Short-term power load comparison based on time series and neural networks considering multiple features

Abstract

In order to achieve a certain balance between power supply and production demand, as well as to ensure the operation of social machinery and multiple markets, accurate power load forecasting is indispensable. A load prediction method based on the Convolutional Neural Network (CNN) - the Long Short-Term Memory (LSTM) and the CNN-gated Recurrent Unit (GRU) is proposed. The multi-feature load forecasting is constructed by the date factor, the weather factor, the load factor and the electricity price factor. The advantage of CNN in feature extraction of the data set is used to establish a high-dimensional relationship with load, optimize the input LSTM and GRU network model, train each group of the neural network model, and output load prediction value. Combining the load data of a certain area with its meteorological factors, the CNN-LSTM and CNN-GRU methods are tested and compared with the single-network models of Back Propagation (BP), LSTM and GRU. By comparing it with other popular algorithms, it is proved that the presented model has superior computational efficiency and prediction accuracy.