Title: An energy market demand prediction based on grey BP-NN optimal combination

Authors: Lei Zhang

Addresses: College of Science, Bengbu University, Bengbu, Anhui, China

Abstract: Aiming at the problems of low-prediction accuracy and long prediction time in traditional energy market demand prediction methods, an energy market demand prediction method based on grey BP-NN optimisation combination is proposed. The influencing factors of energy market demand are analysed through economic growth factors, energy price factors, industrial structure factors, population and urbanisation factors and environmental policy factors, the analysis sequence is determined, the analysis matrix is constructed, the grey correlation degree of each influencing factor is calculated and the mean value is standardised. According to the processing results, the energy market demand prediction model of grey theory is constructed. Taking the prediction results of grey model and factors affecting energy demand as the input of BP neural network, an improved BP neural network structure is constructed and the prediction results are output. The simulation results show that the proposed method has high accuracy and short prediction time.

Keywords: BP neural network; grey theory; energy market; demand prediction; grey correlation degree; mean normalisation.

DOI: 10.1504/IJGEI.2023.130678

International Journal of Global Energy Issues, 2023 Vol.45 No.3, pp.233 - 246

Received: 28 Dec 2021
Accepted: 01 Jun 2022
Published online: 02 May 2023 *

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