 Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and ChinaChong Liu, Wen-Ze Wu, Wanli Xie and Jun Zhang Chaos, Solitons & Fractals, 2020, vol. 141, issue C Abstract: As one of the most important energy sources, electricity plays an important role in power system and is the main driving force for the development of the country and society. Accurately forecasting electricity consumption is of significance of the power system and market. For this, a novel fractional grey polynomial model with time power term (denoted as FPGM(1,1,tα)) is developed for forecasting electricity consumption of India and China, in which the grey polynomial model is optimized by combining time power term and fractional accumulation, the quantum genetic algorithm (QGA) is then applied to determine the model parameters. Particularly, the proposed model can be changed to other existing models by adjusting systematic coefficient. The numerical results shows that the proposed model outperforms other competitive models. Given the efficacy of the proposed model, it is applied to predict electricity consumption in the coming years, which could provide with a reference in preparing energy policies and strategies. Keywords: Electricity consumption; FPGM(1,1,tα); Metabolic mechanism; Quantum genetic algorithm (QGA) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:141:y:2020:i:c:s0960077920308225 DOI: 10.1016/j.chaos.2020.110429 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.