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Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion

文献类型：期刊论文

作者Chen, Siyuan1; Wan, Hang1; Peng, Botao2; Quan, Rui1; Chang, Yufang1; Derigent, William3
刊名ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
出版日期2026
卷号163页码:21
关键词Renewable energy management Multi-step power forecasting Multi-scale convolutional Kolmogorov-Arnold network Artificial Lemming Algorithm Efficient additive attention
ISSN号0952-1976
DOI10.1016/j.engappai.2025.112832
英文摘要With the deepening of power market reform, the increasing share of wind and solar energy introduces significant challenges for power system stability due to the high volatility and uncertainty of weather-dependent generation. Accurate multi-step ultra-short-term forecasting is therefore essential for ensuring power balance and effective dispatch coordination in smart grids. To address this issue, we propose a novel hybrid deep learning framework that integrates a multi-scale convolutional Kolmogorov-Arnold network (MCKAN) to improve forecasting performance. This network is specifically designed to capture high-dimensional spatial and temporal features across multiple levels of abstraction. To improve feature selection and scale-specific weight allocation, we integrate an Efficient Additive Attention (EAA) mechanism, which is applied for the first time in the context of renewable energy forecasting. In addition, a Chaotic Quasi-Reverse Artificial Lemming Algorithm (CQALA) is proposed to automatically optimize the complex multivariate hyperparameters, enabling optimal hyperparameter selection and improving the model's overall predictive performance. Extensive experiments on a two-year wind and photovoltaic power dataset from the State Grid of China demonstrate that the proposed method outperforms several state-of-the-art models. For multi-step forecasting, the mean absolute error is reduced by up to 27.6 percent for photovoltaic power and 33.4 percent for wind power, highlighting the practical value of the proposed approach in real-world renewable energy management.
资助项目National Natural Science Founda-tion of China[62473133] ; Natural Science Foundation of Hubei Province[2024AFB831] ; Natural Science Foundation of Wuhan[2025040601020155]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:001606031600016
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/41596]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wan, Hang
作者单位1.Hubei Univ Technol, Hubei Key Lab Highefficiency Utilizat Solar Energy, Wuhan 430068, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Univ Lorraine, CNRS UMR 7039, CRAN, F-54516 Vandoeuvre Les Nancy, France
推荐引用方式
GB/T 7714		 Chen, Siyuan,Wan, Hang,Peng, Botao,et al. Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2026,163:21.
APA Chen, Siyuan,Wan, Hang,Peng, Botao,Quan, Rui,Chang, Yufang,&Derigent, William.(2026).Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,163,21.
MLA Chen, Siyuan,et al."Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 163(2026):21.

入库方式： OAI收割

来源：计算技术研究所

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