Applications of intelligent methods in solar heaters: an updated review

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

Automated summary

Heating and thermal comfort contribute significantly to final energy consumption. Fossil fuels and electrical technologies have been the main sources for heating in buildings so far, but with concerns over the depletion of fossil fuels and environmental issues, renewable energy sources, particularly solar energy, can provide a practical alternative. Intelligent techniques, such as artificial neural networks and support vector machines, have been used to predict the performance of solar heaters with great precision. The function and architecture of the models generated based on these intelligent techniques play a crucial role in their accuracy.