Applications of Adaptive Network based Fuzzy Inference System (ANFIS)

Table 4 lists the applications of Adaptive Network based Fuzzy Inference System for solar energy systems.




Chaabene and Ammar


Prediction of solar radiation

Moghaddamnia et al.


Mellit et al.


Table 4. Summary of solar energy applications of ANFIS

Chaabene and Ammar (2008) used a neuro-fuzzy dynamic model for forecasting irradiance and ambient temperature. The medium term forecasting (MTF) gives the daily meteorological behaviour. It consists of a neuro-fuzzy estimator based on meteorological parameters’ behaviour during the days before, and on time distribution models. As for the short term forecasting (STF), it estimates for a 5 min time step ahead, the meteorological parameters evolution. According to normalized root mean square error (NRMSE) and the normalized mean bias error (NMBE) computation, the meteorological estimator carries out satisfactory estimation of the meteorological parameters.

Moghaddamnia et al. (2009) estimated daily solar radiation from meteorological data sets with local linear regression (LLR), multi-layer perceptron (MLP), Elman, NNARX (neural network auto-regressive model with exogenous inputs) and adaptive neuro-fuzzy inference system (ANFIS). They used five relevant variables for estimating the daily solar radiation (extraterrestrial radiation, daily maximum temperature, daily mean temperature, precipitation and wind velocity). In general, they have concluded that the ANFIS model does not have the ability to estimate solar radiation precisely, but LLR and NNARX models are the most suitable models for the area under study.

Mellit et al. (2008) proposed a new model based on neuro-fuzzy for predicting the sequences of monthly clearness index and applied it for generating solar radiation, which has been used for the sizing of a PV system. The authors proposed a hybrid model for estimating sequences of daily clearness index by using an ANFIS; the proposed model has been used for estimating the daily solar radiation. An application for sizing a PV system is presented based on the data generated by this model. Fig. 11 shows the proposed ANFIS-based prediction for the monthly clearness index.