Application of Machine Learning and Artificial Intelligence for Enhancing Reliability and Operational Efficiency in Rural Electric Power Systems

Authors

Shqiponja Nallbani Berisha
College AAB, Faculty of Economy
https://orcid.org/0009-0004-1369-2488
Dritan Ceka
College AAB, Faculty of Psychology
DOI: https://doi.org/10.18690/um.epf.7.2026.62

Synopsis

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.

Author Biographies

Shqiponja Nallbani Berisha, College AAB, Faculty of Economy

Prof. Ass. Dr. Sc. Shqiponja Nallbani Berisha is an Assistant Professor at the Faculty of Economics, AAB College, Prishtina, Kosovo. Her research focuses on digital transformation, artificial intelligence in business and marketing, innovation management, and technology-driven decision support systems. She has extensive experience in interdisciplinary research linking analytics, organizational strategy, and operational performance improvement. Dr. Nallbani Berisha has authored and co-authored several peer-reviewed publications indexed in international databases and actively participates in academic and applied projects related to digital ecosystems and sustainable development. ORCID: https://orcid.org/0009-0004-1369-2488

Prishtina, Kosovo. E-mail: shqiponja.nallbani@aab-edu.net

Dritan Ceka, College AAB, Faculty of Psychology

Prof. Ass. Dr. Sc. Dritan Ceka is an Assistant Professor at the Faculty of Psychology, AAB College, and serves as the corresponding author of this study. His academic work centers on cognitive processes, behavioral analytics, decision-making, and the human factors influencing technology adoption and organizational performance. He has contributed to multidisciplinary research integrating psychology with digital systems and data-driven management practices. Dr. Ceka’s expertise supports the behavioral and human-centered dimensions of AI-enabled decision-support frameworks. ORCID: https://orcid.org/0009-0009-1773-5648

Prishtina, Kosovo. E-mail: dritan.ceka@aab-edu.net

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Volume

Sustainable Governance in the Age of Artificial Intelligence: Interdisciplinary Perspectives on ESG, Digital Transformation and Corporate Responsibility

Pages

1253-1284

Published

July 3, 2026

Categories

Copyright (c) 2026 University of Maribor, University of Maribor Press

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Nallbani Berisha, S., & Ceka, D. (2026). Application of Machine Learning and Artificial Intelligence for Enhancing Reliability and Operational Efficiency in Rural Electric Power Systems. In J. Belak & S. Oberman Peterka (Eds.), Sustainable Governance in the Age of Artificial Intelligence: Interdisciplinary Perspectives on ESG, Digital Transformation and Corporate Responsibility (pp. 1253-1284). University of Maribor Press. https://doi.org/10.18690/um.epf.7.2026.62