A PSO algorithm-based seasonal nonlinear grey Bernoulli model with fractional order accumulation for forecasting quarterly hydropower generation

• Published in: Journal of intelligent & fuzzy systems Vol. 40; no. 1; pp. 507 - 519
• Main Authors: Jiang, Jianming, Wu, Wen-Ze, Li, Qi, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: London Sage Publications Ltd 01.01.2021
• Subjects: Algorithms; Economic forecasting; Electricity; Hydroelectric power; Hydroelectric power generation; Mathematical models; Optimization; Seasonal variations; Sustainable development; China
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-200113

The hydropower plays a key role in electricity system owing to its renewability and largest share of clean electricity generation that promotes sustainable development of national economy. Developing a proper forecasting model for the quarterly hydropower generation is crucial for associated energy sectors, which could assist policymakers in adjusting corresponding schemes for facing with sustained demands. For this purpose, this paper presents a fractional nonlinear grey Bernoulli model (abbreviated as FANGBM(1,1)) coupled seasonal factor and Particular Swarm Optimization (PSO) algorithm, namely PSO algorithm-based FASNGBM(1,1) model. In the proposed method, the moving average method that eliminates the seasonal fluctuations is introduced into FANGBM(1,1), then in which the structure parameters of FASNGBM(1,1) are determined by PSO. Based on hydropower generation of China from the first quarter of 2011 to the final quarter of 2018 (2011Q1-2018Q4), the numerical results show that the proposed model has a better performance than that of other benchmark models. Eventually, the quarterly hydropower generation of China from 2019 to 2020 are forecasted by the proposed model, according to results, the hydropower generation of China will reach 11287.14 × 108 Kwh in 2020.