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Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group

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  • Dengchang Ma

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China
    The College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310020, China)

  • Rongyi Xie

    (Guangxi Xijiang Group Investment Corp., Nanning 530000, China)

  • Guobing Pan

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Zongxu Zuo

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Lidong Chu

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Jing Ouyang

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

Abstract

With the increasing proportion of distributed photovoltaic (DPV) installations in county-level power grids, to improve the centralized operation and maintenance of the stations and to meet the needs of power grid dispatching, the output of the county-level regional DPV stations group needs to be predicted. In this paper, the weather prediction information is used to predict the output based on the model input average strategy. To eliminate the effect of the selected non-optimal training sample collection period on the prediction accuracy, an ensemble prediction method based on the minimum redundancy maximum relevance criterion and TabNet model is carried out. To reduce the influence of weather prediction errors on the power output prediction, a modified model based on error prediction is proposed. The ensemble prediction model is used to predict the day-ahead output, and a combination prediction model based on the proposed ensemble prediction model and the proposed modified model is established to predict the hour-ahead output. The experimental results verify the effectiveness of the proposed models. Compared with the corresponding reference models, the proposed ensemble prediction method reduces the normalized mean absolute errors (nMAEs) and the normalized root mean square errors (nRMSEs) of the day-ahead output prediction results by 2.86% and 5.51%, respectively. The combination prediction model reduces the nMAE and nRMSE of the hour-ahead output prediction results by 3.05% and 3.05%, respectively. Therefore, the prediction accuracy can be improved by the proposed models.

Suggested Citation

  • Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5649-:d:1203881
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    References listed on IDEAS

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