Special Day Regression Model for Short-Term Load Forecasting

Zoran Jankovic, Slobodan Ilic, Boban Vesin, Aleksandar Selakov

Abstract: Short-Term Load Forecasting accuracy is profoundly affected by unexpected load shapes during so-called "special days." The lack of representative data sets for these days increases forecasting error. In this paper, the authors propose a novel method for forecasting accuracy improvements during special days. The proposed model tracks historical forecasting errors and uses the deviation trend to correct the most recent forecast. Model also contains the mechanism for recognizing hours for prediction correction on special days. Model validation was performed using Serbian Transmission System Company data and showed significant improvement for special days forecast accuracy.