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

doi:10.1016/j.engappai.2025.112832

	Accurate multi-step wind and solar power forecasting based on multi-scale convolutional Kolmogorov-Arnold network and improved Lemming-optimized attention fusion
	Chen, Siyuan 1; Wan, Hang 1; Peng, Botao 2; Quan, Rui 1; Chang, Yufang 1; Derigent, William 3
	2026
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
卷号	163 页码:21
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.
关键词	Renewable energy management Multi-step power forecasting Multi-scale convolutional Kolmogorov-Arnold network Artificial Lemming Algorithm Efficient additive attention
DOI	10.1016/j.engappai.2025.112832
收录类别	SCI
语种	英语
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
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:001606031600016
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41597
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.