Integrated architecture for short term load forecasting using support vector machines

A new hybrid technique using support vector machines (SVM) to forecast the next `24´ hours load is proposed in this paper. Four modules consisting of the basic SVM, peak and valley SVM, averager and forecaster and adaptive combiner form the integrated method for load forecasting. The proposed architecture can forecast the next `24´ hours load. The basic SVM uses the historical data of load and temperature to predict the next `24´ hour´s load, while the peak and valley SVM uses the past peak and valley data of load and temperatures respectively. The averager captures the average variation of the load from the previous load behavior, while the adaptive combiner uses the weighted combination of outputs from the basic SVM and the forecaster, to forecast the final load. The statistical and artificial intelligence based methods are conceptually incorporated into the architecture to exploit the advantages and disadvantages of each technique.