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^*

^*Corresponding author for this work

Institute of Telecooperation

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

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.

Original language	English
Title of host publication	Information and Communication Technology
Subtitle of host publication	13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II
Publisher	Springer Singapore
Pages	130-140
Number of pages	11
Edition	1
ISBN (Electronic)	978-981-96-4285-4
ISBN (Print)	978-981-96-4284-7
DOIs	https://doi.org/10.1007/978-981-96-4285-4_11
Publication status	Published - 26 Apr 2025

Publication series

Name	Communications in Computer and Information Science
Volume	2351

Fields of science

102013 Human-computer interaction
102002 Augmented reality
102006 Computer supported cooperative work (CSCW)
102027 Web engineering
202038 Telecommunications
102021 Pervasive computing
102015 Information systems
102025 Distributed systems
102 Computer Sciences

JKU Focus areas

Digital Transformation
Sustainable Development: Responsible Technologies and Management

UN SDGs

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

Access to Document

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

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency
Khalil, I. (Speaker)
14 Dec 2024
Activity: Talk or presentation › Poster presentation › science-to-science

Cite this

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 ed., pp. 130-140). (Communications in Computer and Information Science; Vol. 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. ed. Springer Singapore, 2025. pp. 130-140 (Communications in Computer and Information Science).

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title = "Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency",

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.",

author = "Christoph Wittner and Gabriele Kotsis and Ismail Khalil",

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month = apr,

day = "26",

doi = "10.1007/978-981-96-4285-4\_11",

language = "English",

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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 edn, Communications in Computer and Information Science, vol. 2351, Springer Singapore, pp. 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. ed. Springer Singapore, 2025. p. 130-140 (Communications in Computer and Information Science; Vol. 2351).

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

TY - GEN

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

AU - Wittner, Christoph

AU - Kotsis, Gabriele

AU - Khalil, Ismail

PY - 2025/4/26

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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.

U2 - 10.1007/978-981-96-4285-4_11

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

M3 - Conference proceedings

SN - 978-981-96-4284-7

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SP - 130

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BT - Information and Communication Technology

PB - Springer Singapore

ER -

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 ed. Springer Singapore. 2025. p. 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

Publication series

Fields of science

JKU Focus areas

UN SDGs

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Activities

Optimizing Smart Grids with Reinforcement Learning for Enhanced Energy Efficiency

Cite this