A data-driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns

Noura Alkaabi, Chung Suk Cho, Ahmad Mayyas, Elie Azar

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Green building design is a promising approach to reduce the energy intensity of the building sector. However, green buildings often show important discrepancies between their predicted and actual energy use levels, in part due to varying operation patterns that are difficult to predict during design. This paper presents a data-driven modeling and analysis approach to test the resilience of green-certified buildings to uncertainty in the operation of building systems. Using building energy modeling coupled with an extensive empirical Monte Carlo analysis scheme, the framework quantifies and compares the response of a building to uncertainty in key technical and operational features before and after the adoption of green building certification specifications. The framework is illustrated and validated through a case study of an archetype commercial building located in the extreme hot climate of Abu Dhabi, UAE. Results show that adopting the green building features of the local “Estidama” building code reduces energy demand by an average of 17%. More importantly, the variability in demand is reduced (P <.05), confirming the increase in building resilience to uncertainty in design and operation factors. Finally, the techno-economic potential for solar photovoltaic (PV) adoption is also assessed, showing an estimated 16% reduction in capital costs.

Original languageBritish English
Pages (from-to)4250-4269
Number of pages20
JournalEnergy Science and Engineering
Volume8
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • building energy modeling
  • energy efficiency
  • green building rating system
  • resilience
  • solar photovoltaic (PV)
  • uncertainty

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