An ANFIS-based model for solar energy forecasting and its smart grid application

Abstract

In the present work, a model underlying the principle of adaptive neural-fuzzy inference system (ANFIS) architecture is proposed. This research investigates the ANFIS-based model performance to forecast global solar energy under different weather conditions, namely, clear sky, hazy sky, hazy and cloudy sky, and cloudy sky; and for distinct climate zones using salient meteorological parameters. Power generation from solar photovoltaic (PV) may be affected by different factors, such as cloud cover, topographical locations, time, and seasonal variations. Therefore, accurate forecasting of PV power is an important factor for system reliability and robustness. In this work, the proposed model is implemented for smart grid applications in forecasting short-term PV power in a composite climate zone. Finally, the accuracy of the model is compared with other approaches, namely, support vector machine, feedforward neural network, multivariate adaptive regression splines, generalized regression neural network, linear neural network, fuzzy logic-based model, and empirical models based on multiple regression analysis using statistical performance indicators. The overall analysis reveals that the ANFIS-based model outperforms the other models in terms of accuracy.