研究者情報 | 神奈川大学

リ　カセイ Ri Kasei 李　嘉誠所属神奈川大学情報学部システム数理学科職種助教
言語種別	英語
発行・発表の年月	2024/05
形態種別	学術雑誌
査読	査読あり
招待論文	招待あり
標題	A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction
執筆形態	共著
掲載誌名	Processes
掲載区分	国外
出版社・発行元	MDPI
巻・号・頁	12(6),pp.1063-1077
担当区分	責任著者
国際共著	国際共著
著者・共著者	Jia Guo, Jiacheng Li, Yuji Sato, Zhou Yan
概要	Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province.
researchmap用URL	https://www.mdpi.com/2227-9717/12/6/1063