Application of Machine Learning and Artificial Intelligence for Enhancing Reliability and Operational Efficiency in Rural Electric Power Systems - Nallbani Berisha, Shqiponja; Ceka, Dritan (Sustainable Governance in the Age of Artificial Intelligence: Interdisciplinary Perspectives on ESG, Digital Transformation and Corporate Responsibility, 2026)
The growing complexity of rural electric power systems, driven by aging infrastructure, variable loads, and distributed energy resources, calls for advanced data-driven solutions to improve reliability and operational efficiency. This study applies Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze operational data from rural distribution networks, including load profiles, outage records, and performance indicators. Using supervised and unsupervised models, the approach identifies fault patterns, predicts failures, and supports preventive maintenance and planning. Results show that AI-based models improve fault detection accuracy, reduce outage duration, and enhance reliability compared to traditional rule-based and statistical methods. The study demonstrates the practical value of AI and ML as decision-support tools for rural utilities and provides applied insights for improving system resilience and resource allocation.