In order to screen out the effective information contained in massive data and improve the accuracy of short-term load forecasting

a hybrid model of short-term load forecasting method based on convolutional neural network(CNN)and long short-term memory(LSTM)network is proposed due to the timing and nonlinear characteristics of load data

which takes massive historical load data

meteorological data

date information and peak-valley electricity price data as input by constructing a continuous feature map of time sliding window. Firstly

CNN is used to extract feature vectors. The feature vectors are constructed as time series and used as input data for LSTM network

which is utilized to forecast the short-term load. The proposed method is used to predict the power load data of an area in Jiangsu province. The experimental results show that the proposed prediction method has higher prediction accuracy than the traditional load forecasting method

the random forest forecasting method and the standard LSTM network forecasting method.