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Open Access

A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network

School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen 518172, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
Shenzhen Institute for Advanced Study, Shenzhen 518028, China, and also with Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Beijing 100864, China
High-performance Intelligent Computing Research Group, Guangdong Institute of Intelligent Science and Technology, Zhuhai 519031, China
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Abstract

The rapid evolution of distributed energy resources, particularly photovoltaic systems, poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs. The integrated nature of distributed markets, blending centralized and decentralized elements, holds the promise of maximizing social welfare and significantly reducing overall costs, including computational and communication expenses. However, achieving this balance requires careful consideration of various hyperparameter sets, encompassing factors such as the number of communities, community detection methods, and trading mechanisms employed among nodes. To address this challenge, we introduce a groundbreaking neural network-based framework, the Energy Trading-based Artificial Neural Network (ET-ANN), which excels in performance compared to existing algorithms. Our experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.

Keywords

photovoltaicenergy tradinghyperparameterprediction

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Tsinghua Science and Technology
Volume 30 Issue 2,
April 2025
Pages 864-874
DOI: 10.26599/TST.2024.9010150
Cite this article:
Chen C, Zhang Y, Lim BH, et al. A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network. Tsinghua Science and Technology, 2025, 30(2): 864-874. https://doi.org/10.26599/TST.2024.9010150

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Received: 25 December 2023
Revised: 03 June 2024
Accepted: 15 August 2024
Published: 09 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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