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Accurate load forecasting can keep the safety of power grid operation stability and guarantee the normal production and life of society. Support vector regression machine is suitable to load forecasting. Its nature of learning method is to give a good forecasting ability according to a small amount of data information. An algorithm is combining weighted support vector regression machine with feature selection to predict electricity load. It is good significance to forecasting short-term load. The algorithm is used to have feature selection by correlation coefficient and forecast short-term load by weighted support vector regression machine on optimization of data sets. The simulation experimental results indicate that the algorithm made good predicting results and valuable attempts.

Short-term load forecasting based on correlation coefficient and weighted support vector regression machine