基于LSTM的空调负荷预测及其Python实现的代码
时间: 2024-04-22 21:22:08 浏览: 7
好的,下面是基于LSTM的空调负荷预测的Python代码参考:
```python
# 导入相关的库
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
# 读入数据
data = pd.read_csv('data.csv')
# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(data)
# 划分数据集
train_size = int(len(data_scaled) * 0.7)
test_size = len(data_scaled) - train_size
train_data, test_data = data_scaled[0:train_size,:], data_scaled[train_size:len(data_scaled),:]
# 准备数据集
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 = 7 # 每次使用前7天数据进行预测
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)
# 将输入数据重塑为 [样本数, 时间步长, 特征数] 的三维张量
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(1, look_back)))
model.add(LSTM(units=50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)
# 进行预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
# 反归一化处理
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
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))
# 绘制预测结果与真实值的曲线图
train_predict_plot = np.empty_like(data_scaled)
train_predict_plot[:, :] = np.nan
train_predict_plot[look_back:len(train_predict)+look_back, :] = train_predict
test_predict_plot = np.empty_like(data_scaled)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(look_back*2)+1:len(data_scaled)-1, :] = test_predict
plt.plot(scaler.inverse_transform(data_scaled), label='True')
plt.plot(train_predict_plot, label='Train Predict')
plt.plot(test_predict_plot, label='Test Predict')
plt.legend()
plt.show()
```
这是一个简单的示例代码,您可以根据自己的实际情况进行调整和优化。同时,您需要根据实际情况修改读取数据的方式,以及数据预处理、模型训练和评估等步骤。