Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Sensitivity Analysis Using Explainable AI for Building Energy Use

설명가능한 인공지능을 이용한 건물 에너지 사용량 민감도 분석

  • Chu, Han-Gyeong (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Shin, Han-Sol (Institute of Engineering Research, Seoul National University) ;
  • Cho, Seong-Kwon (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Yoo, Young-Seo (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Park, Cheol-Soo (Dept. of Architecture and Architectural Engineering.Institute of Engineering Research.Institute of Construction and Environmental Engineering, Seoul National University)
  • 추한경 (서울대 건축학과) ;
  • 신한솔 (서울대 공학연구원) ;
  • 조성권 (서울대 건축학과) ;
  • 유영서 (서울대 건축학과) ;
  • 박철수 (서울대 건축학과.공학연구원.건설환경종합연구소)
  • Received : 2022.08.27
  • Accepted : 2022.10.28
  • Published : 2022.11.30
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Abstract

Classical sensitivity methods such as Morris and Sobol methods have been widely used in the decision making of building design and retrofit. However, these methods require a large number of samples to obtain reliable results as well as detailed information on input variables. On the other hand, the explainable AI technique can convert the relationship between input and output variables to a degree that can be understood by humans as well as provide more meaningful sensitivity analysis results for rational decision-making. In this paper, three XAI-based analyses were selected including Feature Importance, LIME, and SHAP. The five methods of Morris, Sobol, Feature Importance, LIME, and SHAP were applied to a medium office building provided by US DOE. As a result, it was found that XAI-based sensitivity analyses could provide better results than the classical methods.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 RS-2022-00141900).

References

  1. Arjunan, P., Poolla, K., & Miller, C. (2020). EnergyStar++: Towards more accurate and explanatory building energy benchmarking. Applied Energy, 276, 115413 https://doi.org/10.1016/j.apenergy.2020.115413
  2. Arjunan, P., Poolla, K., & Miller, C. (2022). BEEM: Data-driven building energy benchmarking for Singapore. Energy and Buildings, 260, 111869 https://doi.org/10.1016/j.enbuild.2022.111869
  3. ASHRAE, (2017). ASHRAE Handbook: Fundamentals2017, ASHRAE.
  4. KEA. (2016). Building Energy Efficiency Certification, Korea Energy Agency.
  5. Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. Advances in neural information processing systems, 26.
  6. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  7. Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022). Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy and AI, 100169.
  8. Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161-174. https://doi.org/10.1080/00401706.1991.10484804
  9. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016,). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
  10. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., ... & Tarantola, S. (2008). Global sensitivity analysis: the primer. John Wiley & Sons.
  11. Sobol, I. M. (1993). Sensitivity analysis for non-linear mathematical models. Mathematical modelling and computational experiment, 1, 407-414.
  12. Srinivasan, S., Arjunan, P., Jin, B., Sangiovanni-Vincentelli, A. L., Sultan, Z., & Poolla, K. (2021). Explainable AI for chiller fault-detection systems: gaining human trust. Computer, 54(10), 60-68
  13. Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1), 1-21. https://doi.org/10.1186/1471-2105-8-1
  14. Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and sustainable energy reviews, 20, 411-419 https://doi.org/10.1016/j.rser.2012.12.014
  15. Yoo, Y.S, Yi, D.H., & Park, C.S. (2021). Uncertainty in sensitivity analysis of architectural design variables for heating and cooling loads depending on usage scenarios. Journal of the Architectural Institute of Korea, 37(11), 247-253. https://doi.org/10.5659/JAIK.2021.37.11.247