Support vector machine approach for short term load forecasting

The paper presents a new algorithm for short term load forecasting based on the support vector machine method and improved sequential minimal optimization training algorithm. The advantages of this method include: high forecasting accuracy, global optima property and small time complexity. The practical examples show that the support vector machine method outperforms the multiplayer neural networks and radial basis function networks on both the forecasting accuracy and the computing speed.