A technique for improving petroleum products forecasts using grey convolution models and genetic algorithms
Flavian Emmanuel Sapnken,
Ahmat Khazali Acyl,
Michel Boukar,
Serge Luc Biobiongono Nyobe,
Jean Gaston Tamba
Affiliations
Flavian Emmanuel Sapnken
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; Corresponding author at: Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon.
Ahmat Khazali Acyl
Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon
Michel Boukar
Transports and Applied Logistics Laboratory, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon
Serge Luc Biobiongono Nyobe
Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698, Douala, Cameroon
Jean Gaston Tamba
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
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.
