A review of recent developments in the application of machine learning in solar thermal collector modelling

Over the past few decades, the popularity of solar thermal collectors has increased dramatically because of many significant advantages like being a free, natural, environmentally friendly and permanent energy source. Today, developing and optimising different solar thermal energy systems are more important than before. Thus, there are various methods for investigating the performance of these systems, such as experimental, numerical and mathematical methods. One of the cutting-edge methods is artificial intelligence, which can predict key and effective parameters in solar collector efficiency. This review identified recent machine learning modelling, including multilayer perceptron artificial neural network (MLP-ANN), group method of data handling (GMDH), radial basis function (RBF), artificial neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and studies regarding different types of solar thermal collectors, namely non-concentration and concentration. Furthermore, it investigated the effect of various essential factors on the accuracy, potential issues and challenges facing the application of artificial intelligence in these systems. Finally, it will also be recommended opportunities for future research.

Automated summary

This review discusses the application of artificial intelligence in solar thermal energy systems, covering various models and methods, and evaluates their accuracy, issues, and challenges. Recommendations for future research are provided.