电力系统短期负荷预测（Python代码+数据+详细文章讲解）

news2024/11/25 4:47:11

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🌈3 Python代码+数据+文章详细讲解

🎉4 参考文献

💥1 概述

本文包括以下5个部分

模型构建：

训练出模型，然后就可以预测任意一天的96个时刻点的负荷。

📚2 运行结果

预测日：

部分代码：

max_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '最高温度'), [2]].values / 20
min_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '最低温度'), [2]].values / 20
avg_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '平均温度'), [2]].values / 20
humidity = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '湿度'), [2]].values / 100
weather_data = np.concatenate([max_tempe, min_tempe, avg_tempe, humidity]).reshape((1, -1))


type_of_day = np.eye(7)[date.dayofweek]
holiday = np.eye(2)[int(is_holiday(date))]
time_data = np.concatenate([type_of_day, holiday]).reshape((1, -1))


features = np.concatenate([load_data, weather_data, time_data], axis=1).reshape(1, 685)
features = Tensor(features)

print('Loading model...')
net = load('./model.pt', map_location='cpu')

print('Start predicting...')
net.eval()
labels = net(features).detach().numpy() * 7000
print('=========预测==============')

max_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '最高温度'), [2]].values / 20
min_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '最低温度'), [2]].values / 20
avg_tempe = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '平均温度'), [2]].values / 20
humidity = df_weather.loc[(df_weather[0] == predict_date) & (df_weather[1] == '湿度'), [2]].values / 100
weather_data = np.concatenate([max_tempe, min_tempe, avg_tempe, humidity]).reshape((1, -1))


type_of_day = np.eye(7)[date.dayofweek]
holiday = np.eye(2)[int(is_holiday(date))]
time_data = np.concatenate([type_of_day, holiday]).reshape((1, -1))


features = np.concatenate([load_data, weather_data, time_data], axis=1).reshape(1, 685)
features = Tensor(features)

print('Loading model...')
net = load('./model.pt', map_location='cpu')

print('Start predicting...')
net.eval()
labels = net(features).detach().numpy() * 7000
print('=========预测==============')