A data-driven framework for occupancy optimisation strategies towards energy demand reduction in non-domestic buildings

Proactive strategies for data-driven operational schedules based on monitored occupancy patters can enable energy demand reduction and optimal resource utilisation. A replicable framework that enables strategic closure of specific thermal zones is introduced and design and operational considerations are discussed. The potential of the framework is illustrated through a case study building, where up to 6% annual energy savings were estimated, highlighting the effectiveness of zone closures. Further findings indicated that total energy savings from the simultaneous closure of multiple zones were marginally larger compared to savings from closing off zones individually, depending on zone capacity and use. Therefore, balanced considerations should take place prior to selecting which zones to close off, also taking into account user acceptability, capacity of systems and controls as well as the internal layout of the building. The research enhances the understanding of the relationship between occupancy and energy demand, while offering recommendations for more energy efficient and sustainable building design and operations that require minimal capital cost. Practical Application: This paper provides design and operational considerations based on a robust and replicable framework for energy savings by optimising operational building schedules based on monitored occupancy patterns. This allows a proactive implementation of data-driven strategies that maximise resource utilisation, such as closure of specific zones during periods of low occupancy, without requiring any physical intervention. The findings on a case study building demonstrate that annual energy savings can be achieved, underscoring the potential for substantial cost reductions and improved energy efficiency. Applications can also extend to optimising energy demand for energy flexibility.