ABSTRACT
Photovoltaic (PV) energy is a free energy used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy payback time (EPBT). This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened energy payback time (EPBT). To confirm the effectiveness of the ANFIS algorithm, its results was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT. USE OF MPPT TECHNIQUES TO REDUCE THE ENERGY PAY-BACK TIME IN PV SYSTEMS