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Study on Solar Radiation Models in South Korea for Improving Office Building Energy Performance Analysis

Author

Listed:
  • Kee Han Kim

    (Building and Urban Research Institute, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 10223, Korea)

  • John Kie-Whan Oh

    (Department of Architectural Design, Dongseo University, 47 Jurye-Ro, Sasang-Gu, Busan 47011, Korea)

  • WoonSeong Jeong

    (Department of Architectural Engineering, Ewha Womans University, 52, Ewhayeodae-Gil, Seodaemun-Gu, Seoul 03760, Korea)

Abstract

Hourly global solar radiation in a weather file is one of the significant parameters for improving building energy performance analyses using simulation programs. However, most weather stations worldwide are not equipped with solar radiation sensors because they tend to be difficult to manage. In South Korea, only twenty-two out of ninety-two weather stations are equipped with sensors, and there are large areas not equipped with any sensors. Thus, solar radiation must often be calculated by reliable solar models. Hence, it is important to find a reliable model that can be applied in the wide variety of weather conditions seen in South Korea. In this study, solar radiation in the southeastern part of South Korea was calculated using three solar models: cloud-cover radiation model (CRM), Zhang and Huang model (ZHM), and meteorological radiation model (MRM). These values were then compared to measured solar radiation data. After that, the calculated solar radiation data from the three solar models were used in a building energy simulation for an office building with various window characteristics conditions, in order to identify how solar radiation differences affect building energy performance. It was found that a seasonal solar model for the area should be developed to improve building energy performance analysis.

Suggested Citation

  • Kee Han Kim & John Kie-Whan Oh & WoonSeong Jeong, 2016. "Study on Solar Radiation Models in South Korea for Improving Office Building Energy Performance Analysis," Sustainability, MDPI, vol. 8(6), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:6:p:589-:d:72488
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    Citations

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    Cited by:

    1. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    2. Amina Irakoze & Young-A Lee & Kee Han Kim, 2020. "An Evaluation of the Ceiling Depth’s Impact on Skylight Energy Performance Predictions Through a Building Simulation," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    3. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    4. Mirian Jiménez-Torres & Catalina Rus-Casas & Lenin Guillermo Lemus-Zúiga & Leocadio Hontoria, 2017. "The Importance of Accurate Solar Data for Designing Solar Photovoltaic Systems—Case Studies in Spain," Sustainability, MDPI, vol. 9(2), pages 1-14, February.
    5. George M. Stavrakakis & Dimitris Al. Katsaprakakis & Markos Damasiotis, 2021. "Basic Principles, Most Common Computational Tools, and Capabilities for Building Energy and Urban Microclimate Simulations," Energies, MDPI, vol. 14(20), pages 1-41, October.

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