add some old code
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old/generate_electricity_price.py
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old/generate_electricity_price.py
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import pandas as pd
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import numpy as np
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start_date = '2023-01-01'
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end_date = '2024-01-01'
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# 创建时间索引
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time_index = pd.date_range(start=start_date, end=end_date, freq='15min')
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# 生成电价数据,假设电价在0.28到0.32欧元/kWh之间波动
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price_data = np.random.uniform(0.28, 0.32, len(time_index))
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# 创建DataFrame
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price_df = pd.DataFrame(data={'Time': time_index, 'ElectricityPrice': price_data})
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# 保存到CSV文件
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price_df.to_csv('electricity_price_data.csv', index=False)
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print("Electricity price data generated and saved.")
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old/generatedata.py
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old/generatedata.py
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import pandas as pd
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import numpy as np
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# 设置随机种子以重现结果
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np.random.seed(43)
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def simulate_sunlight(hour, month):
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# 假设最大日照强度在正午,根据月份调整最大日照强度
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max_intensity = 1.0 # 夏季最大日照强度
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if month in [12, 1, 2]: # 冬季
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max_intensity = 0.6
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elif month in [3, 4, 10, 11]: # 春秋
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max_intensity = 0.8
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# 计算日照强度,模拟早晚日照弱,中午日照强
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intensity = max_intensity * np.sin(np.pi * (hour - 6) / 12)**2 if 6 <= hour <= 18 else 0
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return intensity
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def simulate_factory_demand(hour, day_of_week):
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# 周末工厂需求可能减少
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if day_of_week in [5, 6]: # 周六和周日
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base_demand = 3000
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else:
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base_demand = 6000
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# 日常波动
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if 8 <= hour <= 20:
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return base_demand + np.random.randint(100, 200) # 白天需求量大
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else:
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return base_demand - np.random.randint(0, 100) # 夜间需求量小
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def generate_data(days=10):
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records = []
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month_demand = 0
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for day in range(days):
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month = (day % 365) // 30 + 1
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day_of_week = day % 7
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day_demand = 0
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for hour in range(24):
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for minute in [0, 10, 20, 30, 40, 50]:
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time = f'{hour:02d}:{minute:02d}'
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sunlight = simulate_sunlight(hour, month)
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demand = simulate_factory_demand(hour, day_of_week)
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day_demand+=demand
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records.append({'time': time, 'sunlight': sunlight, 'demand': demand})
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print(f"day:{day}, day_demand: {day_demand}")
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month_demand += day_demand
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if day%30 == 0:
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print(f"month:{month}, month_demand:{month_demand}")
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month_demand = 0
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return pd.DataFrame(records)
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# 生成数据
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data = generate_data(365) # 模拟一年的数据
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data.to_csv('simulation_data.csv', index=False)
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print("Data generated and saved to simulation_data.csv.")
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old/generatepriceschedule.py
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old/generatepriceschedule.py
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import pandas as pd
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import numpy as np
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def generate_price_schedule():
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records = []
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# 假设一天分为三个时段:谷时、平时、峰时
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times = [('00:00', '06:00', 0.25),
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('06:00', '18:00', 0.3),
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('18:00', '24:00', 0.35)]
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# 随机调整每天的电价以增加现实性
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for time_start, time_end, base_price in times:
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# 随机浮动5%以内
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fluctuation = np.random.uniform(-0.005, 0.005)
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price = round(base_price + fluctuation, 3)
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records.append({'time_start': time_start, 'time_end': time_end, 'price': price})
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return pd.DataFrame(records)
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# 生成电价计划
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price_schedule = generate_price_schedule()
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price_schedule.to_csv('price_schedule.csv', index=False)
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print("Price schedule generated and saved to price_schedule.csv.")
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print(price_schedule)
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4
old/price_schedule.csv
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old/price_schedule.csv
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time_start,time_end,price
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00:00,06:00,0.247
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06:00,18:00,0.3
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18:00,24:00,0.349
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52561
old/simulation_data.csv
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52561
old/simulation_data.csv
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