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566ebca6cd
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566ebca6cd | ||
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c8c37b756c | ||
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4f1a47d505 | ||
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ad9b5e6a19 | ||
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33871fba77 |
@ -39,7 +39,8 @@ class EnergySystem:
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total_gen = 0
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for index, row in data.iterrows():
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time = row['time']
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sunlight_intensity = row['sunlight']
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# sunlight_intensity = row['sunlight']
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pv_yield = row['PV yield[kW/kWp]']
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factory_demand = row['demand']
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electricity_price = row['buy']
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sell_price = row['sell']
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@ -55,7 +56,7 @@ class EnergySystem:
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soc = self.ess.storage / self.ess.capacity
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self.hour_stored_2.append(soc)
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generated_pv_power = self.pv.capacity * sunlight_intensity # 生成的功率,单位 kW
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generated_pv_power = self.pv.capacity * pv_yield# 生成的功率,单位 kW
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generated_pv_energy = generated_pv_power * time_interval * self.pv.loss # 生成的能量,单位 kWh
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self.generated += generated_pv_energy
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# pv生成的能量如果比工厂的需求要大
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45235
combined_data.csv
45235
combined_data.csv
File diff suppressed because it is too large
Load Diff
35041
factory_power1.csv
Normal file
35041
factory_power1.csv
Normal file
File diff suppressed because it is too large
Load Diff
48
main.py
48
main.py
@ -1,9 +1,5 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[ ]:
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import os
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import glob
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import shutil
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@ -28,9 +24,6 @@ folder_path = 'plots'
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clear_folder_make_ess_pv(folder_path)
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# In[ ]:
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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@ -38,10 +31,6 @@ import pandas as pd
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from EnergySystem import EnergySystem
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from config import pv_config, grid_config, ess_config
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# In[ ]:
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import json
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print("Version 0.0.5")
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@ -49,9 +38,6 @@ print("Version 0.0.5")
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with open('config.json', 'r') as f:
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js_data = json.load(f)
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time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
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print(time_interval)
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@ -132,9 +118,6 @@ plt.savefig('plots/demand.png')
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plt.close()
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# In[ ]:
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def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):
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df=results
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df = df.astype(float)
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@ -220,10 +203,6 @@ def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, fig
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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# In[ ]:
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def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
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df = costs
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df = df.astype(int)
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@ -255,9 +234,6 @@ def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10
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plt.savefig(filename)
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# In[ ]:
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def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10), days=365, granularity=15):
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df = overload_cnt
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print(days, granularity)
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@ -305,18 +281,10 @@ def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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# In[ ]:
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def cal_profit(es: EnergySystem, saved_money, days):
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profit = saved_money - es.ess.get_cost_per_year() / 365 * days - es.pv.get_cost_per_year() / 365 * days
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return profit
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# In[ ]:
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def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price, time_interval, data, days):
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pv = pv_config(capacity=pv_capacity,
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cost_per_kW=pv_cost_per_kW,
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@ -341,9 +309,6 @@ def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacit
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return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated)
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# In[ ]:
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months_results = []
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months_costs = []
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months_overload = []
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@ -434,17 +399,11 @@ draw_overload(overload_cnt=annual_overload,
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figure_size=figure_size)
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# In[ ]:
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def save_data(data, filename):
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data.to_csv(filename+'.csv')
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data.to_json(filename + '.json')
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# In[ ]:
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if not os.path.isdir('data'):
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os.makedirs('data')
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@ -452,15 +411,8 @@ save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess
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save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')
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save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')
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# In[ ]:
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draw_results(annual_result, 'plots/test.png', 'test', False)
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# In[ ]:
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draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)
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38
read_data.py
38
read_data.py
@ -2,56 +2,38 @@ import pandas as pd
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import numpy as np
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import csv
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sunlight_file_name = 'lightintensity.xlsx'
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factory_demand_file_name = 'factory_power1.xlsx'
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pv_yield_file_name = 'read_data/Serbia.csv'
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# factory_demand_file_name = 'factory_power1.xlsx'
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factory_demand_file_name = 'factory_power1.csv'
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electricity_price_data = 'electricity_price_data.csv'
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electricity_price_data_sell = 'electricity_price_data_sell.csv'
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df_sunlight = pd.read_excel(sunlight_file_name, header=None, names=['SunlightIntensity'])
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pv_df = pd.read_csv(pv_yield_file_name, index_col='Time', usecols=['Time', 'PV yield[kW/kWp]'])
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pv_df.index = pd.to_datetime(pv_df.index)
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start_date = '2023-01-01 00:00:00' # 根据数据的实际开始日期调整
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hours = pd.date_range(start=start_date, periods=len(df_sunlight), freq='h')
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df_sunlight['Time'] = hours
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df_sunlight.set_index('Time', inplace=True)
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df_sunlight_resampled = df_sunlight.resample('15min').interpolate()
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df_power = pd.read_excel(factory_demand_file_name,
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header=None,
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names=['FactoryPower'],
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dtype={'FactoryPower': float})
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times = pd.date_range(start=start_date, periods=len(df_power), freq='15min')
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df_power['Time'] = times
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df_power.set_index('Time',inplace=True)
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print(df_power.head())
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df_combined = df_sunlight_resampled.join(df_power)
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df_power = pd.read_csv('factory_power1.csv', index_col='Time', usecols=['Time', 'FactoryPower'])
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df_power.index = pd.to_datetime(df_power.index)
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df_combined = pv_df.join(df_power)
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price_df = pd.read_csv(electricity_price_data, index_col='Time', usecols=['Time', 'ElectricityBuy'])
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price_df.index = pd.to_datetime(price_df.index)
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price_df = price_df.reindex(df_combined.index)
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print("Electricity price data generated and saved.")
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df_combined2 = df_combined.join(price_df)
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sell_df = pd.read_csv(electricity_price_data_sell, index_col='Time', usecols=['Time', 'ElectricitySell'])
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sell_df.index = pd.to_datetime(sell_df.index)
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sell_df = sell_df.reindex(df_combined.index)
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df_combined3 = df_combined2.join(sell_df)
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with open('combined_data.csv', 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow(['time', 'sunlight', 'demand','buy', 'sell'])
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writer.writerow(['time', 'PV yield[kW/kWp]', 'demand','buy', 'sell'])
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cnt = 0
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for index, row in df_combined3.iterrows():
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time_formatted = index.strftime('%H:%M')
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writer.writerow([time_formatted, row['SunlightIntensity'], row['FactoryPower'],row['ElectricityBuy'], row['ElectricitySell']])
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writer.writerow([time_formatted, row['PV yield[kW/kWp]'], row['FactoryPower'],row['ElectricityBuy'], row['ElectricitySell']])
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print('The file is written to combined_data.csv')
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# combined_data.to_csv('updated_simulation_with_prices.csv', index=False)
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print("Simulation data with electricity prices has been updated and saved.")
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75
read_data/convert_data.py
Normal file
75
read_data/convert_data.py
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@ -0,0 +1,75 @@
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#!/usr/bin/env python
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# coding: utf-8
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import os
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import csv
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def generate_min_df(mins = 15):
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end = 60/mins * 24
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start_date = '2023-01-01'
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end_date = '2023-12-31'
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all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
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all_times = pd.timedelta_range(start='0 min', end=f'1435 min', freq=f'{mins} min')
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date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]
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time_frame = pd.DataFrame({
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'Time': date_times
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})
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return time_frame
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def save_csv(df, filename, columns):
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with open(filename, 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow(['Time', 'PV yield[kW/kWp]'])
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for index, row in df.iterrows():
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time_formatted = index.strftime('%H:%M')
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writer.writerow([time_formatted, row[columns[1]]])
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print(f'The file is written to {filename}')
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def read_csv(filename):
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skip_rows = list(range(1, 17))
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data = pd.read_csv(filename, sep=';', skiprows=skip_rows)
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return data
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def process(file_name):
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df = read_csv(file_name)
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city = file_name.split('_')[0]
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remain_column = ['Time','PV energy (AC) minus standby use ']
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energy_row_name = remain_column[1]
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df = df[remain_column]
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df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)
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sum_energy = df[energy_row_name].sum()
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group_size = 15
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df['group_id'] = df.index // group_size
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sums = df.groupby('group_id')[energy_row_name].sum()
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sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')
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time_frame = generate_min_df(15)
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sums_df = pd.concat([time_frame, sums_df], axis=1)
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sums_df.set_index('Time', inplace=True)
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max_value = sums_df['Energy'].max()
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sums_df['Energy'] = sums_df['Energy'] / 390.
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sums_df['Energy'] = sums_df['Energy'].round(4)
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sums_df['Energy'].replace(0.0, -0.0)
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save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])
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if __name__ == '__main__':
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city_list = ['Riyahd', 'Cambodge', 'Berlin', 'Serbia']
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for city in city_list:
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print(f'Processing {city}')
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file_name = f'{city}_raw.csv'
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process(file_name)
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print(f'Processing {city} is done\n')
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16
xlsx2csv.py
Normal file
16
xlsx2csv.py
Normal file
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import pandas as pd
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excel_file = 'factory_power1.xlsx'
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sheet_name = 'Sheet1'
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df = pd.read_excel(excel_file, sheet_name=sheet_name)
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start_date = '2023-01-01'
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df_power = pd.read_excel(excel_file,
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header=None,
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names=['FactoryPower'],
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dtype={'FactoryPower': float})
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times = pd.date_range(start=start_date, periods=len(df_power), freq='15min')
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df_power['Time'] = times
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df_power = df_power[['Time', 'FactoryPower']]
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df_power.to_csv('factory_power1.csv', index=True)
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