Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Abiodun E. Onile; Juri Belikov; Eduard Petlenkov; Yoash Levron

doi:10.1109/ISGTEUROPE56780.2023.10407399

Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Abiodun E. Onile
, Juri Belikov
, Eduard Petlenkov
, Yoash Levron

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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’ comfort is crucial towards promotion of easy adoption of such models from consumers’ 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.

Original language	English
Title of host publication	Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023
ISBN (Electronic)	9798350396782
DOIs	https://doi.org/10.1109/ISGTEUROPE56780.2023.10407399
State	Published - 2023
Event	2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023 - Grenoble, France Duration: 23 Oct 2023 → 26 Oct 2023

Publication series

Name	IEEE PES Innovative Smart Grid Technologies Conference Europe

Conference

Conference	2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023
Country/Territory	France
City	Grenoble
Period	23/10/23 → 26/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Consumer comfort
Demand side recommender system
Distributed power systems
Hybrid digital twins
Industry 5.0

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1109/ISGTEUROPE56780.2023.10407399

Cite this

Onile, A. E., Belikov, J., Petlenkov, E., & Levron, Y. (2023). Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints. In Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023 (IEEE PES Innovative Smart Grid Technologies Conference Europe). https://doi.org/10.1109/ISGTEUROPE56780.2023.10407399

@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 = "English",

series = "IEEE PES Innovative Smart Grid Technologies Conference Europe",

booktitle = "Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023",

}

Onile, AE, Belikov, J, Petlenkov, E & Levron, Y 2023, Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints. in Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023. IEEE PES Innovative Smart Grid Technologies Conference Europe, 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023, Grenoble, France, 23/10/23. https://doi.org/10.1109/ISGTEUROPE56780.2023.10407399

Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints. / Onile, Abiodun E.; Belikov, Juri; Petlenkov, Eduard et al.
Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023. 2023. (IEEE PES Innovative Smart Grid Technologies Conference Europe).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

AU - Onile, Abiodun E.

AU - Belikov, Juri

AU - Petlenkov, Eduard

AU - Levron, Yoash

PY - 2023

Y1 - 2023

N2 - 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’ comfort is crucial towards promotion of easy adoption of such models from consumers’ 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.

AB - 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’ comfort is crucial towards promotion of easy adoption of such models from consumers’ 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.

KW - Consumer comfort

KW - Demand side recommender system

KW - Distributed power systems

KW - Hybrid digital twins

KW - Industry 5.0

UR - https://www.scopus.com/pages/publications/85187273842

U2 - 10.1109/ISGTEUROPE56780.2023.10407399

DO - 10.1109/ISGTEUROPE56780.2023.10407399

M3 - Conference contribution

T3 - IEEE PES Innovative Smart Grid Technologies Conference Europe

BT - Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

T2 - 2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

Y2 - 23 October 2023 through 26 October 2023

ER -

Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this