写一段代码使用LSTM根据天气数据对风电场输出功率进行预测

以下是使用LSTM进行风电场输出功率预测的代码：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 获取天气数据
weather_data = pd.read_csv("weather_data.csv")

# 获取风电场输出功率数据
power_data = pd.read_csv("power_data.csv")

# 合并数据
combined_data = pd.merge(weather_data, power_data, on="datetime")

# 对合并后的数据进行预处理
# 去除一些不必要的列
combined_data = combined_data.drop(columns=["datetime", "dew_point", "pressure", "temperature_min", "temperature_max"])
# 重命名列
combined_data = combined_data.rename(columns={"temperature": "temp", "visibility": "vis", "wind_speed": "wind"})

# 将数据分为训练集和测试集
train_size = int(len(combined_data) * 0.8)
train_data = combined_data[:train_size]
test_data = combined_data[train_size:]

# 对数据进行归一化
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
train_data_scaled = scaler.fit_transform(train_data)
test_data_scaled = scaler.transform(test_data)

# 创建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(train_data_scaled.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(1))

# 编译模型
model.compile(loss="mean_squared_error", optimizer="adam")

# 训练模型
X_train = []
y_train = []
for i in range(60, len(train_data_scaled)):
    X_train.append(train_data_scaled[i-60:i, 0])
    y_train.append(train_data_scaled[i, 0])
X_train = np.array(X_train)
y_train = np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
model.fit(X_train, y_train, epochs=100, batch_size=32)

# 使用模型进行预测
inputs = combined_data[len(combined_data) - len(test_data) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = scaler.transform(inputs)

X_test = []
for i in range(60, inputs.shape[0]):
    X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

predicted = model.predict(X_test)
predicted = scaler.inverse_transform(predicted)

# 画出结果
import matplotlib.pyplot as plt

plt.plot(test_data["power_output"].values, label="Actual Power")
plt.plot(predicted, label="Predicted Power")
plt.legend()
plt.show()

其中，`weather_data.csv` 是包含天气数据的 CSV 文件，`power_data.csv` 是包含风电场输出功率数据的 CSV 文件。此代码使用了 LSTM 模型对天气数据和风电场输出功率数据进行预测，输出的是预测的风电场输出功率数据。