A data‐driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns. Issue 12 (25th September 2020) - Alkaabi, Noura; Cho, Chung‐Suk; Mayyas, Ahmad; Azar, Elie
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. Abstract : The aim is to test the resilience of green buildings to varying operation patterns. This is done using building energy modeling coupled with an extensive Monte Carlo analysis scheme. Results show that the local green building rating system in the UAE reduces energy use and PV systems' capital cost while increasing building resilience to uncertainty in operation patterns.