Title: Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption

Authors: Guangfeng Zhang; Yi Chen; Yun Li; Hongnian Yu; Huosheng Hu; Shaomin Wu

Addresses: Sino-U.S. College, Beijing Institute of Technology (Zhuhai), Zhuhai, 519088, China ' School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China ' School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China ' School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China ' School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK ' Kent Business School, University of Kent, Canterbury, Kent CT2 7PE, UK

Abstract: China's energy consumption is the world's largest and is still rising, leading to concerns of energy shortage and environmental issues. It is, therefore, necessary to estimate the energy demand and to examine the dynamic nature of the electricity consumption. In this paper, we develop a nonlinear model of energy consumption and utilise a computational intelligence approach, specifically a swarm firefly algorithm with a variable population, to examine China's electricity consumption with historical statistical data from 1980 to 2012. Prediction based on these data using the model and the examination is verified with a bivariate sensitivity analysis, a bias analysis and a forecasting exercise, which all suggest that the national macroeconomic performance, the electricity price, the electricity consumption efficiency and the economic structure are four critical factors determining national electricity consumption. Actuate prediction of the consumption is important as it has explicit policy implications on the electricity sector development and planning for power plants.

Keywords: energy consumption; nonlinear modelling; swarm firefly algorithm; parameters determination.

DOI: 10.1504/IJBIC.2019.098407

International Journal of Bio-Inspired Computation, 2019 Vol.13 No.2, pp.111 - 118

Received: 10 Jan 2017
Accepted: 22 May 2017
Published online: 19 Mar 2019 *

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