TY - GEN
T1 - The Smart Appliance Scheduling Problem
T2 - 23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
AU - Tabakhi, Atena M.
AU - Yeoh, William
AU - Fioretto, Ferdinando
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Daily energy demand peaks induce high greenhouse gas emissions and are deleterious to the power grid operations. The autonomous and coordinated control of smart appliances in residential buildings represents an effective solution to reduce peak demands. This coordination problem is challenging as it involves, not only, scheduling devices to minimize energy peaks, but also to comply with user’ preferences. Prior work assumed these preferences to be fully specified and known a priori, which is, however, unrealistic. To remedy this limitation, this paper introduces a Bayesian optimization approach for smart appliance scheduling when the users’ satisfaction with a schedule must be elicited, and thus considered expensive to evaluate. The paper presents a set of ad-hoc energy-cost based acquisition functions to drive the Bayesian optimization problem to find schedules that maximize the user’s satisfaction. The experimental results demonstrate the effectiveness of the proposed energy-cost based acquisition functions which improve the algorithm’s performance up to 26%.
AB - Daily energy demand peaks induce high greenhouse gas emissions and are deleterious to the power grid operations. The autonomous and coordinated control of smart appliances in residential buildings represents an effective solution to reduce peak demands. This coordination problem is challenging as it involves, not only, scheduling devices to minimize energy peaks, but also to comply with user’ preferences. Prior work assumed these preferences to be fully specified and known a priori, which is, however, unrealistic. To remedy this limitation, this paper introduces a Bayesian optimization approach for smart appliance scheduling when the users’ satisfaction with a schedule must be elicited, and thus considered expensive to evaluate. The paper presents a set of ad-hoc energy-cost based acquisition functions to drive the Bayesian optimization problem to find schedules that maximize the user’s satisfaction. The experimental results demonstrate the effectiveness of the proposed energy-cost based acquisition functions which improve the algorithm’s performance up to 26%.
KW - Beyesian optimization
KW - Constraint satisfaction problems
KW - Smart appliance scheduling problem
KW - User’s preference elicitation
UR - http://www.scopus.com/inward/record.url?scp=85102770731&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-69322-0_7
DO - 10.1007/978-3-030-69322-0_7
M3 - Conference contribution
AN - SCOPUS:85102770731
SN - 9783030693213
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 115
BT - PRIMA 2020
A2 - Uchiya, Takahiro
A2 - Bai, Quan
A2 - Marsá Maestre, Iván
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 November 2020 through 20 November 2020
ER -