Comparison between LightGBM and other ML algorithms in PV fault classification

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Monteiro, P (Autor) Outra; Lino, J (Autor) Outra; Rui Esteves Araújo (Autor) FEUP; Costa, L (Autor) Outra

Revista

Título: Eai Endorsed Transactions On Energy Web

Vol. 11

Páginas: 1-7

ISSN: 2032-944X

Editora: European Alliance for Innovation

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Z-Z0D

DOI: 10.4108/ew.4865

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6