Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector

Abstract The present study introduces a new approach to model a photovoltaic thermal nanofluid based collector system (PVT/N). Two artificial neural networks of radial-basis function artificial neural network (RBFANN) and multi-layer perception artificial neural network (MLPANN), as well as adaptive neuro fuzzy inference system (ANFIS) model are used to identify a complex non-linear relationship between input and output parameters of the PVT/N system. Fluid outlet temperature of the collector and the electrical efficiency of the photovoltaic unit (PV) are selected as two essential output parameters of the PVT/N system. In each model, the optimized structure is obtained through a Particle Swarm Optimization (PSO) technique. Zinc-oxide/water nanofluid is considered as the working fluid of the PVT/N setup. Experiments are repeated in ten days with thirteen data points in each day such that different environmental conditions are included in the measurements. Results of the three above-mentioned models are compared and validated with those of the measurements. All three models were found to be reasonably capable of estimating the performance of the PVT/N system. Moreover, the analysis of variance (ANOVA) results indicated that the ANFIS and RBFANN were more accurate in predicting the electrical efficiency and fluid outlet temperature, respectively.

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