Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting

Abstract

With the increasing global warming and enormous pollution, it is obvious to generate power from renewable energy sources. Wind power generation is volatile and intermittent in nature. Stable and reliable power supply may not be feasible with wind power. Power supply reliability is important as much as its availability. To deal with variability of wind power generation, power sector is highly dependent on forecasting methods and techniques. In addition to the implementation of  improved neural network models as generalized regression neural network (GRNN), radial basis function neural network (RBFN), support vector regression (SVR) model is used in short term wind power forecasting (WPF).The performance of these models in WPF is compared in terms of the mean absolute percentage error (MAPE). To carry WPF, the data of meteorological parameters like wind speed, temperature and historical wind power data of Indian Kolkata region are used for forecasting. Except the cases where the wind power generation is very less, SVR model has performed better than GRNN and RBFN in short term WPF. Short term WPF using GRNN is also considered reliable, but gives higher errors in terms of MAPE than SVR model. It is found that the proposed SVR model gives most accurate short term wind power forecasting.
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