Enhanced SVR Ensembles for Wind Power Prediction

Wind energy is an important component in the renewable energy mix, but successful integration into existing grid infrastructure is a major challenge. In this context, the accurate prediction of future wind generation power is extremely valuable as it would facilitate more efficient and sustainable provision. In a previous work, we proposed a method for wind power prediction based on an ensemble of support vector regressors. While this approach was very promising, there are still many avenues for further improvement. In this paper, we present two key extensions to the existing methodology and show that these result in significant performance improvements. In the first approach, we seek to vary the parameter values for the component support vector regressors. This is done in two ways: random initialization, and using an evolutionary strategy to select appropriate parameter values. Secondly, we exploit correlations between the input features to reduce the dimensionality and noise. Again, two approaches are tested: a feature selection stage using correlation, and Principal Component Analysis (PCA), which is shown to greatly reduce computational requirements while increasing prediction accuracy. The combination of these two enhancements produces interesting improvements over the previous prediction system, and these are presented and discussed in this paper.