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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 |
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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 |