A newly published review article in Discover Applied Sciences evaluates the integration of artificial intelligence with solar photovoltaic systems, characterizing it as a breakthrough solution designed to enhance the performance, reliability, and forecasting of solar energy generation. Authored by Gaurav Kansal, the comprehensive survey provides an international perspective on how machine learning and advanced computational frameworks contribute to core operational domains. The analysis indicates that artificial intelligence-based techniques allow for optimized system operation and can improve overall energy conversion efficiency.

The review delineates several principal technical domains where artificial intelligence techniques deliver primary operational contributions within photovoltaic systems. These areas include maximum power point tracking and power generation forecasting. In addition, the paper examines the essential role of artificial intelligence methodologies in fault diagnosis and overarching system optimization.

The review details how techniques such as particle swarm optimization and genetic algorithms have shown significant potential in optimizing photovoltaic system parameters. These techniques have also shown significant potential toward optimizing maximum power point tracking and hybrid artificial intelligence-based frameworks under varying operating conditions.

Despite these technical performance advantages, the review underscores that artificial intelligence implementations face prominent practical challenges that complicate deployment in real-world solar systems. High computational demands, complex requirements for data management, and the persistent necessity of ongoing model maintenance present serious hurdles for operators. At the same time, continuing advancements in underlying artificial intelligence algorithms and supporting hardware technologies are steadily facilitating the integration of renewable energy systems, which may substantially decrease reliance on traditional energy sources.

To establish an application-oriented synthesis, the author conducted extensive literature searches across major scientific indexing databases during the defined review period. The analyzed literature was identified through systematic queries of Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The scope of this bibliographic survey specifically focused on evaluating research literature directed toward artificial intelligence applications in photovoltaic power forecasting, maximum power point tracking algorithms, operational fault diagnosis, image-based inspection routines, and critical cybersecurity concerns across modern installations.

The paper also provides a review of recent developments in the field, emerging research directions, and emerging solutions. These emerging solutions include transfer learning, few-shot learning, and edge computing.

The principal contribution of the review is a critical application-oriented synthesis of these artificial intelligence techniques. By systematically highlighting existing practical challenges, identifying current research gaps, and outlining emerging directions, the review establishes a foundation for more reliable and intelligent system operation.

Subject of Research: Artificial intelligence applications and emerging operational techniques in solar photovoltaic systems

Article Title: A comprehensive review of artificial intelligence applications in solar photovoltaic systems and future perspectives

Article References: Kansal, G. (2026). A comprehensive review of artificial intelligence applications in solar photovoltaic systems and future perspectives. Discover Applied Sciences. https://doi.org/10.1007/s42452-026-09822-4

Image Credits: AI Generated

DOI: 10.1007/s42452-026-09822-4

Keywords: artificial intelligence, photovoltaic systems, solar energy generation, maximum power point tracking, power generation forecasting, fault diagnosis, system optimisation, metaheuristic algorithms, particle swarm optimisation, genetic algorithms, energy conversion efficiency, data management, model maintenance, transfer learning, few-shot learning, edge computing, image-based inspection, cybersecurity