2020
Cicirelli, F.; Gentile, A. F.; Greco, E.; Guerrieri, A.; Spezzano, G.; Vinci, A.
An Energy Management System at the Edge based on Reinforcement Learning Best Paper Proceedings Article
In: 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, 2020, ISBN: 9781728173436.
Abstract | Links | BibTeX | Tag: Edge computing, Energy Management Systems, internet of things, multi-agent systems, Reinforcement Learning
@inproceedings{Cicirelli2020c,
title = {An Energy Management System at the Edge based on Reinforcement Learning},
author = {F. Cicirelli and A. F. Gentile and E. Greco and A. Guerrieri and G. Spezzano and A. Vinci},
doi = {10.1109/DS-RT50469.2020.9213697},
isbn = {9781728173436},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},
journal = {Proceedings of the 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2020},
publisher = {IEEE},
abstract = {In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.},
keywords = {Edge computing, Energy Management Systems, internet of things, multi-agent systems, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.