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Open AccessArticle
A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering
by
Jinming Gao
Jinming Gao 1,
Xianlong Su
Xianlong Su 1,
Changsu Kim
Changsu Kim
Prof. Changsu Kim received his B.S., M.S., and Ph.D. degrees from the Department of Computer of Pai [...]
Prof. Changsu Kim received his B.S., M.S., and Ph.D. degrees from the Department of Computer Engineering of Pai Chai University, Daejeon, Republic of Korea, in 1996, 1998, and 2002, respectively. From 2005 to 2012, he worked for the Department of Internet at Chungwoon University as a professor. Since 2013, he has worked for the Department of Computer Engineering at Pai Chai University as a professor. From 2013 to present, he has worked as an editor of the Journal of the Korean Institute of Information and Communication Engineering. His current research interests mainly include web information systems media, data mining information retrieval and knowledge engineering, and multimedia information processing.
1,
Kerang Cao
Kerang Cao 2 and
Hoekyung Jung
Hoekyung Jung 1,*
1
Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea
2
Department of Computer Science and Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
*
Author to whom correspondence should be addressed.
Energies 2024, 17(16), 3958; https://doi.org/10.3390/en17163958 (registering DOI)
Submission received: 25 June 2024
/
Revised: 31 July 2024
/
Accepted: 8 August 2024
/
Published: 9 August 2024
Abstract
Photovoltaic (PV) power generation is significantly impacted by environmental factors that exhibit substantial uncertainty and volatility, posing a critical challenge for accurate PV power prediction in power system management. To address this, a parallel model is proposed for PV short-term prediction utilizing a multi-level attention mechanism. Firstly, gray relation analysis (GRA) and an improved ISODATA algorithm are used to select a dataset of similar days with comparable meteorological characteristics to the forecast day. A transformer encoder layer with multi-head attention is then used to extract long-term dependency features. Concurrently, BiGRU, optimized with a Global Attention network, is used to capture global temporal features. Feature fusion is performed using Cross Attention, calculating attention weights to emphasize significant features and enhancing feature integration. Finally, high-precision predictions are achieved through a fully connected layer. Utilizing historical PV power generation data to predict power output under various weather conditions, the proposed model demonstrates superior performance across all three climate types compared to other models, achieving more reliable predictions.
Share and Cite
MDPI and ACS Style
Gao, J.; Su, X.; Kim, C.; Cao, K.; Jung, H.
A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering. Energies 2024, 17, 3958.
https://doi.org/10.3390/en17163958
AMA Style
Gao J, Su X, Kim C, Cao K, Jung H.
A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering. Energies. 2024; 17(16):3958.
https://doi.org/10.3390/en17163958
Chicago/Turabian Style
Gao, Jinming, Xianlong Su, Changsu Kim, Kerang Cao, and Hoekyung Jung.
2024. "A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering" Energies 17, no. 16: 3958.
https://doi.org/10.3390/en17163958
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