PyTorch Informer 模型训练和测试示例：使用柳林数据集

本代码示例展示了如何使用 PyTorch 训练 Informer 模型来预测光伏发电功率，并使用柳林数据集进行训练和测试。代码涵盖了数据集读取、预处理、模型训练、评估等步骤。

# Read dataset
df = pd.read_csv('./data/柳林.csv')

# Split dataset into train, valid and test sets
train_size = int(len(df) * 0.6)
valid_size = int(len(df) * 0.2)
test_size = len(df) - train_size - valid_size

train_df = df[:train_size]
valid_df = df[train_size:train_size+valid_size]
test_df = df[train_size+valid_size:]

# Normalize dataset
train_mean = train_df.mean()
train_std = train_df.std()

train_df = (train_df - train_mean) / train_std
valid_df = (valid_df - train_mean) / train_std
test_df = (test_df - train_mean) / train_std

# Prepare data for model training
train_x = torch.tensor(train_df[['辐照度', '温度', '湿度', '风速']].values, dtype=torch.float32)
train_y = torch.tensor(train_df[['功率']].values, dtype=torch.float32)

valid_x = torch.tensor(valid_df[['辐照度', '温度', '湿度', '风速']].values, dtype=torch.float32)
valid_y = torch.tensor(valid_df[['功率']].values, dtype=torch.float32)

test_x = torch.tensor(test_df[['辐照度', '温度', '湿度', '风速']].values, dtype=torch.float32)
test_y = torch.tensor(test_df[['功率']].values, dtype=torch.float32)

# 最后，我们需要定义损失函数和优化器，并对模型进行训练。
# Instantiate model, loss function and optimizer
model = Informer(input_size=4, output_size=1, enc_hid_dim=128, dec_hid_dim=64, n_enc_layers=3, n_dec_layers=2, n_heads=8, pf_dim=256, dropout=0.1)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())

# Train model
num_epochs = 100
batch_size = 64
num_batches = len(train_x) // batch_size

for epoch in range(num_epochs):
    train_loss = 0.0
    valid_loss = 0.0
    
    model.train()
    for i in range(num_batches):
        optimizer.zero_grad()
        
        batch_x = train_x[i*batch_size:(i+1)*batch_size]
        batch_y = train_y[i*batch_size:(i+1)*batch_size]
        
        output = model(batch_x, batch_y)
        loss = criterion(output, batch_y)
        loss.backward()
        optimizer.step()
        
        train_loss += loss.item()
    
    model.eval()
    with torch.no_grad():
        output = model(valid_x, valid_y)
        valid_loss = criterion(output, valid_y).item()
    
    print(f'Epoch {epoch+1}, train_loss: {train_loss/num_batches:.4f}, valid_loss: {valid_loss:.4f}')

# Test model
model.eval()
with torch.no_grad():
    output = model(test_x, test_y)
    test_loss = criterion(output, test_y).item()

print(f'Test loss: {test_loss:.4f}')

注意:

该代码示例假设您已经安装了必要的库，例如 PyTorch、pandas 等。
您需要将 './data/柳林.csv' 替换为实际数据集的路径。
代码中的 Informer 类需要您根据自己的模型结构进行定义。
该代码示例仅提供基本框架，您可能需要根据实际情况进行修改。

希望该示例能够帮助您理解如何使用 PyTorch 训练 Informer 模型来预测光伏发电功率。