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Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces

Author

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  • Zhang, Hu
  • Tian, Wei
  • Tan, Jingyuan
  • Yin, Juchao
  • Fu, Xing

Abstract

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind).

Suggested Citation

  • Zhang, Hu & Tian, Wei & Tan, Jingyuan & Yin, Juchao & Fu, Xing, 2024. "Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces," Applied Energy, Elsevier, vol. 363(C).
    • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s0306261924004252
    DOI: 10.1016/j.apenergy.2024.123042
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    References listed on IDEAS

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    1. Devin Francom & Bruno Sansó & Vera Bulaevskaya & Donald Lucas & Matthew Simpson, 2019. "Inferring Atmospheric Release Characteristics in a Large Computer Experiment Using Bayesian Adaptive Splines," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1450-1465, October.
    2. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    3. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Shang, Yue & Nogal, Maria & Teixeira, Rui & Wolfert, A.R. (Rogier) M., 2024. "Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion. Application to train–track–bridge systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Kevin F. Forbes, 2023. "CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    6. Liu, Peng & Justo Alonso, Maria & Mathisen, Hans Martin, 2023. "Global sensitivity analysis and optimal design of heat recovery ventilation for zero emission buildings," Applied Energy, Elsevier, vol. 329(C).
    7. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
    8. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    9. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    10. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    11. Neale, John & Shamsi, Mohammad Haris & Mangina, Eleni & Finn, Donal & O’Donnell, James, 2022. "Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques," Applied Energy, Elsevier, vol. 315(C).
    12. Gauch, H.L. & Dunant, C.F. & Hawkins, W. & Cabrera Serrenho, A., 2023. "What really matters in multi-storey building design? A simultaneous sensitivity study of embodied carbon, construction cost, and operational energy," Applied Energy, Elsevier, vol. 333(C).
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