A novel two-stage seasonal grey model for residential electricity consumption forecasting

Accurate electricity consumption forecasting plays a significant role in power production and supply and power dispatching. Thus, a new hybrid model combing a grey model with fractional order accumulation, called FGM (1, 1), with seasonal factors, sine cosine algorithm (SCA), and an error correction strategy is proposed in this research. To accurately predict the seasonal fluctuations, seasonal factors are used in this model; Then, with the aim of improving the prediction performance, a SFGM (1, 1) model optimized by SCA rather than least square method, namely SCA-SFGM (1, 1), is establish to forecast electricity consumption; Moreover, considering forecasting error sequence may contain useful information, an error correction strategy is introduced to model forecasting error time series to adjust the preliminary forecasts of SCA-SFGM (1, 1). Fourth, four comparison models, three measurement criteria and a statistical hypothesis testing method using monthly residential electricity consumption dataset from 2015 to 2020 are designed to verify the prediction performance of models; Lastly, experimental results show that the mean absolute percentage error (MAPE) of the proposed model is 4.1698%, which is much lower than 14.5642%, 6.5108%, 5.9472%, 5.7060% and 4.9219% of GM (1, 1), SARIMA, SGM (1, 1), SFGM (1, 1) and SCA-SFGM (1, 1) models, respectively, showing that the proposed model can not only effectively capture seasonal fluctuations, it also adds an operational candidate forecasting benchmark model in electricity markets.