@mastersthesis {1504110,
	title = {The Power of Flexibility: Autonomous Agents That Conserve Energy in Commercial Buildings },
	year = {2013},
	type = {PhD thesis},
	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, 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. 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{\textquoteright}s preferences. Third, when multiple users contribute to energy
savings, fair division of credit for such savings to incentivize users for their energy saving activities
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.
These new models have not only advanced the state of the art in multiagent algorithms, but
have actually been successfully integrated within agents dedicated to energy efficiency: SAVES,
TESLA and THINC. 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. THINC provides an end-to-end integration within
a single agent of energy efficient scheduling, rescheduling and credit allocation. While SAVES,
TESLA and THINC thus differ in their scope and applicability, they 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.},
	author = {Kwak, Jun-young}
}
