add some old code

old/generate_electricity_price.py

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+import pandas as pd
+import numpy as np
+
+start_date = '2023-01-01'
+end_date = '2024-01-01'
+
+# 创建时间索引
+time_index = pd.date_range(start=start_date, end=end_date, freq='15min')
+
+# 生成电价数据，假设电价在0.28到0.32欧元/kWh之间波动
+price_data = np.random.uniform(0.28, 0.32, len(time_index))
+
+# 创建DataFrame
+price_df = pd.DataFrame(data={'Time': time_index, 'ElectricityPrice': price_data})
+
+# 保存到CSV文件
+price_df.to_csv('electricity_price_data.csv', index=False)
+
+print("Electricity price data generated and saved.")

old/generatedata.py

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+import pandas as pd
+import numpy as np
+
+# 设置随机种子以重现结果
+np.random.seed(43)
+
+def simulate_sunlight(hour, month):
+    # 假设最大日照强度在正午，根据月份调整最大日照强度
+    max_intensity = 1.0  # 夏季最大日照强度
+    if month in [12, 1, 2]:  # 冬季
+        max_intensity = 0.6
+    elif month in [3, 4, 10, 11]:  # 春秋
+        max_intensity = 0.8
+    
+    # 计算日照强度，模拟早晚日照弱，中午日照强
+    intensity = max_intensity * np.sin(np.pi * (hour - 6) / 12)**2 if 6 <= hour <= 18 else 0
+    return intensity
+
+def simulate_factory_demand(hour, day_of_week):
+    # 周末工厂需求可能减少
+    if day_of_week in [5, 6]:  # 周六和周日
+        base_demand = 3000
+    else:
+        base_demand = 6000
+    
+    # 日常波动
+    if 8 <= hour <= 20:
+        return base_demand + np.random.randint(100, 200)  # 白天需求量大
+    else:
+        return base_demand - np.random.randint(0, 100)  # 夜间需求量小
+
+def generate_data(days=10):
+    records = []
+    month_demand = 0
+    for day in range(days):
+        month = (day % 365) // 30 + 1
+        day_of_week = day % 7
+        day_demand = 0
+        for hour in range(24):
+            for minute in [0, 10, 20, 30, 40, 50]:
+                time = f'{hour:02d}:{minute:02d}'
+                sunlight = simulate_sunlight(hour, month)
+                demand = simulate_factory_demand(hour, day_of_week)
+                day_demand+=demand
+                records.append({'time': time, 'sunlight': sunlight, 'demand': demand})
+        print(f"day:{day}, day_demand: {day_demand}")
+        month_demand += day_demand
+        if day%30 == 0:
+            print(f"month:{month}, month_demand:{month_demand}")
+            month_demand = 0
+    return pd.DataFrame(records)
+
+# 生成数据
+data = generate_data(365)  # 模拟一年的数据
+data.to_csv('simulation_data.csv', index=False)
+print("Data generated and saved to simulation_data.csv.")

old/generatepriceschedule.py

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+import pandas as pd
+import numpy as np
+
+def generate_price_schedule():
+    records = []
+    # 假设一天分为三个时段：谷时、平时、峰时
+    times = [('00:00', '06:00', 0.25),  
+             ('06:00', '18:00', 0.3),  
+             ('18:00', '24:00', 0.35)]  
+    
+    # 随机调整每天的电价以增加现实性
+    for time_start, time_end, base_price in times:
+        # 随机浮动5%以内
+        fluctuation = np.random.uniform(-0.005, 0.005)
+        price = round(base_price + fluctuation, 3)
+        records.append({'time_start': time_start, 'time_end': time_end, 'price': price})
+    
+    return pd.DataFrame(records)
+
+# 生成电价计划
+price_schedule = generate_price_schedule()
+price_schedule.to_csv('price_schedule.csv', index=False)
+print("Price schedule generated and saved to price_schedule.csv.")
+print(price_schedule)

old/price_schedule.csv

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+time_start,time_end,price
+00:00,06:00,0.247
+06:00,18:00,0.3
+18:00,24:00,0.349