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Occupant behaviour optimisation towards thermal comfort and energy efficiency in buildings

Chytis Thodoris Maximilianos

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Summary

Occupant behaviour impact is under-recognized in the design and operation of buildings. Conventional approaches address thermal comfort and energy efficiency typically relying on rule-based and schedule-based systems. This study combines innovative dynamic methods and shifts focus on occupant behaviour and its impact on building conditions, thermal comfort, and energy efficiency. A framework is introduced to simulate building occupant behaviours and thus provide decision making support tools for low carbon communities while prioritising occupant comfort and satisfaction. The primary objectives include advancing scholarly understanding in building energy efficiency and indoor environmental quality, in the context of occupant-centric and regulated interventions and adjustments. Optimisation towards the above topics supports the base for an occupant-centric decision support system. In the interest of achieving the aforementioned goals, we employ a methodology, which comprises of (i) building energy simulations (EnergyPlus), (ii) occupant actions modelling, (iii) Python simulation coupling, (iv) machine learning (ReLU-based Artificial Neural Networks) and (v) mixed integer optimisation techniques. The optimisation techniques applied, utilize the concept of the “rolling horizon”, in order to determine strategies of (sub)optimal behaviours of a singular occupant over time. Outcomes include a comprehensive understanding of the intricate interplay between occupant thermal comfort adjustment actions, building thermal conditions, and building energy consumption under exposure to location specific weather conditions.

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