Towards Optimal Planning for Distributed Coordination Under Uncertainty in Energy Domains

Recent years have seen a rise of interest in the deployment of multiagent systems in energy domains that inherently have uncertain and dynamic environments with limited resources. In such domains, the key challenge is to minimize the energy consumption while satisfying the comfort level of occupants in the buildings under uncertainty (regarding agent negotiation actions). As human agents begin to interact with complex building systems as a collaborative team, it becomes crucial that the resulting multiagent teams reason about coordination under such uncertainty to optimize multiple metrics, which have not been systematically considered in previous literature. This paper presents a novel multiagent system based on distributed coordination reasoning under uncertainty for sustainability called SAVES. There are three key ideas in SAVES: (i) it explicitly considers uncertainty while reasoning about coordination in a distributed manner relying on MDPs; (ii) human behaviors and their occupancy preferences are incorporated into planning and modeled as part of the system; and (iii) the influence of various control strategies for multiagent teams is evaluated on an existing university building as the practical research testbed with actual energy consumption data. We empirically show the preliminary results that our intelligent control strategies substantially reduce the overall energy consumption in the actual simulation testbed compared to the existing control means while achieving comparable average satisfaction level of occupants.