请给我写一个关于基于分位数回归的LSTM光伏功率预测的python代码

以下是一个基于分位数回归的LSTM光伏功率预测的Python代码，其中使用了Keras框架和pandas库：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# 导入数据
data = pd.read_csv('solar.csv', header=0, parse_dates=[0], index_col=0, squeeze=True)

# 数据预处理
dataset = data.values.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 划分数据集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# 将时间序列转化为监督学习问题
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 3
train_X, train_Y = create_dataset(train, look_back)
test_X, test_Y = create_dataset(test, look_back)

# LSTM模型
def create_model(optimizer='adam', activation='relu', neurons=4, dropout_rate=0.1):
    model = Sequential()
    model.add(LSTM(neurons, input_shape=(look_back, 1)))
    model.add(Dropout(dropout_rate))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer=optimizer)
    return model

model = KerasRegressor(build_fn=create_model, verbose=0)

# 网格搜索调参
optimizers = ['adam', 'rmsprop']
activations = ['relu', 'tanh', 'sigmoid']
neurons = [4, 8, 12]
dropout_rates = [0.1, 0.2, 0.3]

param_grid = dict(optimizer=optimizers, activation=activations, neurons=neurons, dropout_rate=dropout_rates)
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(train_X, train_Y)

# 最优参数
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

# 模型预测
train_predict = grid.predict(train_X)
test_predict = grid.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RMSE
train_score = np.sqrt(mean_squared_error(train_Y[0], train_predict[:,0]))
print('Train Score: %.2f RMSE' % (train_score))
test_score = np.sqrt(mean_squared_error(test_Y[0], test_predict[:,0]))
print('Test Score: %.2f RMSE' % (test_score))

# 绘制预测结果和实际结果的图像
train_predict_plot = np.empty_like(dataset)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict)+look_back, :] = train_predict

test_predict_plot = np.empty_like(dataset)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(look_back*2)+1:len(dataset)-1, :] = test_predict

dataset = scaler.inverse_transform(dataset)

plt.plot(dataset, label='actual')
plt.plot(train_predict_plot, label='train')
plt.plot(test_predict_plot, label='test')
plt.legend()
plt.show()

注意，此代码中的数据集应该是一个CSV文件，其中包含一列时间戳和一列光伏功率。此外，这个代码还使用了GridSearchCV函数来对LSTM模型的超参数进行网格搜索调参。