Achieving Improved Personalization and Energy Efficiency in Cohabited Work-Spaces Through Data-Driven Predictive Control

Abstract This paper studies the problem of indoor zone temperature control in shared work-spaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. We consider two scenarios to assess the performance of our control strategies. The first scenario requires time-bound pre-cooling/pre-heating of a shared space in preparation for a scheduled activity (Scenario A). The second scenario considers a cohabited work-space where occupants have different temperature preferences (Scenario B). Utilizing an on-campus smart conference room (SCR) as a test-bed, we use data-driven model learning to establish a relationship between the room’s heating, ventilation and cooling (HVAC) operations and the zone temperatures. Next, we use a model predictive control (MPC)-based approach to achieve a desired average temperature while minimizing power consumption (for Scenario A) and minimize the thermal discomfort experienced by individuals based on their temperature preferences (for Scenario B). The experimental results show that for Scenario A, the proposed control policy can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Scenario B, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants’ thermal preferences.