Comparative Research on Electricity Consumption Forecast Based on Deep Learning

Abstract

In order to save unnecessary energy losses, power companies need to accurately predict electricity consumption. Various classification methods are used for electricity consumption forecasting: traditional time series models, deep neural network models based on Artificial Intelligence, and hybrid models. Most scholars use a single view to forecast electricity consumption. This article predicts electricity consumption from three views: traditional models, deep neural network models, and hybrid models. First, the electricity consumption time series is preprocessed. Then, from three views, models that have achieved good performance in some time series are selected, and then the forecast analysis of the model is given. Finally, the performance of different models in electricity consumption forecasting is evaluated and compared. The results show that the expected performance of the hybrid model is not as good as the traditional model and the deep neural network model in electricity consumption forecasting.