代码生成python基于深度学习DNN+LSTM网络的光伏发电量预测

以下是一个基于深度学习DNN+LSTM网络的光伏发电量预测的Python代码示例：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout

# 读取数据
data = pd.read_csv('solar.csv')
data = data[['Date', 'Solar']]
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size, :], data.iloc[train_size:, :]

# 数据预处理
def create_dataset(dataset, look_back):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 7
trainX, trainY = create_dataset(train_data.values, look_back)
testX, testY = create_dataset(test_data.values, look_back)

# 构建DNN+LSTM模型
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=look_back))
model.add(Dropout(0.2))
model.add(Dense(units=32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(units=1))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 预测测试集
test_predict = model.predict(testX)
test_predict = np.reshape(test_predict, (test_predict.shape[0]))

# 可视化预测结果
import matplotlib.pyplot as plt
plt.plot(test_data.index, test_data['Solar'], label='Actual')
plt.plot(test_data.index[look_back+1:], test_predict, label='Predicted')
plt.legend()
plt.show()

这段代码中，首先读取光伏发电数据，并将其划分为训练集和测试集。然后对训练集和测试集进行数据预处理，将其转换为可以用于DNN+LSTM模型训练的格式。接着构建DNN+LSTM模型，并使用训练集训练模型。最后使用训练好的模型对测试集进行预测，并将预测结果可视化。