대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제37권12호
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  • Pages.255-264
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  • 2021
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  • 2733-6239(pISSN)
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  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
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확률적 자연채광 모델과 대공간 건물에서의 모델 예측 제어 적용

Stochastic Daylighting Model for Predictive Control in Large Open-space Building

  • 조형곤 (서울대학교 건축학과) ;
  • 김영섭 (서울대학교 건축학과) ;
  • 박철수 (서울대학교 건축학과.건설환경종합연구소)
  • Jo, Hyeong-Gon ;
  • Kim, Young-Sub ;
  • Park, Cheol-Soo (Department of Architecture and Architectural Engineering.Institute of Engineering Research, Institute of Construction and Environmental Engineering, Seoul National University)
  • 투고 : 2021.09.28
  • 심사 : 2021.12.10
  • 발행 : 2021.12.30
⟨ 이전 논문 다음 논문 ⟩

초록

Development of stochastic daylighting prediction model for a large open-space building was presented in this paper. The daylit prediction model uses solar altitude and azimuth, an illuminance value at a reference point and a cloud cover and predict daylit illuminances at sixteen workplane. The model can be regarded as 'virtual sensor' without installing actual photosensor. For capturing stochastic characteristics of daylit luminous indoor environment, Gaussian process was used. The daylit prediction model was then integrated to electric lighting control of the building. The optimal lighting control variables that can minimize electric lighting power consumption while satisfying required illuminance level expressed as a safety margin were found. Based on the eight days' validation, it is found that the proposed approach could save energy by 12.3%. It is expected that this stochastic control approach could be applied to other lighting control or indoor environmental control system.

키워드

과제정보

이 논문은 2021년도 정부(산업통상자원부)의 재원으로 한국에너지기술평가원의 지원을 받아 수행된 연구임 (20202020800030, 제로에너지건축물 구현을 위한 스마트 외장재·설비 융복합 기술개발 및 성능평가 체계 구축, 실증)

참고문헌

  1. Ayoub, M. (2019). 100 Years of daylihgting: A chronological review of daylight prediction and calculation methods, Solar Energy, 194, 360-390. https://doi.org/10.1016/j.solener.2019.10.072
  2. Afshari, S. & Mishra, S. (2016). A plug-and-play realization of decentralized feedback control for smart lighting systems, IEEE Transactions on Control Systems Technology, 24(4), 1317-1327. https://doi.org/10.1109/TCST.2015.2487880
  3. Borile, S., Pandharipande, A., Caicedo, D., Schenato, L. & Cenedese, A. (2017). A data-driven daylight estimation approach to lighting control, IEEE Access, 5, 21461-21471. https://doi.org/10.1109/ACCESS.2017.2679807
  4. Caicedo, D., Pandharipande, A., & Willems, F. M. J. (2014). Daylight-adapted lighting control using light sensor calibration prior-information, Energy and Buildings, 73, 105-114. https://doi.org/10.1016/j.enbuild.2014.01.022
  5. Delvaeye, R., Ryckaert, W., Stroobant, L., Hanslaer, P., Klein, R., & Breesch, H. (2016). Analysis of senergy savings of three daylight control systems in a school building by means of monitoring, Energy and Buildings, 127, 969-979. https://doi.org/10.1016/j.enbuild.2016.06.033
  6. Di Laura, D. L., Houser, K. W., Mistrick, R. G., & Steffy, G. R. (2011). The lighting handbook: reference and application, New York, Illuminating Engineering Society of North America.
  7. Guo, X., Tiller, D. K., Henze, G. P. & Waters, C.E. (2010). The performance of occupancy-based lighting control systems: A review, Lighting Research & Technology, 42(4), 415-431. https://doi.org/10.1177/1477153510376225
  8. Haq, M., Hassan, M., Abdullah, H., Rahman, H., Abdullah, M., Hussin, F., & Said, D. (2014). A review on lighting control technologies in commercial buildings. Renewable and Sustainable Energy Reviews, 33, 268-279. https://doi.org/10.1016/j.rser.2014.01.090
  9. Hermkes, M., Kuehn, N. M. & Riggelsen, C. (2014). Simultaneous quantification of epistemic and aleatory uncertainty in GPMEs using Gaussian process regression. Bulletin of Earthquake Engineering, 12, 449-466. https://doi.org/10.1007/s10518-013-9507-7
  10. Hu, J., Place, W., & Konradi, C. (2012). Incorporating Sky Luminance Data Measured by EKO Scanner with a Scanning Sky Simulator for Predicting Daylight Quantity in Buildings. Am. Sol. Energy Soc.-Solar, 2012.
  11. Jain, S. & Garg, V. (2018). A review of open loop control strategies for shades, blinds, and integrated lighting by use of real-time daylight prediction methods, Building and Environment, 135, 352-364. https://doi.org/10.1016/j.buildenv.2018.03.018
  12. Ji, Y., Ok, G., & Kwon, D. (2019). Environmental monitoring system for intelligent buildings using IoT, Korea computer congress 2019, 345-346.
  13. Kazanasmaz, T., Gunaydin, M. & Binol, S. (2009). Artificial neural network to predict daylight illuminance in office buildings, Building and Environment, 44(8), 1751-1757. https://doi.org/10.1016/j.buildenv.2008.11.012
  14. Kim, J. M., & Park, C. S. (2020). Epistemic and aleatoric uncertainty of bayesian neural network model for a chiller, Journal of Architectural Institute of Korea, 36(6), 177-184. https://doi.org/10.5659/JAIK.2020.36.6.177
  15. Kim, Y. S., Kim, J. M., Shin, H. S., & Park, C. S. (2020). Simulation-assisted optimal lighting control for a factory building, Journal of Architectural Institute of Korea, 36(8), 127-135. https://doi.org/10.5659/JAIK.2020.36.8.127
  16. Maasoumy, M., Razmara, M., Shahbakhti, M. & Vincentelli, A. S. (2014). Handling model uncertainty in model predictive control for energy efficient buildings, Energy and Buildings, 77, 377-392. https://doi.org/10.1016/j.enbuild.2014.03.057
  17. Mahdavi, A. (2008). Predictive simulation-based lighting and shading systems contol in buildings, Building Simulation, 1(1), 25-35. https://doi.org/10.1007/s12273-008-8101-4
  18. Mardaljevic, J. (2015). Climate-based daylight modeling and its discontents, CIBSE Technical Symposium, London.
  19. Meugheuvel, N., Pandharipande, A., Caicedo, D., & Hof, P. P. J. (2014). Distributed lighting control with daylight and occupancy adaptation, Energy and Buildings, 75, 321-329. https://doi.org/10.1016/j.enbuild.2014.02.016
  20. Nagpal, H., Stanio, A. & Basu, B. (2020). Robust model predictive control of HVAC systems with uncertainty in building parameters using linear matrix inequalities, Advances in Building Energy Research, 14(3), 338-354. https://doi.org/10.1080/17512549.2019.1588165
  21. Pandharipande, A., & Caicedo, D. (2015). Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches. Energy and Buildings, 104, 369-377. https://doi.org/10.1016/j.enbuild.2015.07.035
  22. Public Data Portal. (2021). Meteorological Administration_Short-term forecast inquiry, Retrieved September 8, 2021 from https://www.data.go.kr/en/data/15084084/openapi.do
  23. Rasmussen, C.E. & Williams, C. K. I. (2006). Gaussian Process for Machine Learning, Massachusetts, MIT Press.
  24. Reinhart, C. F. (2011). Daylight performance predictions. In Hensen, J. L., Lamberts, R. (Eds.), Building performance simulation for design and operation. Spon Press, New York, United Sates, 235-276.
  25. Rossi, M., Pandharipande, A., Caicedo, D., Schenato, L. & Cenedese, A. (2015). Personal lighting control with occupancy and daylight adaptation. Energy and Buildings, 105, 263-272. https://doi.org/10.1016/j.enbuild.2015.07.059
  26. Sun, Y., Su, H., Wu, C. J., & Augenbroe, G. (2015). Quantification of model form uncertainty in the calculation of solar diffuse irradiation on inclined surfaces for building energy simulation. Journal of Building Performance Simulation, 8(4), 253-265. https://doi.org/10.1080/19401493.2014.914247
  27. Tian, W., Heo, Y., Wilde, P., Li, Z., Yan, D., Park, C. S., Feng, X. & Augenbroe, G. (2018). A review of uncertainty analysis in building energy assessment, Renewable and Sustainable Energy Reviews, 93, 285-301. https://doi.org/10.1016/j.rser.2018.05.029
  28. Tregenza, P. R. & Waters, I. M. (1983). Daylight coefficients, Lighting Research & Technology, 15(2), 65-71. https://doi.org/10.1177/096032718301500201
  29. Tregenza, P. R. (2017). Uncertainty in daylight calculations, Lighting Research & Technology, 49(7), 829-844. https://doi.org/10.1177/1477153516653786
  30. Wagiman, K. R., Abdullah, M. N., Hassan, M. Y., Radzi, N. H. M., Baker, A. H. A., & Kwang, T. C. (2020). Lighting system control techniques in commercial buildings: Current trends and future directions, Journal of Building Engineering, 31.
  31. Yun, G., Yoon, K. C., & Kim, K. S. (2014). The influence of shading control strategies on the visual comfort and energy demand of office buildings. Energy and Buildings, 84, 70-85. https://doi.org/10.1016/j.enbuild.2014.07.040