写一个用lstm模型预测风电功率的代码
时间: 2023-04-05 12:01:08 浏览: 120
以下是一个使用 LSTM 模型预测风电功率的 Python 代码示例:
```python
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('wind_power.csv')
# 数据预处理
data['date'] = pd.to_datetime(data['date'])
data = data.set_index('date')
data = data.resample('H').mean()
data = data.dropna()
# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data.iloc[:train_size, :]
test_data = data.iloc[train_size:, :]
# 归一化
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)
# 准备数据
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back):
X.append(dataset[i:(i+look_back), 0])
Y.append(dataset[i+look_back, 0])
return np.array(X), np.array(Y)
look_back = 24
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)
# 调整数据形状
train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))
test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))
# 构建 LSTM 模型
model = Sequential()
model.add(LSTM(50, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(train_X, train_Y, epochs=100, batch_size=64, verbose=2)
# 预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
# 反归一化
train_predict = scaler.inverse_transform(train_predict)
train_Y = scaler.inverse_transform([train_Y])
test_predict = scaler.inverse_transform(test_predict)
test_Y = scaler.inverse_transform([test_Y])
# 计算误差
from sklearn.metrics import mean_squared_error
train_error = np.sqrt(mean_squared_error(train_Y[0], train_predict[:,0]))
test_error = np.sqrt(mean_squared_error(test_Y[0], test_predict[:,0]))
print('Train RMSE: %.2f' % train_error)
print('Test RMSE: %.2f' % test_error)
```
该代码使用 LSTM 模型对风电功率进行预测,其中使用了 MinMaxScaler 进行归一化处理,使用 create_dataset 函数将数据转换为适合 LSTM 模型的格式,使用 Sequential 和 Dense 构建模型,使用 fit 函数训练模型,使用 predict 函数进行预测,最后使用 mean_squared_error 函数计算误差。