Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation

Citation:

Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe, Timothy Hayes, Wendy Wood, and Burcin Becerik-Gerber. 2012. “Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation .” In Workshop on Agent Technologies for Energy Systems (ATES) at AAMAS .

Abstract:

to the significant growth in energy usage. Building multiagent systems for real-world energy applications raises several research challenges regarding scalability, optimizing multiple competing objectives, model uncertainty, and complexity in deploying the system. Motivated by these challenges, this paper proposes a new approach to effectively conserve building energy. This work contributes to a very new area that requires considering large-scale multi-objective optimization as well as uncertainty over occupant preferences when negotiating energy reduction. There are three major contributions. We (i) develop a new method called HRMM to compute robust solutions in practical situations; (ii) experimentally show that obtained strategies from HRMM converge to near-optimal solutions; and (iii) provide a systematic way to tightly incorporate the insights from human subject studies into our computational model and algorithms. The HRMM method is verified in a validated simulation testbed in terms of energy savings and comfort levels of occupants
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