Comparative study of short-term electrical load forecast models

In order to solve the problems of electrical load prediction performance improvement, more efforts are being made to apply artificial intelligence methods in electrical load prediction. Using the electricity load data of Hunan Province from 2014 to 2017, the autoregressive (AR) model, BP neural network (BPNN), and exponential smoothing (ES) model were compared in terms of their performance of predicting both daily and monthly electrical load, respectively, and analyze the differences among the aforementioned three models. According to the experimental results, it was that the autoregressive model performs better in daily predictions than the other two models, while the exponential smoothness model gives better monthly predictions.