改成使用pytorch库

如果你想在PyTorch库中实现类似的功能，你可以使用`torch.nn`模块中的`Conv1d`和`LSTM`层。以下是相应的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim

class WindTemperatureModel(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, lstm_units):
        super(WindTemperatureModel, self).__init__()
        
        self.conv1d = nn.Conv1d(in_channels, filters=32, kernel_size=kernel_size, padding=(kernel_size-1)//2) # 填充保证输出尺寸不变
        self.max_pool = nn.MaxPool1d(kernel_size=2)
        self.flatten = nn.Flatten()
        
        self.lstm = nn.LSTM(input_size=32, hidden_size=lstm_units, num_layers=1, batch_first=True)
        self.fc = nn.Linear(lstm_units, out_channels) # 输出层

    def forward(self, x):
        x = self.conv1d(x)
        x = F.relu(x)
        x = self.max_pool(x)
        x = self.flatten(x)
        x, _ = self.lstm(x) # 我们只取最后一个时刻的隐藏状态作为输入给全连接层
        output = self.fc(x)
        return output

# 假设输入数据和模型配置已准备好
model = WindTemperatureModel(wind_speed_channel, irradiance_channel, kernel_size=3, lstm_units=64)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss() # PyTorch中使用MSELoss而不是mean_squared_error

# 训练循环
for epoch in range(num_epochs):
    # 填充batch数据到固定长度
    padded_data = pad_sequence(data, batch_first=True)
    target = torch.stack(target_data, dim=0)

    optimizer.zero_grad()
    output = model(padded_data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

# 预测
predictions = model(new_data)

在这个例子中，你需要提供`wind_speed_channel`和`irradiance_channel`的值，以及`pad_sequence()`函数来填充输入序列。注意，PyTorch的`nn.Module`类更注重动态计算图，因此在编写模型和训练过程中略有不同。