A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering

Jinming Gao, Xianlong Su, Changsu Kim, Kerang Cao and Hoekyung Jung ()
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Jinming Gao: Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea
Xianlong Su: Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea
Changsu Kim: Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea
Kerang Cao: Department of Computer Science and Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
Hoekyung Jung: Department of Computer Science and Engineering, Pai Chai University, 155-40 Baejae-ro, Daejeon 35345, Republic of Korea

Energies, 2024, vol. 17, issue 16, 1-17

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.

Keywords: photovoltaic prediction; artificial intelligence; similar day analysis; bi-directional long global attention; cross attention (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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