Integrated approach for short term load forecasting using SVM and ANN

A new hybrid technique using support vector machines (SVM) and artificial neural networks (ANN) to forecast the next dasia24psila hours load is proposed in this paper. The forecasted load for the next dasia24psila hours is obtained by using four modules consisting of the basic SVM, Peak and Valley ANN, averager and forecaster and adaptive combiner. These modules try to extract the various components like basic component, Peak and Valley components, average component, periodic component & random component of a typical weekly load profile. The basic SVM uses the historical data of load and temperature to predict the next dasia24psila hourpsilas load, while the Peak and Valley ANN 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 behaviour, 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.