AI-Driven Intelligent Sensor Fault Detection in Solar

Authors

DOI:

https://doi.org/10.69667/ajs.261103

Keywords:

Artificial Intelligence, Machine Learning, Deep Learning, Fault Detection.

Abstract

This research will provide a systematic literature review (SLR) of decentralized solar energy approaches as a means of economic development in remote areas, in particular, Libya. The objective of this review is to examine applied technologies in terms of their economic and social outcomes and consider challenges and opportunities associated with their implementation. Peer-reviewed articles published from 2020 to 2025 were analyzed systematically in accordance with the PRISMA approach. As a result of data synthesis, it was found out that photovoltaic (PV) systems including both standalone and hybrid ones prevail among used technologies. These help enhance the provision of electricity, promote economic growth, and reduce dependence on fossil fuels. Some of the problems facing this technology include a lack of finances, experts and adequate infrastructure. The opportunity areas for PV systems are immense in terms of solar power availability in Libya, community-driven, and participation in international projects. The research areas that can be taken up in the future are economic sustainability and scalability.

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Published

2026-11-10

How to Cite

AI-Driven Intelligent Sensor Fault Detection in Solar. (2026). Alqalam Journal of Science , 1019-1025. https://doi.org/10.69667/ajs.261103