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

Sustainable energy domains have become extremely important due 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.

[1]  Yiannis Demiris,et al.  Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs) , 2011, GECCO '11.

[2]  Ching-Lai Hwang,et al.  Multiple attribute decision making : an introduction , 1995 .

[3]  Scott Sanner,et al.  Efficient Solutions to Factored MDPs with Imprecise Transition Probabilities , 2009, ICAPS.

[4]  R. Baron,et al.  The relation between distraction and persuasion. , 1973 .

[5]  T. Ostrom,et al.  Repetition of highly similar messages and attitude change. , 1974 .

[6]  Milind Tambe,et al.  Improving adjustable autonomy strategies for time-critical domains , 2009, AAMAS.

[7]  Rajiv T. Maheswaran,et al.  Improving building energy efficiency with a network of sensing, learning and prediction agents , 2012, AAMAS.

[8]  David T. Neal,et al.  A new look at habits and the habit-goal interface. , 2007, Psychological review.

[9]  Nicholas R. Jennings,et al.  Adaptive home heating control through Gaussian process prediction and mathematical programming , 2011 .

[10]  Patrice Perny,et al.  A Compromise Programming Approach to multiobjective Markov Decision Processes , 2011, Int. J. Inf. Technol. Decis. Mak..

[11]  Robert Givan,et al.  Bounded-parameter Markov decision processes , 2000, Artif. Intell..

[12]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[13]  Sarvapali D. Ramchurn,et al.  Optimal decentralised dispatch of embedded generation in the smart grid , 2012, AAMAS.

[14]  John T. Cacioppo,et al.  Effects of message Repetition on Argument Processing, Recall, and Persuasion , 1989 .

[15]  Milind Tambe,et al.  A prototype infrastructure for distributed robot-agent-person teams , 2003, AAMAS '03.

[16]  Thomas A. Henzinger,et al.  Markov Decision Processes with Multiple Objectives , 2006, STACS.

[17]  A. Osyczka.  An approach to multicriterion optimization problems for engineering design , 1978 .

[18]  D. Stewart,et al.  Advertising Repetition: A Critical Review of Wearin and Wearout , 1988, Current Issues and Research in Advertising.

[19]  Sarvapali D. Ramchurn,et al.  Agent-based control for decentralised demand side management in the smart grid , 2011, AAMAS.

[20]  M.A. Wiering,et al.  Computing Optimal Stationary Policies for Multi-Objective Markov Decision Processes , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

[21]  Willett Kempton,et al.  Deploying power grid-integrated electric vehicles as a multi-agent system , 2011, AAMAS.

[22]  Milind Tambe,et al.  SAVES: a sustainable multiagent application to conserve building energy considering occupants , 2012, AAMAS.