Short term load forecasting using semi-parametric method and support vector machines

Abstract

Accurate short term load forecasting plays a very important role in power system management. As electrical load data is highly non-linear in nature, in the proposed approach, we first separate out the linear and the non-linear parts, and then forecast the load using the nonlinear part only. The semi-parametric spectral estimation method is used to decompose a load data signal into a harmonic linear signal model and a non-linear trend. A support vector machine is then used to predict the non-linear trend. The final predicted signal is then found by adding the support vector machine predicted trend and the linear signal part. With careful determination of the linear component, the performance of the proposed method seems to be more robust than using only the raw load data, and in many cases the predicted signal of the proposed method is more accurate when we have only a small training set.