Journal of Engineering in Industrial Research

h-index: 18     i10-index: 25

Advanced Machine Learning Techniques for Smart ‎Grid Optimization and Energy Management

Document Type : Original Research Article

Author

Alireza Joshan

Department of Power Electrical Engineering, University of Gilan, Rasht, Iran‎

https://doi.org/10.48309/jeires.2025.507095.1175
Abstract
Machine Learning Operations (MLOps) refers to a set of practices and processes that aim to effectively integrate data science and machine learning in the production and support of enterprise-level applications and products. Distributed energy resources (DERs), such as photovoltaic (PV) panels, energy storage systems, and wind turbines, play a special role in making power grids smarter. These resources contribute to a more decentralized and resilient energy infrastructure, but the probabilistic nature of their energy production poses significant challenges for grid management. Data science provides powerful tools to address these challenges and optimize the operation and scheduling of DERs, to maximize their benefits and ensure grid stability. MLOps can help manage and optimize energy consumption by using machine learning algorithms to analyze energy consumption data and identify consumption patterns. Afterwards, by predicting consumption trends and identifying areas for improvement, energy consumption reduction strategies can be implemented. For example, in industries with high energy consumption, MLOps can help develop and deploy models that automatically adjust systems for greater efficiency and, as a result, reduce energy consumption. This could include adjusting building temperatures, optimizing manufacturing processes, or even better managing city traffic to avoid excessive fuel consumption. Ultimately, using MLOps, organizations can automate and continuously improve energy consumption optimization processes.

Keywords

Advanced machine learning
‎Technique
Smart grid optimization
‎Energy management.‎

Subjects

OPEN ACCESS

©2025 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

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Journal of Engineering in Industrial Research
Volume 6, Issue 2
Winter 2025
Pages 142-156

  • Receive Date 16 February 2025
  • Revise Date 23 February 2025
  • Accept Date 23 March 2025

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