使用pytorch搭建DBN用于负荷预测

DBN可以用于负荷预测，其中输入特征是历史负荷数据，输出是未来的负荷预测。下面是一个使用PyTorch实现DBN用于负荷预测的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class DBN(nn.Module):
    def __init__(self, input_size, hidden_sizes):
        super(DBN, self).__init__()
        self.input_size = input_size
        self.hidden_sizes = hidden_sizes
        self.num_layers = len(hidden_sizes)

        # 创建RBM层
        self.rbms = nn.ModuleList()
        for i in range(self.num_layers):
            if i == 0:
                rbm = nn.Linear(self.input_size, self.hidden_sizes[i])
            else:
                rbm = nn.Linear(self.hidden_sizes[i-1], self.hidden_sizes[i])
            self.rbms.append(rbm)

    def forward(self, x):
        # 前向传播
        for i in range(self.num_layers):
            rbm = self.rbms[i]
            x = F.relu(rbm(x))
        return x

class LoadForecast(nn.Module):
    def __init__(self, input_size, hidden_sizes, output_size):
        super(LoadForecast, self).__init__()
        self.input_size = input_size
        self.hidden_sizes = hidden_sizes
        self.output_size = output_size

        # 创建DBN特征提取器
        self.dbn = DBN(input_size, hidden_sizes)

        # 创建线性层
        self.linear = nn.Linear(hidden_sizes[-1], output_size)

    def forward(self, x):
        # 将输入数据传递给DBN特征提取器
        features = self.dbn(x)

        # 将特征输入到线性层中
        output = self.linear(features)

        return output

# 创建模型
model = LoadForecast(input_size=24, hidden_sizes=[128, 64], output_size=1)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())

# 加载数据
train_data = ... # 读取训练数据
test_data = ... # 读取测试数据

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, targets) in enumerate(train_data):
        # 将输入数据和目标数据转换为Variable类型
        inputs = Variable(torch.Tensor(inputs))
        targets = Variable(torch.Tensor(targets))

        # 前向传播
        outputs = model(inputs)

        # 计算损失并进行反向传播
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # 在测试集上进行测试
    total_loss = 0
    for inputs, targets in test_data:
        inputs = Variable(torch.Tensor(inputs))
        targets = Variable(torch.Tensor(targets))
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        total_loss += loss.data[0]
    print('Epoch %d: Test Loss = %.4f' % (epoch+1, total_loss / len(test_data)))

在上面的代码中，我们首先定义了一个DBN类，其中包含多个RBM层。然后，我们创建了一个LoadForecast类，该类包含一个DBN特征提取器和一个线性层，用于将特征转换为负荷预测。在训练过程中，我们使用均方误差损失函数和Adam优化器来训练模型，并在测试集上进行测试。