Forecasting electricity demand using a new grey prediction model with smoothness operator

Abstract

A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction.