lstm时间序列预测python发电量

以下是使用多层LSTM网络预测电力发电量的Python代码示例：

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

# 读取数据
data = pd.read_csv('power.csv', usecols=[1])
dataset = data.values
dataset = dataset.astype('float32')

# 数据归一化
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 将数据集分为训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# 将时间序列数据转换为监督学习问题
def create_dataset(dataset, look_back=1):
    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 = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 将数据重塑为LSTM网络的输入格式
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# 创建LSTM网络模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back), return_sequences=True))
model.add(LSTM(4, return_sequences=True))
model.add(LSTM(4))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 使用训练好的模型进行预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 将预测结果反归一化
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

# 计算均方根误差
from sklearn.metrics import mean_squared_error
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))