Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

Christoph Wittner; Gabriele Kotsis; Ismail Khalil

doi:10.1007/978-981-96-4285-4_11

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

Christoph Wittner
, Gabriele Kotsis
, Ismail Khalil^*

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Telekooperation

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.

Originalsprache	Englisch
Titel	Information and Communication Technology
Untertitel	13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II
Verlag	Springer Singapore
Seiten	130-140
Seitenumfang	11
Auflage	1
ISBN (elektronisch)	978-981-96-4285-4
ISBN (Print)	978-981-96-4284-7
DOIs	https://doi.org/10.1007/978-981-96-4285-4_11
Publikationsstatus	Veröffentlicht - 26 Apr. 2025

Publikationsreihe

Name	Communications in Computer and Information Science
Band	2351

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Wissenschaftszweige

102013 Human-Computer Interaction
102002 Augmented Reality
102006 Computer Supported Cooperative Work (CSCW)
102027 Web Engineering
202038 Telekommunikation
102021 Pervasive Computing
102015 Informationssysteme
102025 Verteilte Systeme
102 Informatik

JKU-Schwerpunkte

Digital Transformation
Sustainable Development: Responsible Technologies and Management

Zugriff auf Dokument

10.1007/978-981-96-4285-4_11

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency
Khalil, I. (Vortragende*r)
14 Dez. 2024
Aktivität: Vortrag oder Präsentation › Posterpräsentation › Science-to-science

Dieses zitieren

Wittner, C., Kotsis, G., & Khalil, I. (2025). Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency. in Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II (1 Aufl., S. 130-140). (Communications in Computer and Information Science; Band 2351). Springer Singapore. https://doi.org/10.1007/978-981-96-4285-4_11

Wittner, Christoph ; Kotsis, Gabriele ; Khalil, Ismail. / Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency. Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II. 1. Aufl. Springer Singapore, 2025. S. 130-140 (Communications in Computer and Information Science).

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abstract = "The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.",

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Wittner, C, Kotsis, G & Khalil, I 2025, Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency. in Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II. 1 Aufl., Communications in Computer and Information Science, Bd. 2351, Springer Singapore, S. 130-140. https://doi.org/10.1007/978-981-96-4285-4_11

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency. / Wittner, Christoph; Kotsis, Gabriele ; Khalil, Ismail.
Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II. 1. Aufl. Springer Singapore, 2025. S. 130-140 (Communications in Computer and Information Science; Band 2351).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

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N2 - The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.

AB - The transition from traditional, centralized electric grids to smart grids offers numerous opportunities for optimizing energy distribution and consumption. This paper presents a reinforcement learning-based approach for load scheduling in smart grids, aiming to reduce energy loss and enhance grid reliability. By leveraging consumer preferences, the proposed system schedules loads efficiently, thereby minimizing energy loss in transmission lines and reducing peak loads. Our results, tested on simulated grid environments of varying scales, demonstrate significant improvements in energy efficiency, suggesting that reinforcement learning can play a crucial role in the future of smart grid management.

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DO - 10.1007/978-981-96-4285-4_11

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Wittner C, Kotsis G , Khalil I. Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency. in Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II. 1 Aufl. Springer Singapore. 2025. S. 130-140. (Communications in Computer and Information Science). doi: 10.1007/978-981-96-4285-4_11

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

Abstract

Publikationsreihe

UN SDGs

Wissenschaftszweige

JKU-Schwerpunkte

Zugriff auf Dokument

Aktivitäten

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

Dieses zitieren