Photovoltaic power prediction method based on CNN-BIGRU-ATTENTION model

Zongyi Luo; Jianjie He; Jianhong Chen; Ziyuan Qian; Lingxiao Wu; Xinran Chen

doi:10.1117/12.3024564

13 May 2024 Photovoltaic power prediction method based on CNN-BIGRU-ATTENTION model

Zongyi Luo, Jianjie He, Jianhong Chen, Ziyuan Qian, Lingxiao Wu, Xinran Chen

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315913 (2024) https://doi.org/10.1117/12.3024564
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

This study addresses the challenge of accurately predicting photovoltaic (PV) power output across diverse weather conditions. To tackle this issue, we propose a novel combined prediction model that integrates Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BIGRU), and Attention mechanisms. First, the Copula method is applied to identify the crucial meteorological features that impact photovoltaic output. Subsequently, K-means clustering is employed to group daily photovoltaic power generation scenarios. Following this, the CNN-BIGRU-ATTENTION model is established and applied for power prediction across various clustering scenarios. Finally, we conduct a case study utilizing photovoltaic power generation data from Australia. The results unequivocally demonstrate a significant enhancement in prediction accuracy achieved by the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zongyi Luo, Jianjie He, Jianhong Chen, Ziyuan Qian, Lingxiao Wu, and Xinran Chen "Photovoltaic power prediction method based on CNN-BIGRU-ATTENTION model", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315913 (13 May 2024); https://doi.org/10.1117/12.3024564

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