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

Zhang, Guangfeng; Chen, Yi; Li, Yun; Yu, Hongnian; Hu, Hosheng; Wu, Shaomin

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.

Citation

Zhang, G., Chen, Y., Li, Y., Yu, H., Hu, H., & Wu, S. (2019). Intelligent swarm firefly algorithm for the prediction of China's national electricity consumption. International Journal of Bio-Inspired Computation, 13(2), 111-118. https://doi.org/10.1504/ijbic.2019.098407

• Journal Article Type: Article
• Acceptance Date: Mar 20, 2019
• Online Publication Date: Mar 21, 2019
• Publication Date: 2019
• Deposit Date: Jun 17, 2022
• Journal: International Journal of Bio-Inspired Computation
• Print ISSN: 1758-0366
• Electronic ISSN: 1758-0374
• Publisher: Inderscience
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 2
• Pages: 111-118
• DOI: https://doi.org/10.1504/ijbic.2019.098407
• Keywords: General Computer Science; Theoretical Computer Science
• Public URL: http://researchrepository.napier.ac.uk/Output/2879994