Adaptive expert fusion model for online wind power prediction.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Renfang Wang, Jingtong Wu, Xu Cheng, Xiufeng Liu, Hong Qiu
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引用次数: 0

Abstract

Wind power prediction is a challenging task due to the high variability and uncertainty of wind generation and weather conditions. Accurate and timely wind power prediction is essential for optimal power system operation and planning. In this paper, we propose a novel Adaptive Expert Fusion Model (EFM+) for online wind power prediction. EFM+ is an innovative ensemble model that integrates the strengths of XGBoost and self-attention LSTM models using dynamic weights. EFM+ can adapt to real-time changes in wind conditions and data distribution by updating the weights based on the performance and error of the models on recent similar samples. EFM+ enables Bayesian inference and real-time uncertainty updates with new data. We conduct extensive experiments on a real-world wind farm dataset to evaluate EFM+. The results show that EFM+ outperforms existing models in prediction accuracy and error, and demonstrates high robustness and stability across various scenarios. We also conduct sensitivity and ablation analyses to assess the effects of different components and parameters on EFM+. EFM+ is a promising technique for online wind power prediction that can handle nonstationarity and uncertainty in wind power generation.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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