Soft Computing Techniques for a Solar Collector Using Solar Radiation Data

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Abstract

Prediction of solar radiation data using different soft computing approaches like Multi layer Perceptron (MLP), Artificial Neuro Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) for East coast region of India has been presented in this paper. Based on the input data like sunshine duration, temperature and humidity for the period of 1984-1999, soft computing models for a solar radiation data are analysed. The performances of these models are evaluated by comparing the predicted and measured data in terms of absolute relative error. Results obtained by ANFIS model predicted better compared to the results obtained using RBF and MLP. Based on the predicted solar radiation data, the efficiency of a solar flat plate collector is carried out. It has been found out that the soft computing approach is very promising for predicting of solar radiation and for calculation of efficiency of a solar flat plate collector. Eastern India region is considered for the present analysis due to its peculiar characteristics of solar radiation and cyclonic nature in the coastal areas.

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Mohanty, S. P., Rout, A., Patra, P. K., & Sahoo, S. S. (2017). Soft Computing Techniques for a Solar Collector Using Solar Radiation Data. In Energy Procedia (Vol. 109, pp. 439–446). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.03.059

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