Events Calendar

PhD Defense - Jun-young Kwak

PhD Candidate: Jun-young Kwak

Committee members:
Milind Tambe (chair)
Rajiv Maheswaran
Yu-Han Chang
Burcin Becerik-Gerber
Wendy Wood (outside member)
Pradeep Varakantham

Time: September 5th 9am-11am
Location: RTH 526

Title: The Power of Flexibility: Towards Agents That Conserve Energy in Commercial Buildings

Abstract:

Agent-based systems for energy conservation are now a growing area of research in multiagent systems, with applications ranging from energy management and control on the smart grid, to energy conservation in residential buildings, to energy generation and dynamic negotiations in distributed rural communities. Contributing to this area, my thesis presents new agent-based models and algorithms aiming to conserve energy in commercial buildings.

More specifically, my thesis provides three sets of algorithmic contributions. First, when multiple users contribute to energy savings, fair division of credit for such savings arises as an important question. I appeal to cooperative game theory and specifically to the concept of Shapley value for this fair division. Unfortunately, scaling up this Shapley value computation is a major hindrance in practice. Therefore, I present novel approximation algorithms to efficiently compute the Shapley value based on sampling and partitions and to speed up the characteristic function computation. Second, I present a novel BM-MDP (Bounded-parameter Multi-objective Markov Decision Problem) model and robust algorithms for multi-objective optimization under uncertainty both at the planning and execution time. The BM-MDP model and its robust algorithms are useful in (re)scheduling events to achieve energy efficiency in the presence of uncertainty over user's preferences. Third, I provide online predictive scheduling algorithms to handle massive numbers of meeting/event scheduling requests considering flexibility, which is a novel concept for capturing generic user constraints while optimizing the desired objective.

These new models have not only advanced the state of the art in multiagent algorithms, but have actually been successfully integrated within two agents dedicated to energy efficiency: SAVES and TESLA. SAVES focuses on the day-to-day energy consumption of individuals and groups in commercial buildings by reactively suggesting energy conserving alternatives. TESLA takes a long-range planning perspective and optimizes overall energy consumption of a large number of group events or meetings together. While SAVES and TESLA thus differ in their scope and applicability, both demonstrate the utility of agent-based systems in actually reducing energy consumption in commercial buildings.

I evaluate my algorithms and agents using extensive analysis on data from over 110,000 real meetings/events at multiple educational buildings including the main libraries at the University of Southern California. I also provide results on simulations and real-world experiments, clearly demonstrating the power of agent technology to assist human users in saving energy in commercial buildings.

Location: Ronald Tutor Hall of Engineering (RTH) - 526

Audiences: Everyone Is Invited

Contact: Lizsl De Leon