Abstract: Short term electricity load forecasting is nowadays of paramount importance in order to estimate next day 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 religious and national events 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. In this study, a two stage clustering approach, based on unsupervised clustering methods is examined to identify regional Algerian electricity load day types. For instance, SOM (Self Organizing Maps) and fuzzy C-means will be presented and applied in detail to load day-type identification with the investigation of the fuzzy C-means clustering algorithm. The optimal number of clusters is obtained using fuzzy cluster validation measures, such as Xie and Beni's index (XB), among others. This application shows that the two-level clustering approach, can effectively identify load day types, for subsequent prediction modeling stages given a possible weighted classification among clusters for particular days.
