Discussion on wind power scenario generation method

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GAN network can be used to generate wind power output scenarios. GAN network is a deep learning model. It is composed of two neural networks, a generator network and a discriminator network, which learn together to generate data conforming to the actual wind power output scenario.

For your question, you can train the GAN network using 100 hours of output data from the six wind farms you already have. You can enter this data as real data into the discriminator network, and the generator network will generate fake data that looks like real data. During training, the network of discriminators and the network of generators play games with each other, constantly adjusting parameters until the generated data is realistic enough that it cannot be distinguished by the discriminator as real or fake.

Once the GAN network training is complete, you can use the generator network to generate more wind power output scenarios. All you need to do is provide some noise signals as input, and the generator network will output a new wind power output scenario.

It is important to note that the data generated using GAN networks may not be completely realistic because the model may generate some patterns that are different from the real scenario. Therefore, you need to further validate and calibrate the generated data to ensure that it is realistic.

Here is the complete example code for a GAN network generating wind power output scenario using PyTorch. Due to the lack of specific wind power output data, MNIST handwritten digit data set is used here for illustration.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np

# 超参数
batch_size = 128 # 批次大小
noise_dim = 100 # 噪声向量维度
lr = 0.0002 # 学习率
beta1 = 0.5 # Adam优化器参数
epochs = 200 # 训练轮数

# 加载MNIST数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)

# 定义判别器网络
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = x.view(-1, 28*28)
        x = self.model(x)
        return x

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(noise_dim, 256),
            nn.LeakyReLU(0.2),
            nn.BatchNorm1d(256),
            nn.Linear(256, 512),
            nn.LeakyReLU(0.2),
            nn.BatchNorm1d(512),
            nn.Linear(512, 28*28),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.model(x)
        x = x.view(-1, 1, 28, 28)
        return x

# 创建判别器和生成器网络实例
D = Discriminator()
G = Generator()

# 定义损失函数和优化器
criterion = nn.BCELoss()
d_optimizer = optim.Adam(D.parameters(), lr=lr, betas=(beta1, 0.999))
g_optimizer = optim.Adam(G.parameters(), lr=lr, betas=(beta1, 0.999))

# 训练GAN网络
d_losses = []
g_losses = []
for epoch in range(epochs):
    for i, (real_images, _) in enumerate(trainloader):
        # 训练判别器
        D.zero_grad()

        # 真实数据
        real_images = real_images.to(device)
        batch_size = real_images.size(0)
        labels = torch.ones(batch_size, 1).to(device)
        output = D(real_images)
        d_loss_real = criterion(output, labels)

        # 生成假数据
        noise = torch.randn(batch_size, noise_dim).to(device)
        fake_images = G(noise)
        labels.fill_(0)
        output = D(fake_images.detach())
        d_loss_fake = criterion(output, labels)

               # 计算判别器总损失
        d_loss = d_loss_real + d_loss_fake
        d_loss.backward()
        d_optimizer.step()

        # 训练生成器
        G.zero_grad()
        labels.fill_(1)
        output = D(fake_images)
        g_loss = criterion(output, labels)
        g_loss.backward()
        g_optimizer.step()

        # 打印损失
        if i == len(trainloader)-1:
            print('[%d/%d] d_loss: %.3f, g_loss: %.3f' % (epoch+1, epochs, d_loss.item(), g_loss.item()))
            d_losses.append(d_loss.item())
            g_losses.append(g_loss.item())

    # 生成示例图片
    G.eval()
    with torch.no_grad():
        noise = torch.randn(64, noise_dim).to(device)
        fake_images = G(noise).cpu()

    fig, ax = plt.subplots(figsize=(8, 8))
    for j in range(64):
        ax[j // 8, j % 8].imshow(fake_images[j, 0], cmap='gray')
        ax[j // 8, j % 8].axis('off')
    plt.show()

# 绘制损失曲线
plt.plot(d_losses, label='Discriminator')
plt.plot(g_losses, label='Generator')
plt.legend()
plt.show()