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Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature

Author

Listed:
  • Dong Eun Jung

    (Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Chanuk Lee

    (Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Kee Han Kim

    (Department of Architectural Engineering, Ulsan University, Ulsan 44610, Korea)

  • Sung Lok Do

    (Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea)

Abstract

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environment data, which means that the previous studies have limitations related to timestep. That is, there is a lack of predictive models that calculate the future surface temperatures by using the current measured data. Therefore, this study developed a predictive model using an ANN (artificial neural network) algorithm to determine the surface temperature of PV modules for a future period of time. Then, this study evaluated the actual performance (i.e., power generation) with the predicted surface temperatures.

Suggested Citation

  • Dong Eun Jung & Chanuk Lee & Kee Han Kim & Sung Lok Do, 2020. "Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature," Energies, MDPI, vol. 13(15), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:4005-:d:394003
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    References listed on IDEAS

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    1. Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
    2. Waithiru Charles Lawrence Kamuyu & Jong Rok Lim & Chang Sub Won & Hyung Keun Ahn, 2018. "Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs," Energies, MDPI, vol. 11(2), pages 1-13, February.
    3. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
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    1. 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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