Journal of the Architectural Institute of Korea Structure & Construction (대한건축학회논문집:구조계)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Five Machine Learning Models for HVAC Systems in an Existing Office Building

기존 건물 HVAC 시스템에 대한 다섯 가지 기계학습 모델 개발

  • Received : 2017.06.03
  • Accepted : 2017.08.17
  • Published : 2017.10.30
⟨ Previous Next ⟩

Abstract

The first principles-based simulation model, e.g. dynamic simulation, is influenced by model uncertainty, simplification of the reality, lack of information, a modeler's subjective assumptions, etc. Recently, a data-driven machine learning model has received a growing attention for simulation of existing buildings. The data-driven model is advantageous that it is simpler and requires less inputs than the first principles based model. In this study, the authors applied five different machine learning techniques (Artificial Neural Network, Support Vector Machine, Gaussian Process, Random Forest, and Genetic Programming) to HVAC systems (chiller, cooling tower, pump, ice thermal storage system and air handling unit) installed in an existing office building. It was found that the five machine learning models are good enough to predict the dynamic behavior of the HVAC systems. The machine learning model made by Genetic Programming is most accurate among the five machine learning models. The models made by Support Vector Machine and Gaussian Process Model require significant computation time and thus are limited in terms of the number of inputs. The accuracy of the model made by Random Forest is dependent on the set of inputs.

Keywords

Acknowledgement

Supported by : 한국에너지기술평가원 (KETEP)

References

  1. Ahn, K. U., Kim, D. W., Kim, Y. J., & Park, C. S. (2016). Development of an Gaussian Process Model using a Data Filtering Method, Journal of the Architectural Institute of Korea: Planning & Design, 32(4), 97-105.
  2. Ahn, K. U., Kim, Y. J., & Park, C. S. (2012). Issues on Dynamic Building Energy Performance Assessment in Design Process, Journal of the Architectural Institute of Korea: Planning & Design, 28(12), 361-369.
  3. Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1994). An evolutionary algorithm that constructs recurrent neural networks, IEEE transactions on Neural Networks, 5(1), 54-65. https://doi.org/10.1109/72.265960
  4. ASHRAE (2014). ASHRAE Guideline 14-2014: Measurement of Energy, Demand and Water Savings, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA
  5. Bottou, L., & Lin, C. J. (2007). Support vector machine solvers, Large scale kernel machines, 301-320.
  6. Breiman, L. (1996). Bagging predictors, Machine learning, 24(2), 123-140. https://doi.org/10.1023/A:1018054314350
  7. Breiman, L. (2001). Random Forest, Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  8. DOE (2016a). EnergyPlus 8.6 Engineering Reference, U.S. Department of Energy, Washington, DC.
  9. DOE (2016b). EnergyPlus 8.6 Input Output Reference, U.S. Department of Energy, Washington, DC.
  10. Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study, Energy and Buildings, 49, 591-603. https://doi.org/10.1016/j.enbuild.2012.03.010
  11. Foresee, F. D., & Hagan, M. T. (1997). Gauss-Newton approximation to Bayesian learning, Proceedings of the International Conference on Neural Networks, 1930-1935.
  12. Garg, A., & Tai, K. (2012). Review of genetic programming in modeling of machining processes, Proceedings of the International Conference on Modelling, Identification and Control(ICMIC), 653-658.
  13. Jovanovic, R. Z., Sretenovic, A. A., & Zivkovic, B. D. (2015). Ensemble of various neural networks for prediction of heating energy consumption, Energy and Buildings, 94, 189-199. https://doi.org/10.1016/j.enbuild.2015.02.052
  14. Kim, Y. M., Ahn, K. U., & Park, C. S. (2016). Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings, Sustainability, 8(6), 543. https://doi.org/10.3390/su8060543
  15. Louppe, G. (2014). Understanding random forests: From theory to practice, Doctoral dissertation, University of Liege, Liege, Belgium.
  16. Park, C. S. (2006). Normative Assessment of Technical Building Performance, Journal of the Architectural Institute of Korea: Planning & Design, 22(11), 337-344.
  17. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning, MIT Press
  18. Sane, H. S., Haugstetter, C., & Bortoff, S. A. (2006), Building HVAC control systems-role of controls and optimization, Proceedings of the American Control Conference, 6.
  19. Suh, W. J., & Park, C. S. (2016). Real-time Optimal Control of Chiller using Genetic Programming, Journal of the Architectural Institute of Korea: Planning & Design, 32(6), 105-112.
  20. Van Dijk, H., Spiekman, M., & de Wilde, P. (2005). A monthly method for calculating energy performance in the context of European building regulations, Building Simulation, 255-262.
  21. Wang, H., & Hu, D. (2005). Comparison of SVM and LS-SVM for regression, Proceedings of the International Conference on Neural Networks and Brain, 1, 279-283.
  22. Xavier-de-Souza, S., Suykens, J. A., Vandewalle, J., & Bolle, D. (2010). Coupled simulated annealing, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(2), 320-335. https://doi.org/10.1109/TSMCB.2009.2020435
  23. Yao, X. (1999). Evolving artificial neural networks, Proceedings of the IEEE, 87(9), 1423-1447. https://doi.org/10.1109/5.784219
  24. Zhang, Y., O'Neill, Z., Dong, B., & Augenbroe, G. (2015). Comparisons of inverse modeling approaches for predicting building energy performance, Building and Environment, 86, 177-190. https://doi.org/10.1016/j.buildenv.2014.12.023
  25. Zhao, H. X., & Magoules, F. (2012). A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews, 16(6), 3586-3592. https://doi.org/10.1016/j.rser.2012.02.049