A technique for improving petroleum products forecasts using grey convolution models and genetic algorithms.

Autor:	Sapnken FE; Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon.; Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon., Acyl AK; Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon., Boukar M; Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon., Nyobe SLB; Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon., Tamba JG; Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon.; Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon.
Jazyk:	angličtina
Zdroj:	MethodsX [MethodsX] 2023 Feb 27; Vol. 10, pp. 102097. Date of Electronic Publication: 2023 Feb 27 (Print Publication: 2023).
DOI:	10.1016/j.mex.2023.102097
Abstrakt:	Forecasting energy consumption is a major concern for policymakers, oil industry companies, and many other associated businesses. Though there exist many forecasting tool, selecting the most appropriate one is critical. GM(1,1) has proven to be one of the most successful forecasting tool. GM(1,1) does not require any specific information and can be adapted to predict energy consumption using a minimum of four observations. Unfortunately, GM(1,1) on its own will generate too large forecast errors because it performs well only when data follow an exponential trend and should be implemented in a political-socio-economic free environment. To reduce these short-comings, this paper proposes a new GM(1,n) convolution model optimized by genetic algorithms integrating a sequential selection mechanism and arc consistency, abbreviated Sequential-GMC(1,n)-GA. The new model, like some recent hybrid versions, is robust and reliable, with MAPE of 1.44%, and RMSE of 0.833.•Modification, extension and optimization of grey multivariate model is done.•The model is very generic can be applied to a wide variety of energy sectors.•The new hybrid model is a valid forecasting tool that can be used to track the growth of households' energy demand. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Author(s).)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu