lstm预训练微调预测光伏出力代码

以下是使用Pytorch实现LSTM预训练、微调和预测光伏出力的代码示例：

import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, 1)
        
    def forward(self, x):
        out, _ = self.lstm(x)
        out = self.fc(out[:, -1, :])
        return out

# 定义数据集类
class PVData(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        return self.data[idx]
    
# 定义预训练函数
def pretrain(model, data_loader, optimizer, criterion, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for data in data_loader:
            optimizer.zero_grad()
            output = model(data.unsqueeze(0))
            loss = criterion(output, data.unsqueeze(-1))
            loss.backward()
            optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 定义微调函数
def finetune(model, data_loader, optimizer, criterion, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for data in data_loader:
            optimizer.zero_grad()
            output = model(data.unsqueeze(0))
            loss = criterion(output, data.unsqueeze(-1))
            loss.backward()
            optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 定义预测函数
def predict(model, data):
    model.eval()
    with torch.no_grad():
        output = model(data.unsqueeze(0))
    return output.item()

# 准备数据
data = [1.2, 2.5, 3.0, 2.8, 1.5, 0.8, 0.9, 1.5, 2.0, 2.4, 2.2, 1.8, 1.2, 0.5, 0.3, 0.6]
train_data = data[:10]
test_data = data[10:]
train_dataset = PVData(train_data)
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)

# 预训练
input_size = 1
hidden_size = 8
num_layers = 2
model = LSTMModel(input_size, hidden_size, num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
pretrain(model, train_loader, optimizer, criterion, num_epochs=50)

# 微调
finetune_dataset = PVData(test_data)
finetune_loader = DataLoader(finetune_dataset, batch_size=1, shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
finetune(model, finetune_loader, optimizer, criterion, num_epochs=50)

# 预测
test_input = torch.tensor([2.5, 2.8, 1.5])
output = predict(model, test_input)
print('Predicted output:', output)

在这个例子中，我们使用了一个简单的数据集，其中包含了16个光伏出力数据。我们将前10个数据用于预训练，后6个数据用于微调和预测。在预训练中，我们使用了50个epoch进行训练，学习率为0.01；在微调中，我们使用了50个epoch进行训练，学习率为0.001。最后，我们使用模型对数据集中的一段数据进行了预测。