@inproceedings{9ba2e35e06f84be888aa43ef04012405,
title = "Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints",
abstract = "The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers{\textquoteright} comfort is crucial towards promotion of easy adoption of such models from consumers{\textquoteright} perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort.",
keywords = "Consumer comfort, Demand side recommender system, Distributed power systems, Hybrid digital twins, Industry 5.0",
author = "Onile, {Abiodun E.} and Juri Belikov and Eduard Petlenkov and Yoash Levron",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE European Union.; 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023 ; Conference date: 23-10-2023 Through 26-10-2023",
year = "2023",
doi = "10.1109/ISGTEUROPE56780.2023.10407399",
language = "الإنجليزيّة",
series = "IEEE PES Innovative Smart Grid Technologies Conference Europe",
booktitle = "Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023",
}
