Comparison between LightGBM and other ML algorithms in PV fault classification




Photovoltaic faults, Fault diagnostics, Fault classification, Data-driven, Machine Learning


In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.


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How to Cite

Monteiro P, Lino J, Araújo RE, Costa L. Comparison between LightGBM and other ML algorithms in PV fault classification. EAI Endorsed Trans Energy Web [Internet]. 2024 Jan. 16 [cited 2024 Feb. 22];11. Available from: