代码生成python基于transformer的光伏发电预测

以下是一个基于Transformer的光伏发电预测模型的代码示例：

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from torch.utils.data import Dataset, DataLoader

# 定义Transformer模型
class Transformer(nn.Module):
    def __init__(self, input_dim, output_dim, d_model, nhead, num_layers):
        super(Transformer, self).__init__()

        self.encoder = nn.Linear(input_dim, d_model)
        self.decoder = nn.Linear(d_model, output_dim)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model, nhead), num_layers
        )

    def forward(self, x):
        x = self.encoder(x)
        x = x.permute(1, 0, 2)
        x = self.transformer(x)
        x = x.permute(1, 0, 2)
        x = self.decoder(x)
        return x[:, -1, :]

# 定义数据集
class PVData(Dataset):
    def __init__(self, data, target):
        self.data = data
        self.target = target

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.target[idx]

# 数据预处理
data = pd.read_csv('pv_data.csv')
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data_scaled) * 0.8)
train_data = data_scaled[:train_size, :]
test_data = data_scaled[train_size:, :]

# 准备训练数据和测试数据
train_x, train_y = train_data[:, :-1], train_data[:, -1]
test_x, test_y = test_data[:, :-1], test_data[:, -1]
train_dataset = PVData(train_x, train_y)
test_dataset = PVData(test_x, test_y)

# 定义超参数
input_dim = train_x.shape[1]
output_dim = 1
d_model = 128
nhead = 8
num_layers = 6
batch_size = 64
epochs = 100

# 初始化模型和优化器
model = Transformer(input_dim, output_dim, d_model, nhead, num_layers)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# 训练模型
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
    for x, y in train_loader:
        optimizer.zero_grad()
        y_pred = model(x.float())
        loss = criterion(y_pred.squeeze(), y.float())
        loss.backward()
        optimizer.step()
    print(f'epoch: {epoch+1}, loss: {loss.item():.4f}')

# 测试模型
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
y_pred = []
with torch.no_grad():
    for x, _ in test_loader:
        pred = model(x.float())
        y_pred.append(pred.squeeze().numpy())
y_pred = np.concatenate(y_pred)
test_y = scaler.inverse_transform(test_y.reshape(-1, 1))
y_pred = scaler.inverse_transform(y_pred.reshape(-1, 1))

# 计算MSE和MAE
mse = np.mean((test_y - y_pred)**2)
mae = np.mean(np.abs(test_y - y_pred))
print(f'Test MSE: {mse:.4f}, Test MAE: {mae:.4f}')

其中，'pv_data.csv'是存储光伏发电数据的文件名。该代码通过Transformer模型对光伏发电数据进行预测，并计算预测结果的MSE和MAE。