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Autonomous Agents and Multi-Agent Systems
Published in: Autonomous Agents and Multi-Agent Systems 4/2014

01-07-2014

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

Authors: Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Yu-Han Chang, Milind Tambe, Burcin Becerik-Gerber, Wendy Wood

Published in: Autonomous Agents and Multi-Agent Systems | Issue 4/2014

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Abstract

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.
previous article BDD-versus SAT-based bounded model checking for the existential fragment of linear temporal logic with knowledge: algorithms and their performance
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Footnotes
1
QBTU indicates Quadrillion BTU, which is used as the common unit to explain global energy use. 1 BTU = 0.00029 kWh.
 
2
Flexibility is already present in the meeting request as its constraints, and \(\alpha \) is a measure of such constraints.
 
3
\(e_{l,t}^i\) gets affected by a meeting in the previous time slot in the same location. This is because adjacent meetings affect the indoor temperature, which makes HVACs operate differently to maintain the desired temperature level.
 
4
The average performance of the predictive non-myopic (SAA) optimization depends on the prediction method of future requests. We, thus, additionally tested a more sophisticated prediction method considering the time factor that is one of key features determining the overall trend of requests (i.e., when the meeting requests arrive at the system to be scheduled; e.g., regular semester vs. summer/ winter break). With this additional consideration, the predictive non-myopic (SAA) method improved the overall performance of the predictive method by 1.1 %.
 
5
Note that canceled meetings were not considered while scheduling meetings in the earlier results.
 
6
While evaluating TESLA, we considered the assumed average number of electronic devices including the actual number of devices existing in each room as well as the average number of devices that people bring with them.
 
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Metadata
Title
TESLA: an extended study of an energy-saving agent that leverages schedule flexibility
Authors
Jun-young Kwak
Pradeep Varakantham
Rajiv Maheswaran
Yu-Han Chang
Milind Tambe
Burcin Becerik-Gerber
Wendy Wood
Publication date
01-07-2014
Publisher
Springer US
Published in
Autonomous Agents and Multi-Agent Systems / Issue 4/2014
Print ISSN: 1387-2532
Electronic ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-013-9234-0

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