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Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning

Author

Listed:
  • Woo-Gyun Shin

    (Photovoltaics Research Department, Korea Institute of Energy Research, 152 Gajeong-ro, Youseong-gu, Daejeon 34129, Korea)

  • Ju-Young Shin

    (Corporate R&D Center, SG Energy Co., Ltd., 75, Sinilseo-ro 85 beon-gil, Daedeok-gu, Daejeon 34325, Korea)

  • Hye-Mi Hwang

    (Photovoltaics Research Department, Korea Institute of Energy Research, 152 Gajeong-ro, Youseong-gu, Daejeon 34129, Korea)

  • Chi-Hong Park

    (Corporate R&D Center, SG Energy Co., Ltd., 75, Sinilseo-ro 85 beon-gil, Daedeok-gu, Daejeon 34325, Korea)

  • Suk-Whan Ko

    (Photovoltaics Research Department, Korea Institute of Energy Research, 152 Gajeong-ro, Youseong-gu, Daejeon 34129, Korea)

Abstract

The building-integrated photovoltaic (BIPV) system is provoking mention as a technology for generating the energy consumed in cities with renewable sources. As the number of BIPV systems increases, performance diagnosis through power-generation predictions becomes more essential. In the case of a colored BIPV module that has been installed on a wall, it is more difficult to predict the amount of power generation because the shading loss varies based on the entrance altitude of the irradiance. Recently, artificial intelligence technology that is able to predict power by learning the output data of the system has begun being used. In this paper, the power values of colored BIPV systems that have been installed on walls are predicted, and the system output values are compared. The current-voltage (I–V) curve data are measured to predict the power required changing the intensity of the irradiance, and the linear regression model is derived for the changes in the voltage and current at a maximum power operating point and during irradiance changes. To improve the power prediction accuracy by considering the shading loss of colored BIPVs, a new model is proposed via neural network machine learning (ML). In addition, the accuracy of the proposed prediction models is evaluated by comparing the metrics such as RMSE, MAE, and R 2 . As a result of testing the linear regression model and the proposed ML model, the R 2 values for the voltage and current values of the proposed ML model were 5% higher for voltage and 2% higher for current. From this result, the proposed ML model of the RMSE about real power improved by more than 50% (0.0754 kW) compared to the simulation model (0.1581 KW). The proposed model demonstrates high-accuracy power estimations and is expected to help diagnose the performance of BIPV systems with colored modules.

Suggested Citation

  • Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2589-:d:785595
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    References listed on IDEAS

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    2. Andrzej Ożadowicz & Gabriela Walczyk, 2023. "Energy Performance and Control Strategy for Dynamic Façade with Perovskite PV Panels—Technical Analysis and Case Study," Energies, MDPI, vol. 16(9), pages 1-23, April.

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