Machine learning in photovoltaic systems: A review
Jorge Felipe Gaviria, Gabriel Narváez, Camilo Andrés Guillén, Luis Felipe Giraldo, Michaël Bressan
Abstract
This paper presents a review of up-to-date Machine Learning (ML) techniques applied to photovoltaic (PV) systems, with a special focus on deep learning. It examines the use of ML applied to control, islanding detection, management, fault detection and diagnosis, forecasting irradiance and power generation, sizing, and site adaptation in PV systems. The contribution of this work is three fold: first, we review more than 100 research articles, most of them from the last five years, that applied state-of-the-art ML techniques in PV systems; second, we review resources where researchers can find open data-sets, source code, and simulation environments that can be used to test ML algorithms; third, we provide a case study for each of one of the topics with open-source code and data to facilitate researchers interested in learning about these topics to introduce themselves to implementations of up-to-date ML techniques applied to PV systems. Also, we provide some directions, insights, and possibilities for future development.