Performance evaluation of stand alone solar photovoltaic system using ANFIS predicted solar radiation: An experimental case study

Abstract

In South India, Tamil Nadu has enormous solar potential, because of its location on the Deccan plateau, with an annual average solar radiation ranging from 4-7 kWhm-2. This study, presents an adaptive neuro-fuzzy inference system (ANFIS) based modelling approach to predict the monthly global solar radiation (MGSR) in Tamil Nadu (India). The statistical performance parameters on predicted and measured values validate the model. In addition, a case study was discussed on the application of predicted solar radiation for sizing of the standalone PV system design including battery and their results were observed to be satisfactory when compared with the other earth regions found in the literature. A detailed investigation on the complete standalone PV system emphasize that the performance is satisfactory from predicted solar radiation and sizing co-efficients for any geographical location and particularly in isolated sites where the global solar radiation data is not always available.

Prediction of tar and particulate in biomass gasification using adaptive neuro fuzzy inference system

“…ANFIS is widely applied for investigation related to prediction [5], classification [6], control [7], recognition [8] and diagnosis [9]. Few research works were carried out in the field of renewable energy for prediction of solar and wind energy parameters [10][11][12][13]. However, soft computing based prediction of tar content and particulate has not been addressed so far.…”