Impact of occupants’ behavior on buildings’ energy consumption
The behavior of the occupants plays an essential role in building energy demand increases and activity growth. In addition, occupants’ behavior has significant impacts on building energy performance and occupant comfort and as such, definition and estimation of occupants’ behavior are common challenges during building energy policymaking, implementation and evaluation processes[1].
During the design stage of buildings, energy simulation is used to predict the energy consumption of buildings. However, many times there is a considerable difference between the predicted and actual energy consumption of buildings, mainly because human behavior and occupant preferences affect the gap between predicted and actual building energy performance. Changes in human behavior can increase the efficiency of energy consumption in buildings, while adaptive behaviors such as drinking cold beverages, changing/adjusting clothes, adjusting the room’s thermostat, and opening or closing operable window(s) can dramatically reduce building energy consumption[2].
Building energy consumption simulations: Which are the difficulties?
Due to the uncertainty associated with occupant behavior model inputs, simulation results often vary widely from actual building energy consumption which can be up to 30%. The integration of occupant behavior models in existing building performance simulation programs can help researchers and practitioners to simulate energy-related occupant behavior in buildings, and match simulated results with the actual energy use[3].
But, most of the occupant behavioral studies were conducted in office buildings, where the main occupant-related energy consumption is electricity, which is easy to measure. However, in the residential sector there are more challenges, mainly consist of diversity in occupant behavioral patterns and financial barriers[4].
How the BEYOND platform can help?
The BEYOND platform will implement an accurate building energy performance simulation, considering fine-grained occupancy and comfort profiles of building occupants. In fact, the platform will offer the ability to select the most cost-optimal renovation measures and to predict as much more accurate as possible estimation of building energy performance generated by the selected renovation measures at the design phase.
References
[1] Hu, S., Yan, D., Azar, E. and Guo, F., 2020. A systematic review of occupant behavior in building energy policy. Building and Environment, 175, p.106807.
[2] Paone, A. and Bacher, J.P., 2018. The impact of building occupant behavior on energy efficiency and methods to influence it: A review of the state of the art. Energies, 11(4), p.953.
[3] Hong, T., Taylor-Lange, S.C., D’Oca, S., Yan, D. and Corgnati, S.P., 2016. Advances in research and applications of energy-related occupant behavior in buildings. Energy and buildings, 116, pp.694-702.
[4] Csoknyai, T., Legardeur, J., Abi Akle, A. and Horváth, M., 2019. Analysis of energy consumption profiles in residential buildings and impact assessment of a serious game on occupants’ behavior. Energy and Buildings, 196, pp.1-20.
