Load pattern clustering for short-term load forecasting of anomalous days

Load forecasting algorithms try to capture regular behaviours in historic load time series in order to perform an accurate forecast. The presence of anomalous days (holidays, working days between holidays, social events) is a serious drawback and requires a dedicated forecast. The successful application of artificial neural networks (ANN) in this field suggested the use of the Kohonen Self-Organising Map for clustering the similar load patterns and classifying day typologies. In order to evaluate the benefits of this choice, this work compares the Kohonen map with a classic clustering algorithm, both applied to grouping the daily load patterns in homogeneous sets. The information gathered by the clustered data is then applied to the 24-hour ahead load forecasting of anomalous days, by means of an ANN-based approach. The results show that the combined use of both clustering techniques allows better understanding of the anomalous load patterns