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Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization

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  • Siyuan Fan
  • Shengxian Cao
  • Yanhui Zhang

Abstract

The output stability of the photovoltaic (PV) system is directly affected by temperature change of PV panels. In this paper, a novel temperature prediction method of PV panels with support vector machine (SVM) is proposed, which can solve the temperature prediction problem in a complex environment. In order to optimize parameters of SVM, a Pigeon-Inspired Optimization (PIO) method is given. Meanwhile, the delay factor (DF) is added to improve the PIO algorithm for avoiding the problem of local optimum. Moreover, a multisensor monitoring system of PV is established, and the collected data of temperature are used to train and verify the accuracy of the model. Finally, the proposed method is evaluated using synthetic and actual data sets. Simulation results show that the DFPIO-SVM can obtain better predictive performance.

Suggested Citation

  • Siyuan Fan & Shengxian Cao & Yanhui Zhang, 2020. "Temperature Prediction of Photovoltaic Panels Based on Support Vector Machine with Pigeon-Inspired Optimization," Complexity, Hindawi, vol. 2020, pages 1-12, December.
    • Handle: RePEc:hin:complx:9278162
    DOI: 10.1155/2020/9278162
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    Cited by:

    1. Honglu Zhu & Tingting Jiang & Yahui Sun & Shuang Sun, 2022. "A New Regional Distributed Photovoltaic Power Calculation Method Based on FCM-mRMR and nELM Model," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    2. Serrano-Luján, L. & Toledo, C. & Colmenar, J.M. & Abad, J. & Urbina, A., 2022. "Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms," Applied Energy, Elsevier, vol. 315(C).

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