Sequential Minimal Optimization Algorithm Applied in Short-Term Load Forecasting

Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. Today more and more papers apply support vector machines in short-term load forecasting and get good results. In this paper we present a new load data qualification algorithm and combine it with improved sequential minimal optimization. The improved sequential minimal optimization algorithm can keep the length of the block data, which has remained unchanged, and it provides the excellent forecasting accuracy proved by the result of the experiment. The new load data qualification algorithm sorts the data with trade, and according to the electro-proportion of every trade does separate forecasting.