype13:
|
||||||
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
||||||
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
||||||
if q_hp[i] > 0:
|
if q_hp[i] > 0:
|
||||||
hp_cop[i] = (1 / (eer_curve_coefficients[0] +
|
hp_cop[i] = 4
|
||||||
eer_curve_coefficients[1] * t_sup_hp_fahrenheit +
|
# (1 / (eer_curve_coefficients[0] +
|
||||||
eer_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
|
# eer_curve_coefficients[1] * t_sup_hp_fahrenheit +
|
||||||
eer_curve_coefficients[3] * t_out_fahrenheit +
|
# eer_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
|
||||||
eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
# eer_curve_coefficients[3] * t_out_fahrenheit +
|
||||||
eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)) * cooling_efficiency / 3.41
|
# eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||||
|
# eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)) * cooling_efficiency / 3.41
|
||||||
hp_electricity[i] = q_hp[i] / cooling_efficiency
|
hp_electricity[i] = q_hp[i] / cooling_efficiency
|
||||||
else:
|
else:
|
||||||
hp_cop[i] = 0
|
hp_cop[i] = 0
|
||||||
|
@ -251,7 +254,7 @@ class Archetype13:
|
||||||
|
|
||||||
def dhw_system_simulation(self):
|
def dhw_system_simulation(self):
|
||||||
hp, tes = self.dhw_sizing()
|
hp, tes = self.dhw_sizing()
|
||||||
cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
# cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||||
demand = [0] + [x for x in self._hourly_dhw_demand for _ in range(number_of_ts)]
|
demand = [0] + [x for x in self._hourly_dhw_demand for _ in range(number_of_ts)]
|
||||||
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
||||||
|
@ -298,12 +301,13 @@ class Archetype13:
|
||||||
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
||||||
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
||||||
if q_hp[i] > 0:
|
if q_hp[i] > 0:
|
||||||
hp_cop[i] = (cop_curve_coefficients[0] +
|
hp_cop[i] = 3
|
||||||
cop_curve_coefficients[1] * t_out[i] +
|
# (cop_curve_coefficients[0] +
|
||||||
cop_curve_coefficients[2] * t_out[i] ** 2 +
|
# cop_curve_coefficients[1] * t_out[i] +
|
||||||
cop_curve_coefficients[3] * t_tank[i] +
|
# cop_curve_coefficients[2] * t_out[i] ** 2 +
|
||||||
cop_curve_coefficients[4] * t_tank[i] ** 2 +
|
# cop_curve_coefficients[3] * t_tank[i] +
|
||||||
cop_curve_coefficients[5] * t_tank[i] * t_out[i]) * float(hp.heat_efficiency)
|
# cop_curve_coefficients[4] * t_tank[i] ** 2 +
|
||||||
|
# cop_curve_coefficients[5] * t_tank[i] * t_out[i]) * float(hp.heat_efficiency)
|
||||||
hp_electricity[i] = q_hp[i] / hp_cop[i]
|
hp_electricity[i] = q_hp[i] / hp_cop[i]
|
||||||
else:
|
else:
|
||||||
hp_cop[i] = 0
|
hp_cop[i] = 0
|
Loading…
Reference in New Issue
Block a user