Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA

     

Impact of occupant behavior on performance optimized building retrofits

Julian Donges 1, Alessandro Prada 2, Francesca Cappelletti 3, Andrea Gasparella 1
1 Faculty of Science and Technology, Free University of Bozen-Bolzano, Italy
2 Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy
3 Department of Design and Planning in Complex Environments, University IUAV of Venice, Italy


DOI: https://doi.org/10.26868/25222708.2021.30965
Abstract: In the framework of the recent Directive 844/2018, practitioner often rely on Building Energy Simulation (BES) combined with Multi-Objective Optimization (MOO) to find optimal energy saving measures for building retrofits. However, occupant behaviour is usually oversimplified as a static schedule provided by technical standards mainly developed for energy certification. This can lead to a significant gap between the performance of the optimal designed solution and its actual performance. In this study, we investigate how detailed user-behaviour profiles - e.g. static, probabilistic, and adaptive models - for the operation of windows impact on the optimal retrofit strategy. While the standard and adaptive model use a base ventilation rate like a constraint for indoor air quality (IAQ), the probabilistic models rely solely on the occupant actions on windows. The results demonstrate that the behavioural models result in major differences in indoor comfort conditions. Optimal solutions defined through probabilistic models are likely to be not very robust to the ventilation rate showing the potential for performance gaps. The importance of realistic user behaviour representation is highlighted to raise awareness about its influence on the full potential of retrofitting a building, maybe excluding those solutions that could majorly improve comfort.
Keywords: performance gap, multi-objective optimization, adaptive occupant behavior, probabilistic occupant behavior, user resilience
Pages: 3686 - 3693
Paper:
bs2021_30965