Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology

In the event of rapidly depleting conventional sources of energy such as fossil fuels and an urge for protecting environment from pollution, there is a thrust, across the globe, to produce cleaner and sustainable energy. Solar energy is one such form which is seen as a future source of energy. Solar energy is converted into electrical and thermal energy with the help of a hybrid photovoltaic thermal system (HPVTS). Performance of the PV panel may be affected when it gets heated due to high ambient temperature and other reasons. Proper cooling of PV panel may protect it from heating which may prevent deterioration in its performance. This paper proposes a mechanism for cooling the PV panel by circulating nanofluid around it. It also experimentally investigates the effect of critical input parameters such as irradiance, ambient temperature, flow rate, and concentration of the nanofluid on the nanofluid cooled HPVTS attributes (output responses) like overall efficiency, exergy loss, surface temperature, entropy generation, and electrical efficiency using the Taguchi's L-16 orthogonal array (OA). Weighting factors are calculated using Triangular fuzzy numbers (TFN) for output responses and optimal setting of the input parameters is obtained using TOPSIS. (C) 2020 Elsevier Ltd. All rights reserved.