AN ANFIS-BASED PREDICTION FOR MONTHLY CLEARNESS INDEX AND DAILY SOLAR RADIATION: APPLICATION FOR SIZING OF A STAND-ALONE PHOTOVOLTAIC SYSTEM

A suitable Neuro-Fuzzy model is presented for estimating sequences of monthly clearness index (Kt) in isolated sites based only on geographical coordinates. The clearness index (Kt)corresponds to the solar radiation data (H) divided by the corresponding extraterrestrial data (H0). Solar radiation data is the most important parameters for sizing photovoltaic (PV) system. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model is trained by using the Multilayer Perceptron (MLP) based on the Fuzzy Logic (FL) rule. The inputs of the network are the latitude, longitude, and altitude, while the outputs are the 12-values of Kt, where these data have been collected over 60 locations in Algeria. The Kt corresponding of 56 sites have been used for training the proposed ANFIS. However, the Kt relative to 4-sites have been selected randomly from the database in order to test and validate the proposed ANFIS model. The performance of the approach in the predictionKtis favorably compared to the measured values, with a Root Mean Square Error (RMSE) between 0.0215 and 0.0235, and the Mean Relative Error (MRE) not exceeding 2.2%. In addition, a comparison between the results obtained by the ANFIS model and other Artificial Neural Networks (ANN) is presented in order to show the performance of the model. An example of sizing PV system is presented. Although this technique has been applied for Algerian locations, but can be generalized in any geographical location in the world.