Applications of intelligent methods in solar heaters: an updated review

Abstract

Heating and thermal comfort have remarkable share of final energy consumption. Until now, mostof the demand for heating applications in buildings is supplied by fossil fuels and electrical tech-nologies. Concerning the exhaustion of fossil fuels in the future and the environmental problemsrelated to their consumption, making use of renewable energy sources can be a practical alter-native. On this point, solar energy is an appropriate source to be applied for heating by utilizingdifferent technologies. The function and output of solar heaters depends on numerous factors, andthis causes difficulties in the prediction of their performance and modelling. In this scenario, intel-ligent techniques are helpful and have been used by several scholars in recent years. This paperreviews proposed models for the prediction of the performance of different solar heaters. The lit-erature review reveals that artificial neural Networks represent one of the most used approaches forthe performance prediction of solar heaters; however, other intelligent techniques, namely supportvector machines, have been used for this purpose too. Moreover, it is found that these methods havethe ability to predict with great precision by applying the appropriate approach and architecture. Inaddition, it can be noted that the function of the models generated based on intelligent techniquesare associated with some elements such as the employed function and architecture of the model.