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

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成果类型:
期刊论文
作者:
Jiang, Jianming;Wu, Wen-Ze*;Li, Qi;Zhang, Yu
通讯作者:
Wu, Wen-Ze
作者机构:
[Jiang, Jianming] Baise Univ, Sch Math & Stat, Baise, Peoples R China.
[Wu, Wen-Ze; Li, Qi] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.
[Zhang, Yu] Nanjing Univ Aeronaut & Astronaut, Dept Math, Nanjing, Peoples R China.
通讯机构:
[Wu, Wen-Ze] C
Cent China Normal Univ, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Quarterly hydropower generation;seasonal fluctuation;FASNGBM(1,1);Particle Swarm Optimization (PSO)
期刊:
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
ISSN:
1064-1246
年:
2021
卷:
40
期:
1
页码:
507-519
基金类别:
Fundamental Research Funds for the Central Universities of ChinaFundamental Research Funds for the Central Universities [2019YBZZ062]
机构署名:
本校为通讯机构
院系归属:
经济与工商管理学院
摘要:
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, nam...

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