A COMPARATIVE APPROACH OF NEURAL NETWORK AND REGRESSION ANALYSIS IN VERY SHORT-TERM WIND SPEED PREDICTION

The accurate estimation of very short-term wind speed is essential for planning, management, and distribution of wind power produced by any installed wind turbine at a power plant. This study is based on very short-term wind characteristics and meteorological data measured from the wind farm at Bogdanci, in the Former Yugoslav Republic of Macedonia (FYROM) in between May-September 2015. Moreover, the study focuses on the comparative analysis of conventional polynomial based regression analysis and artificial neural network (ANN) methods for very short-term wind speed prediction at the interval of 10 min using four types of wind directions, and three atmospheric parameters. Polynomial regression analysis results in the maximum accuracy (R-2 = 0.71) in the prediction of wind speed rotation mean (WSRM) using the wind direction base mean (WDBM) and temperature. The ANN method achieves the best efficiency (R-2 = 0.97) in the prediction of WSRM using four types of wind directions and three atmospheric parameters. The ANN performs better than the conventional regression analysis in the prediction of each of the target wind speeds.