tailieunhanh - Báo cáo vật lý: "AN ANFIS-BASED PREDICTION FOR MONTHLY CLEARNESS INDEX AND DAILY SOLAR RADIATION: APPLICATION FOR SIZING OF A STAND-ALONE PHOTOVOLTAIC SYSTEM"

Tuyển tập các báo cáo nghiên cứu khoa học trên tạp chí khoa học vật lý quốc tế đề tài: AN ANFIS-BASED PREDICTION FOR MONTHLY CLEARNESS INDEX AND DAILY SOLAR RADIATION: APPLICATION FOR SIZING OF A STAND-ALONE PHOTOVOLTAIC SYSTEM | Journal of Physical Science Vol. 18 2 15-35 2007 15 AN ANFIS-BASED PREDICTION FOR MONTHLY CLEARNESS INDEX AND DAILY SOLAR RADIATION APPLICATION FOR SIZING OF A STAND-ALONE PHOTOVOLTAIC SYSTEM A. Mellit1 2 A. Hadj Arab2 3 and S. Shaari4 department of Electronics Faculty of Sciences Engineering Jijel University of Médéa 26000 Algeria development Centre of Renewable Energy CDER . Box 62 Bouzareah Algiers 16000 Algeria 3Departamento de Energias Renerables- CIEMAT Arda Complutense 22 Madrid 28040 Spain 4Faculty of Applied Sciences Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Corresponding author solarman@ Abstract 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 prediction Kt is favorably compared to the measured values with a Root Mean Square Error RMSE between and and the Mean Relative Error MRE not exceeding . 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 .