A compound deep learning model for long range forecasting in electricity sale

Accurate prediction of electricity sale has a positive effect on power companies in rationally arranging power supply plans, scientifically optimizing power resource allocation, improving power management efficiency, saving energy and reducing consumption. Predicting future electricity sale based on historical electricity sale data can essentially be summarized as a time series forecasting problem. This paper proposes a fast and memory-efficient method, which adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) for long range forecasting in electricity sale. Through a large number of experiments and evaluation of real-world datasets, the effectiveness of the proposed method is proved and verified in terms of prediction accuracy, time consuming and training speed.