Recent Advances in Machine Learning Applications in Electrical Utility Systems

In recent years, the electrical utility industry (EUI) has undergone rapid transformation with the integration of advanced technologies such as artificial intelligence (AI). Among these, machine learning (ML) has emerged as a powerful tool for addressing complex problems in the operation, control, and optimization of modern power systems. This paper presents a comprehensive survey of recent advancements in the application of machine learning techniques within the electrical utility domain, focusing on smart grids, load forecasting, anomaly detection, energy management, and system reliability. By reviewing 20 recent peer-reviewed articles from 2023–2025, we categorize the various ML models, discuss their computational trade-offs, and analyze their effectiveness across different power system applications. This survey identifies current research trends, highlights technical challenges, and outlines promising future directions for ML-based approaches in electrical utilities.

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Publication Details

Journal
Open Access Research Journal of Engineering and Technology
Published
2025-08-25
DOI
https://doi.org/10.53022/oarjet.2025.9.1.0077
Primary Topic
Electricity Theft Detection Techniques
Type
article
Field-Weighted Citation Impact
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article

Recent Advances in Machine Learning Applications in Electrical Utility Systems

Jayesh Patil, P. Patil, Ashvini S. Kolate
Open Access Research Journal of Engineering and Technology
Electricity Theft Detection Techniques
article

Recent Advances in Machine Learning Applications in Electrical Utility Systems

Jayesh Patil, P. Patil, Ashvini S. Kolate
article en

Abstract

In recent years, the electrical utility industry (EUI) has undergone rapid transformation with the integration of advanced technologies such as artificial intelligence (AI). Among these, machine learning (ML) has emerged as a powerful tool for addressing complex problems in the operation, control, and optimization of modern power systems. This paper presents a comprehensive survey of recent advancements in the application of machine learning techniques within the electrical utility domain, focusing on smart grids, load forecasting, anomaly detection, energy management, and system reliability. By reviewing 20 recent peer-reviewed articles from 2023–2025, we categorize the various ML models, discuss their computational trade-offs, and analyze their effectiveness across different power system applications. This survey identifies current research trends, highlights technical challenges, and outlines promising future directions for ML-based approaches in electrical utilities.

Open Access Research Journal of Engineering and TechnologyVol. 9(1)
Openalex Percentile: Top 79%
Electricity Theft Detection Techniques
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