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

Avtorji

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

Kratka vsebina

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.

Biografije avtorja

Shqiponja Nallbani Berisha, College AAB, Faculty of Economy

Prof. dr. sc. Shqiponja Nallbani Berisha je docentka na Ekonomski fakulteti AAB College v Prištini na Kosovu. Njene raziskave se osredotočajo na digitalno transformacijo, umetno inteligenco v poslovanju in trženju, upravljanje inovacij in tehnološko vodene sisteme za podporo odločanju. Ima bogate izkušnje z interdisciplinarnimi raziskavami, ki povezujejo analitiko, organizacijsko strategijo in izboljšanje operativne uspešnosti. Dr. Nallbani Berisha je avtorica in soavtorica več strokovno recenziranih publikacij, indeksiranih v mednarodnih podatkovnih bazah, in aktivno sodeluje v akademskih in aplikativnih projektih, povezanih z digitalnimi ekosistemi in trajnostnim razvojem. ORCID: https://orcid.org/0009-0004-1369-2488

Priština, Kosovo. E-pošta: shqiponja.nallbani@aab-edu.net

Dritan Ceka, College AAB, Faculty of Psychology

Prof. dr. sc. Dritan Ceka je docent na Fakulteti za psihologijo AAB College in je korespondenčni avtor te študije. Njegovo akademsko delo se osredotoča na kognitivne procese, vedenjsko analitiko, odločanje in človeške dejavnike, ki vplivajo na sprejemanje tehnologije in organizacijsko uspešnost. Prispeval je k multidisciplinarnim raziskavam, ki povezujejo psihologijo z digitalnimi sistemi in praksami upravljanja, ki temeljijo na podatkih. Strokovno znanje dr. Ceke podpira vedenjske in na človeka osredotočene dimenzije ogrodij za podporo odločanju, ki jih omogoča umetna inteligenca. ORCID: https://orcid.org/0009-0009-1773-5648

Priština, Kosovo. E-pošta: dritan.ceka@aab-edu.net

Prenosi

PDF

Izdaja

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

Strani

1253-1284

Izdano

3 julij 2026

Kategorije

Avtorske pravice (c) 2026 Univerza v Mariboru, Univerzitetna založba

Licenca

Creative Commons licenca

To delo je licencirano pod Creative Commons Priznanje avtorstva 4.0 mednarodno licenco.

Kako citirati

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