Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system

Abstract

The measurement of solar energy data is a difficult task and rarely available even for those stations where measurement has already been done. Further, the PV power forecasting is an important element for smart energy management system. In the present scenario, utilities are developing the smart‐grid application and PV power forecasting is an important key tool for a new paradigm. The forecasting of solar energy during clear sky‐condition can be easily estimated using mathematical models; however, forecasting under the influence of hazy, cloudy, and foggy sky‐conditions do not provide accuracy with these models. Therefore, an intelligent modelling techniques i.e. fuzzy logic, artificial neural network (ANN), and adaptive‐neural‐fuzzy‐inference system (ANFIS) models are proposed based on sky‐conditions namely clear/sunny sky, hazy sky, partially cloudy/foggy sky, and fully cloudy/foggy sky‐conditions for forecasting global solar energy. To design the model, 15 years averaged datasets of meteorological parameters were used for distinct climate zones across India. Further, comparison of intelligent models has been carried out with regression models using statistical indicators. The proposed model has been implemented for short‐term PV power forecasting under composite climatic conditions. Simulation results confirm that the ANFIS model provides supremacy for PV power forecast as compared to other models.