Tree-based ensemble methods for predicting the module temperature of a grid-tied photovoltaic system in the desert

The PV module temperature is a crucial parameter in the performance of a grid-tied PV station and it has an important effect on the PV system efficiency. In this work, we are interested in predicting the module temperature of a grid-tied photovoltaic system using tree-based ensemble methods, namely random forest and boosted decision tree. The linear least square method and the artificial neural network method were used as a frame of reference to evaluate the results of tree-based ensemble methods. The hyper-tuning of the tree ensemble method was done to optimize the model's parameters and to improve accuracy and prevent overfitting. All developed models have similar accuracy during the training and they are equally applicable for predicting PV module temperature. The results showed that during testing, the tree-based ensemble methods maintained their accuracy with R2 above 0.98. Meanwhile, the accuracy of other methods declined, which proves the utility of the tree-based ensemble over the classical method especially the ANN

Automated summary

This study utilized tree-based ensemble methods such as random forest and boosted decision tree to predict the module temperature of a grid-tied photovoltaic system, with hyper-tuning done to optimize model parameters. Results showed that the tree-based ensemble methods maintained high accuracy during testing, outperforming classical methods, especially artificial neural networks.