An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting - Wang, Jianzhou; Du, Pei; Lu, Haiyan; Yang, Wendong; Niu, Tong (Applied soft computing, 2018)
•A dynamic choice rolling GM (1, 1) (DCRGM (1, 1)) is successfully developed.•Proposed a novel hybrid forecasting model based on MOALO and DCRGM (1, 1).•Small sample and extended versions of the grey model are discussed in this study.•The proposed hybrid model demonstrates higher prediction accuracy. Accurate and stable annual electricity consumption forecasting play vital role in modern social and economic development through providing effective planning and guaranteeing a reliable supply of sustainable electricity. However, establishing a robust method to improve prediction accuracy and stability simultaneously of electricity consumption forecasting has been proven to be a highly challenging task. Most previous researches only pay more attention to enhance prediction accuracy, which usually ignore the significant of forecasting stability, despite its importance to the effectiveness of forecasting models. Considering the characteristics of annual power consumption data as well as one criterion i.e. accuracy or stability is insufficient, in this study a novel hybrid forecasting model based on an improved grey forecasting mode optimized by multi-objective ant lion optimization algorithm is successfully developed, which can not only be utilized to dynamic choose the best input training sets, but also obtain satisfactory forecasting results with high accuracy and strong ability. Case studies of annual power consumption datasets from several regions in China are utilized as illustrative examples to estimate the effectiveness and efficiency of the proposed hybrid forecasting model. Finally, experimental results indicated that the proposed forecasting model is superior to the comparison models.