Comparison between LightGBM and other ML algorithms in PV fault classification

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Monteiro, P (Author) Other; Lino, J (Author) Other; Rui Esteves Araújo (Author) FEUP; Costa, L (Author) Other

Journal

Title: Eai Endorsed Transactions On Energy Web

Vol. 11

Pages: 1-7

ISSN: 2032-944X

Publisher: European Alliance for Innovation

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6