IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3171-d373459.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3171/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3171/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    2. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    3. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    4. Skandalos, Nikolaos & Wang, Meng & Kapsalis, Vasileios & D'Agostino, Delia & Parker, Danny & Bhuvad, Sushant Suresh & Udayraj, & Peng, Jinqing & Karamanis, Dimitris, 2022. "Building PV integration according to regional climate conditions: BIPV regional adaptability extending Köppen-Geiger climate classification against urban and climate-related temperature increases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    5. Yiqing Dai & Yan Yin & Yundi Lu, 2021. "Strategies to Facilitate Photovoltaic Applications in Road Structures for Energy Harvesting," Energies, MDPI, vol. 14(21), pages 1-14, October.
    6. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    7. Jing, Yifan & Zhu, Li & Yin, Baoquan & Li, Fangfang, 2023. "Evaluating the PV system expansion potential of existing integrated energy parks: A case study in North China," Applied Energy, Elsevier, vol. 330(PA).
    8. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    9. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Panayides, Ioannis & Joseph-Stylianou, Josephina & Zomeni, Zomenia, 2012. "Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration," Energy, Elsevier, vol. 48(1), pages 233-240.
    10. Keçebaş, Ali & Alkan, Mehmet Ali & Yabanova, İsmail & Yumurtacı, Mehmet, 2013. "Energetic and economic evaluations of geothermal district heating systems by using ANN," Energy Policy, Elsevier, vol. 56(C), pages 558-567.
    11. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Christodoulides, Paul & Joseph-Stylianou, Josephina, 2015. "Artificial neural networks for the generation of a conductivity map of the ground," Renewable Energy, Elsevier, vol. 77(C), pages 400-407.
    12. Bushra, Nayab, 2022. "A comprehensive analysis of parametric design approaches for solar integration with buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    13. Boukelia, T.E. & Ghellab, A. & Laouafi, A. & Bouraoui, A. & Kabar, Y., 2020. "Cooling performances time series of CSP plants: Calculation and analysis using regression and ANN models," Renewable Energy, Elsevier, vol. 157(C), pages 809-827.
    14. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    15. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    16. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    17. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    18. Ito, Risa & Lee, Sihwan, 2024. "Development of adjustable solar photovoltaic system for integration with solar shading louvers on building façades," Applied Energy, Elsevier, vol. 359(C).
    19. Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.
    20. Yu, Guoqing & Yang, Hongxing & Luo, Daina & Cheng, Xu & Ansah, Mark Kyeredey, 2021. "A review on developments and researches of building integrated photovoltaic (BIPV) windows and shading blinds," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3171-:d:373459. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.