Robustness Indicators for the Impact of Occupant Behavior Uncertainty on Building Energy Consumption

Jiahui Ying, Jian Yao () and Rongyue Zheng ()
Additional contact information
Jiahui Ying: Department of Civil Engineering, Ningbo University, Ningbo 315211, China
Jian Yao: Department of Architecture, Ningbo University, Ningbo 315211, China
Rongyue Zheng: Department of Civil Engineering, Ningbo University, Ningbo 315211, China

Energies, 2024, vol. 17, issue 18, 1-26

Abstract: Due to the significant impact of occupant behavior uncertainty on building performance, robustness indicators are crucial for assessing and predicting building energy consumption. This study evaluates the robustness of building performance under occupant behavior uncertainty using various robustness indicators such as Maximax, Maximin, Hurwicz, and Laplace. Benchmark values (0.635) and coefficients of variation (0.544) from statistics were introduced to quantify the relative performance of each indicator and the relative dispersion of the data, allowing for fair comparisons across different magnitudes of indicators. “Variance”, “Starr’s Domain”, “Kurtosis”, and “Maximin” were identified as key indicators for assessing the robustness of energy consumption and load data. Based on these indicators and statistical principles, assuming the data follows a normal distribution, energy consumption and loads were predicted, showing that the optimized outcomes demonstrate good robustness.

Keywords: occupant behavior; robustness indicators; NSGA-II; Pareto-optimal solution; energy consumption prediction; confidence interval (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/18/4571/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/18/4571/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:18:p:4571-:d:1476628

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().