Short-Term Load Forecasting Based on an Adaptive Hybrid Method

Abstract—This paper aims to develop a load forecasting method for short-term load forecasting, based on an adaptive two-stage hy-brid network with self-organized map (SOM) and support vector machine (SVM). In the first stage, a SOM network is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day’s load pro-file are used to fit the training data of each subset in the second stage in a supervised way. The proposed structure is robust with different data types and can deal well with the nonstationarity of load series. In particular, our method has the ability to adapt to different models automatically for the regular days and anomalous days at the same time. With the trained network, we can straight-forwardly predict the next-day hourly electricity load. To confirm the effectiveness, the proposed model has been trained and tested on the data of the historical energy load from New York Indepen-dent System Operator. Index Terms—Adaptiveness, load forecast, nonstationarity