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

• Published in: Environmental science and pollution research international Vol. 30; no. 2; pp. 2406 - 2439
• Main Authors: Vakili, Masoud, Salehi, Seyedeh Akram
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2023; Springer Nature B.V
• Subjects: Aquatic Pollution; Artificial Intelligence; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Earth and Environmental Science; Ecotoxicology; Energy sources; Environment; Environmental Chemistry; Environmental Health; Environmental science; Forecasting; Fuzzy logic; Group method of data handling; Learning algorithms; Machine Learning; Modelling; Multilayer perceptrons; Neural networks; Neural Networks, Computer; Numerical methods; Parameter identification; Photovoltaic cells; Radial basis function; Review Article; Solar collectors; Solar energy; Solar heating; Support Vector Machine; Support vector machines; Thermal energy; Waste Water Technology; Water Management; Water Pollution Control; Artificial neural network; Solar thermal collectors; Machine learning; Solar energy
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-24044-y

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.