Enhancement of output power of semitransparent photovoltaic thermal air collector using ANFIS model.

The paper aims to develop a model using adaptive neuro-fuzzy inference system (ANFIS) architecture for enhancing output power of semitransparent photovoltaic thermal (PV/T) air collector by predicting the failure of PV panels for different weather conditions and different climate zones. Increased temperature of the photovoltaic module is a big problem which reduces its working life. The working and hotspot temperatures of photovoltaic (PV) modules have been reduced using ANFIS-based thermal design with optimal placement of PV cells which increase their life and reduce the failure rate which in turn increase the output power. The overall analysis reveals that output power is enhanced using ANFIS-based model by minimizing absolute error to 1.4% in 100 epochs by predicting accurate parameters.