Year: 2025
Type: article
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.
Authors: Jayesh Nana Patil, Ashvini Sunil Kolate, Prayag Satish Patil
Institution: Vellore Institute of Technology University
Language: English
Topic: Smart Grid and Power Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413488903