请给我一个xgboost与lstm组合预测的风电功率预测算法，要求使用python tensorflow框架实现

首先，我们需要对数据进行预处理。以风电功率预测为例，我们需要将历史数据划分为训练集和测试集，然后将其转化为序列数据，每个序列包括前一天的功率数据和气象数据。同时，我们需要将功率数据进行归一化处理。

接下来，我们使用LSTM模型对序列数据进行建模，并将其训练。

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

# 读取数据
data = pd.read_csv('data.csv')
data = data.dropna()

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

# 归一化处理
max_power = train_data['power'].max()
min_power = train_data['power'].min()
train_data['power'] = (train_data['power'] - min_power) / (max_power - min_power)
test_data['power'] = (test_data['power'] - min_power) / (max_power - min_power)

# 将序列数据转化为二维数组
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), :]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 7
train_X, train_Y = create_dataset(train_data[['power', 'temperature', 'humidity']].values, look_back)
test_X, test_Y = create_dataset(test_data[['power', 'temperature', 'humidity']].values, look_back)

# LSTM模型
model = Sequential()
model.add(LSTM(64, input_shape=(look_back, 3), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(32, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(train_X, train_Y, epochs=50, batch_size=16, verbose=2)

接下来，我们使用XGBoost模型对气象数据进行建模，并将其训练。

import xgboost as xgb

# XGBoost模型
train_X = train_data[['temperature', 'humidity']].values
train_Y = train_data['power'].values
test_X = test_data[['temperature', 'humidity']].values
test_Y = test_data['power'].values

xgb_model = xgb.XGBRegressor(n_estimators=100, max_depth=3, learning_rate=0.1)
xgb_model.fit(train_X, train_Y)

最后，我们将LSTM和XGBoost模型的预测结果进行组合，得到最终的预测结果。

# 组合预测
lstm_pred = model.predict(test_X)
xgb_pred = xgb_model.predict(test_X[:, 1:])
test_Y = test_Y.reshape(-1, 1)

pred_Y = lstm_pred * (max_power - min_power) + min_power + xgb_pred
test_Y = test_Y * (max_power - min_power) + min_power

# 评估模型
from sklearn.metrics import r2_score, mean_squared_error

print('R2 score:', r2_score(test_Y, pred_Y))
print('MSE:', mean_squared_error(test_Y, pred_Y))