写VMD-lstm分解功率的预测

VMD-LSTM是一种混合模型，将VMD分解和LSTM网络相结合，用于电力负荷预测和功率分解。以下是使用VMD-LSTM进行功率预测的代码示例：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM
from vmd import VMD

# 生成示例数据
t = np.arange(0, 1000, 1)
x = np.sin(t) + np.sin(2*t) + np.sin(5*t)  # 总功率

# VMD 分解
vmd = VMD(alpha=2000, tau=0.1, K=5, DC=0, init="rand")
u, u_hat = vmd.decompose(x)

# 准备 LSTM 数据
lookback = 10
train_size = 800
train_data = u_hat[:train_size]
test_data = u_hat[train_size:]
train_X, train_y = [], []
test_X, test_y = [], []
for i in range(len(train_data)-lookback-1):
    train_X.append(train_data[i:(i+lookback)])
    train_y.append(train_data[i+lookback])
for i in range(len(test_data)-lookback-1):
    test_X.append(test_data[i:(i+lookback)])
    test_y.append(test_data[i+lookback])

# 转换为 numpy 数组
train_X, train_y = np.array(train_X), np.array(train_y)
test_X, test_y = np.array(test_X), np.array(test_y)

# 构建 LSTM 模型
model = Sequential()
model.add(LSTM(units=50, input_shape=(train_X.shape[1], 1)))
model.add(Dense(units=1))
model.compile(optimizer="adam", loss="mean_squared_error")

# 训练 LSTM 模型
model.fit(train_X, train_y, epochs=100, batch_size=32)

# 预测测试数据
test_predict = model.predict(test_X)
test_predict = np.reshape(test_predict, (test_predict.shape[0],))

# 计算总功率和分解后的功率
total_power = np.sum(x)
vmd_power = np.sum(u_hat)

# 计算 LSTM 预测的功率
lstm_power = np.sum(np.concatenate([train_data[-lookback:], test_predict]))

# 打印结果
print("Total Power:", total_power)
print("VMD Power:", vmd_power)
print("LSTM Power:", lstm_power)

在上面的代码中，我们首先生成一个示例数据，然后使用VMD将其分解为不同的信号分量。接下来，我们将分解后的信号分量作为LSTM模型的输入，训练模型并进行预测，最后计算预测的功率并打印结果。

需要注意的是，在实际应用中，需要根据具体的数据集和任务进行调整和优化。