代码生成python在分析光伏发电原理的基础上,论证了辐照度、光伏板工作温度等影响光伏输出功率的因素,通过实时监测的光伏板运行状态参数和气象参数建立预测模型,预估光伏电站瞬时发电量,根据光伏电站DCS系统提供的实际发电量数据进行对比分析,验证模型的实际应用价值
时间: 2024-05-17 11:15:06 浏览: 24
这是一个非常有用的应用,通过监测光伏板的运行状态参数和气象参数,建立预测模型,可以帮助光伏电站提高发电效率,减少能源浪费。在预测模型的建立过程中,需要考虑多种因素,如辐照度、光伏板工作温度等,这些因素都会影响光伏输出功率。通过实时监测和预测,可以更加准确地预估光伏电站瞬时发电量,进而优化光伏电站的运行效率。同时,通过与实际发电量数据进行对比分析,可以验证预测模型的准确性和实际应用价值。
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代码生成python基于LSTM的光伏发电预测
以下是一个基于LSTM的光伏发电预测的Python代码示例:
```
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
# 读取数据
data = pd.read_csv('solar.csv')
data = data[['Date', 'Solar']]
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)
# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size, :], data.iloc[train_size:, :]
# 数据预处理
def create_dataset(dataset, look_back):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
look_back = 7
trainX, trainY = create_dataset(train_data.values, look_back)
testX, testY = create_dataset(test_data.values, look_back)
# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=50, input_shape=(look_back, 1)))
model.add(Dense(units=1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# 预测测试集
test_predict = model.predict(testX)
test_predict = np.reshape(test_predict, (test_predict.shape[0]))
# 可视化预测结果
import matplotlib.pyplot as plt
plt.plot(test_data.index, test_data['Solar'], label='Actual')
plt.plot(test_data.index[look_back+1:], test_predict, label='Predicted')
plt.legend()
plt.show()
```
这段代码中,首先读取光伏发电数据,并将其划分为训练集和测试集。然后对训练集和测试集进行数据预处理,将其转换为可以用于LSTM模型训练的格式。接着构建LSTM模型,并使用训练集训练模型。最后使用训练好的模型对测试集进行预测,并将预测结果可视化。
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:
```python
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。
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