Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 665-673.doi: 10.23919/JSEE.2022.000061

• SYSTEMS ENGINEERING • Previous Articles     Next Articles

A self-adaptive grey forecasting model and its application

Xiaozhong TANG1,2(), Naiming XIE1,*()   

  1. 1 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    2 Department of Industry and Finance, Huangshan Vocational and Technical College, Huangshan 245000, China
  • Received:2021-02-15 Online:2022-06-18 Published:2022-06-24
  • Contact: Naiming XIE;
  • About author:|TANG Xiaozhong was born in 1985. He received his B.S. degree from Huangshan University in 2008, and M.S. degree from Shenyang University of Technology in 2012. Now, he is an associate professor at Huang shan Vocational and Technical College. He has been pursuing his Ph.D. degree in Nanjing University of Aeronautics and Astronautics since 2018. His research interests include grey system theory and prediction modeling algorithm. E-mail:||XIE Naiming was born in 1981. He received his B.S., M.S., and Ph.D. degrees in grey systems theory from Nanjing University of Aeronautics and Astronautics (NUAA). Now he is a professor in the College of Economics and Management of NUAA and an associate editor of Grey Systems: Theory and Application. His research interests include Grey Systems theory, production scheduling, and control. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (72171116;71671090), the Fundamental Research Funds for the Central Universities (NP2020022), the Key Research Projects of Humanities and Social Sciences in Anhui Education Department (SK2021A1018), and Qinglan Project for Excellent Youth or Middle-aged Academic Leaders in Jiangsu Province, China


GM(1,1) models have been widely used in various fields due to their high performance in time series prediction.However, some hypotheses of the existing GM(1,1) model family may reduce their prediction performance in some cases. To solve this problem, this paper proposes a self-adaptive GM(1,1) model, termed as SAGM(1,1) model, which aims to solve the defects of the existing GM (1,1) model family by deleting their modeling hypothesis. Moreover, a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed, the proposed multi-parameter optimization scheme adopts machine learning ideas, takes all adjustable parameters of SAGM(1,1) model as input variables, and trains it with firefly algorithm. And Sobol’ sensitivity indices are applied to study global sensitivity of SAGM(1,1) model parameters, which provides an important reference for model parameter calibration. Finally, forecasting capability of SAGM(1,1) model is illustrated by Anhui electricity consumption dataset. Results show that prediction accuracy of SAGM(1,1) model is significantly better than other models, and it is shown that the proposed approach enhances the prediction performance of GM(1,1) model significantly.

Key words: grey forecasting model, GM(1,1) model, firefly algorithm, Sobol’ sensitivity indices, electricity consumption prediction