Advanced machine learning techniques for predicting power generation and fault detection in solar photovoltaic systems

Abstract This study investigated the application of advanced Machine Learning techniques to predict power generation and detect abnormalities in solar Photovoltaic systems. The study conducted a comprehensive assessment of various sophisticated models, including Random Trees, Random Forest, eXtreme Gradient Boosting, Linear Regression, Gradient Boosting (GB), and Categorical Boosting (CatBoost), utilizing a substantial dataset of 97,333 sets. The analysis focused on two fundamental objectives: power prediction and fault identification, both of which are crucial for enhancing the effectiveness and dependability of PV systems. CatBoost and GB models exhibited exceptional performance in power prediction, with the…

• SV
  South Valley University