IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i23p6369-d455022.html
   My bibliography  Save this article

Investigation of the Geometric Shape Effect on the Solar Energy Potential of Gymnasium Buildings

Author

Listed:
  • Lei Jiang

    (School of Architecture, Nanjing Tech University, Nanjing 211816, China
    College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Weiqing Liu

    (College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Haiping Liao

    (Department of Research Center, Jiangsu Institute of Urban Planning and Design, Nanjing 210036, China)

  • Jiabao Li

    (College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China)

Abstract

Gymnasium are typically large-span buildings with abundant solar energy resources due to their extensive roof surface. However, relevant research on this topic has not been thoroughly conducted to investigate the effect of the geometric shape of gymnasium buildings on their solar potential. In this paper, an investigation of the geometric shape effect on the solar potential of gymnasium buildings is presented. A three-dimensional radiation transfer model coupled with historical meteorological data was established to estimate the real-time solar potential of the roof of a gymnasium building. The rooftop solar potential of three typical building foundation shapes and different types of roof shapes that have evolved was systematically analyzed. An annual solar potential cloud map of each gymnasium building is generated. The monthly and annual average solar radiation intensities of the different types of roof shapes are investigated. Compared to the optimal tilt angle, the maximum decrease in the average radiation intensity reached −20.42%, while the minimum decline was −8.64% for all types of building shapes. The solar energy potential fluctuated by up to 11% across the various roof shapes, which indicate that shape selection is of vital importance for integrated photovoltaic gymnasium buildings. The results presented in this work are essential for clarifying the effects of the geometric shape of gymnasium buildings on the solar potential of their roofs, which provide an important reference for building design.

Suggested Citation

  • Lei Jiang & Weiqing Liu & Haiping Liao & Jiabao Li, 2020. "Investigation of the Geometric Shape Effect on the Solar Energy Potential of Gymnasium Buildings," Energies, MDPI, vol. 13(23), pages 1-21, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6369-:d:455022
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/23/6369/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/23/6369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang, Xiande & Li, Dingkun, 2013. "Solar photovoltaic and thermal technology and applications in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 330-340.
    2. Ferreira, Agmar & Kunh, Sheila S. & Fagnani, Kátia C. & De Souza, Tiago A. & Tonezer, Camila & Dos Santos, Geocris Rodrigues & Coimbra-Araújo, Carlos H., 2018. "Economic overview of the use and production of photovoltaic solar energy in brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 181-191.
    3. GhaffarianHoseini, AmirHosein & Dahlan, Nur Dalilah & Berardi, Umberto & GhaffarianHoseini, Ali & Makaremi, Nastaran & GhaffarianHoseini, Mahdiar, 2013. "Sustainable energy performances of green buildings: A review of current theories, implementations and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 1-17.
    4. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    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. Yu Dong & Haoqi Duan & Xueshun Li & Ruinan Zhang, 2024. "Influence of Different Forms on BIPV Gymnasium Carbon-Saving Potential Based on Energy Consumption and Solar Energy in Multi-Climate Zones," Sustainability, MDPI, vol. 16(4), pages 1-20, February.

    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. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    2. Amri, Amun & Jiang, Zhong Tao & Pryor, Trevor & Yin, Chun-Yang & Djordjevic, Sinisa, 2014. "Developments in the synthesis of flat plate solar selective absorber materials via sol–gel methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 316-328.
    3. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    4. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    5. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    6. Kazimierz Kawa & Rafał Mularczyk & Waldemar Bauer & Katarzyna Grobler-Dębska & Edyta Kucharska, 2024. "Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings," Energies, MDPI, vol. 17(13), pages 1-16, June.
    7. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    8. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    9. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    10. Andrzej Pacana & Karolina Czerwińska & Grzegorz Ostasz, 2023. "Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity," Energies, MDPI, vol. 16(8), pages 1-26, April.
    11. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    12. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    13. Rashidi, Hamidreza & GhaffarianHoseini, Ali & GhaffarianHoseini, Amirhosein & Nik Sulaiman, Nik Meriam & Tookey, John & Hashim, Nur Awanis, 2015. "Application of wastewater treatment in sustainable design of green built environments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 845-856.
    14. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    15. de Oliveira, Lucas Guedes & Aquila, Giancarlo & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & de Queiroz, Anderson Rodrigo & de Oliveira Pamplona, Edson & Camatta, Ulisses Pessin, 2020. "Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    16. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    18. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    19. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    20. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(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:23:p:6369-:d:455022. 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.