Wind Speed Forecasting Using Multivariate Time-Series Radial Basis Function Neural Network

An accurate wind information forecasting plays the significant role for wind power system. However; the intermittent characteristic wind speed in nature over the time and from one location to another makes it hard to estimate the usage factor of wind farms. Therefore, actual long and short duration forecasting of wind speed is necessary for wind power generation system efficiency. In this research, we propose the method to forecast the wind speed data based on weather parameters including, temperature, sea level pressure, dew point, visibility, station pressure, rain intensity, optimum wind speed, maximum temperature, minimum temperature, hail intensity and thunder intensity data. Au parameters were predicted using time series model, then the result of predicted data was implemented to predict the wind speed data. This research implemented radial basis function neural network (RBF NN) to predict the wind speed and the results were compared to univariate time series forecasting and Least Square Support Vector Machine (LS SVM) algorithm. The result experimentally express better forecasting using RBF NN compared to two other models on the measures of MAPE, MSE and correlation coefficient.