用kreas编写BILSTM对电力数据进行预测的代码

好的，以下是用keras编写BILSTM对电力数据进行预测的代码：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Bidirectional
from sklearn.preprocessing import MinMaxScaler

# 读取数据
df = pd.read_csv('electricity_data.csv')

# 数据预处理
scaler = MinMaxScaler()
data = scaler.fit_transform(df['electricity'].values.reshape(-1, 1))

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

# 定义函数，将数据集转换为时间序列数据集
def create_dataset(dataset, look_back=1):
    data_X, data_Y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        data_X.append(a)
        data_Y.append(dataset[i + look_back, 0])
    return np.array(data_X), np.array(data_Y)

# 构建时间序列数据集
look_back = 10
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)

# 将数据集重构为LSTM需要的格式
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))

# 构建BILSTM模型
model = Sequential()
model.add(Bidirectional(LSTM(50, input_shape=(1, look_back))))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

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

# 预测测试集
test_predict = model.predict(test_X)
test_predict = scaler.inverse_transform(test_predict)

# 计算均方根误差（RMSE）
rmse = np.sqrt(np.mean((test_predict - test_Y)**2))
print('Test RMSE: %.3f' % rmse)

注意，这只是一个简单的示例代码，实际应用中可能需要更多的数据预处理、调参等步骤。