add generate price and read data

read_data.py

@@ -0,0 +1,52 @@
+import pandas as pd
+import numpy as np
+import csv
+
+df_sunlight = pd.read_excel('lightintensity.xlsx', header=None, names=['SunlightIntensity'])
+
+start_date = '2023-01-01 00:00:00'  # 根据数据的实际开始日期调整
+hours = pd.date_range(start=start_date, periods=len(df_sunlight), freq='h')
+df_sunlight['Time'] = hours
+df_sunlight.set_index('Time', inplace=True)
+
+df_sunlight_resampled = df_sunlight.resample('15min').interpolate()
+
+df_power = pd.read_excel('factory_power.xlsx', 
+                         header=None, 
+                         names=['FactoryPower'], 
+                         dtype={'FactoryPower': float})
+times = pd.date_range(start=start_date, periods=len(df_power), freq='15min')
+df_power['Time'] = times
+df_power.set_index('Time',inplace=True)
+print(df_power.head())
+
+df_combined = df_sunlight_resampled.join(df_power)
+
+df_combined.to_csv('combined_data.csv', index=True, index_label='Time')
+
+price_data = np.random.uniform(0.3, 0.3, len(times))
+
+# 创建DataFrame
+price_df = pd.DataFrame(data={'Time': times, 'ElectricityPrice': price_data})
+
+price_df.set_index('Time', inplace=True)
+
+# 保存到CSV文件
+price_df.to_csv('electricity_price_data.csv', index=True)
+print(price_df.head())
+print("Electricity price data generated and saved.")
+
+df_combined2 = df_combined.join(price_df)
+print(df_combined2.head())
+# 保存结果
+with open('combined_data.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    writer.writerow(['time', 'sunlight', 'demand','price'])
+    for index, row in df_combined2.iterrows():
+        time_formatted = index.strftime('%H:%M')
+        writer.writerow([time_formatted, row['SunlightIntensity'], row['FactoryPower'],row['ElectricityPrice']])
+    print('The file is written to combined_data.csv')
+
+# combined_data.to_csv('updated_simulation_with_prices.csv', index=False)
+
+print("Simulation data with electricity prices has been updated and saved.")