Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads.

摘要: For effective integration of building operations into the evolving demand response programs power grid, real-time decisions concerning use appliances for grid services must excel on multiple criteria, ranging from added value to occupants' comfort quality services. In this paper, we present a data-driven decision-support framework dynamically rank load control alternatives in commercial building, addressing needs decision criteria (e.g. occupant comfort, service quality) under uncertainties occupancy patterns. We adopt stochastic multi-criteria algorithm recently applied prioritize residential on/off loads, and extend it i) complex dimming lights, changing zone temperature set-points) building; ii) systematic zonal patterns accurately identify short-term opportunities. evaluate performance curtailment air-conditioning, lighting, plug-loads multi-zone office range design choices. With help prototype system that integrates an interactive \textit{Data Analytics Visualization} frontend, demonstrate way operators monitor flexibility energy consumption develop trust recommendations by interpreting rationale behind ranking.