SOM++: integration of self-organizing map and k-means++ algorithms
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Short term electricity load forecasting is nowadays, of paramount importance in order to estimate next day electricity load resulting in energy save and environment protection. Electricity demand is influenced (among other things) by the day of the week, the time of year and special periods and/or days such as Ramadhan, all of which must be identified prior to modeling. This identification, known as day-type identification, must be included in the modeling stage either by segmenting the data and modeling each day-type separately or by including the day-type as an input. This paper investigates day-type identification approach for Algerian electricity load. Kohonen maps are used to identify daytypes. The K-Means clustering method will be used as a complementary method to precisely identify the obtained classes. Clustering validity is done by using a criteria measurement of quality. This work has allowed the identification of six different classes.