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Performance Evaluation of Control Methods for PV-Integrated Shading Devices

Author

Listed:
  • Sung Kwon Jung

    (CS Enertech, Seoul 06730, Korea)

  • Youngchul Kim

    (Department of Civil and Environmental Engineering, KAIST Smart City Research Center, KAIST Urban Design Lab, KAIST, Daejeon 34141, Korea)

  • Jin Woo Moon

    (School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea)

Abstract

This study aimed to develop a building-integrated photovoltaic (BIPV) device and optimal control methods that increase the photovoltaic (PV) efficiency and visual comfort of the indoor space. A louver-type PV-integrated shading device was suggested and an artificial neural networks (ANN) model was developed to predict PV electricity output, work plane illuminance, and daylight glare index (DGI). The slat tilt angle of the shading device was controlled to maximize PV electricity output based on three different strategies: one without visual comfort constraints, and the other two with visual comfort constraints: work plane illuminance and DGI. Optimal tilt angle was calculated using predictions of the ANN. Experiments were conducted to verify the system modeling and to evaluate the performance of the shading device. Experiment results revealed that the ANN model successfully predicted the PV output, work plane illuminance, and DGI. The PV-integrated shading device was more efficient in producing electricity than the conventional wall-mount PV systems, the control method without visual comfort constraints was most efficient in generating electricity than the other two with such constraints, and excluding the constraints resulted in less comfortable visual environment and reduced energy benefit. From the results analysis, it can be concluded that based on the accurate predictions, the PV-integrated shading device controlled using the proposed methods produced more electricity compared to the wall-mount counterpart.

Suggested Citation

  • Sung Kwon Jung & Youngchul Kim & Jin Woo Moon, 2020. "Performance Evaluation of Control Methods for PV-Integrated Shading Devices," Energies, MDPI, vol. 13(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3171-:d:373459
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    References listed on IDEAS

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    1. Chae, Young Tae & Kim, Jeehwan & Park, Hongsik & Shin, Byungha, 2014. "Building energy performance evaluation of building integrated photovoltaic (BIPV) window with semi-transparent solar cells," Applied Energy, Elsevier, vol. 129(C), pages 217-227.
    2. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Debbarma, Mary & Sudhakar, K. & Baredar, Prashant, 2017. "Thermal modeling, exergy analysis, performance of BIPV and BIPVT: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1276-1288.
    5. Walker, Linus & Hofer, Johannes & Schlueter, Arno, 2019. "High-resolution, parametric BIPV and electrical systems modeling and design," Applied Energy, Elsevier, vol. 238(C), pages 164-179.
    6. Freitas, Jader de Sousa & Cronemberger, Joára & Soares, Raí Mariano & Amorim, Cláudia Naves David, 2020. "Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug," Renewable Energy, Elsevier, vol. 160(C), pages 1468-1479.
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    Cited by:

    1. Abdelhakim Mesloub & Aritra Ghosh & Mabrouk Touahmia & Ghazy Abdullah Albaqawy & Emad Noaime & Badr M. Alsolami, 2020. "Performance Analysis of Photovoltaic Integrated Shading Devices (PVSDs) and Semi-Transparent Photovoltaic (STPV) Devices Retrofitted to a Prototype Office Building in a Hot Desert Climate," Sustainability, MDPI, vol. 12(23), pages 1-17, December.

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