In Libya, from time to time, the National Electricity Grid is directed by the National Electricity Company to conduct load shedding to mitigate pressure on supply at times of peak demand. This involves hours' of power outages in the area covered by this study, namely, the Southern Electrical Grid of Libya (SEGL). This paper discusses the results of a pattern extraction process using the kmeans clustering algorithm to predict load shedding for this scenario. The data consist of all loads shed in 40 electrical power stations in southern Libya for a two-year period from 2009 through 2010. An experiment was conducted to assess the effectiveness of the k-means clustering algorithm in grouping (clustering) the data as a means to predict future load shedding in the SEGL. Each cluster was generated five times to create five different cluster sizes (1, 2, 5, 7 and 10) with different seed values. The pattern extracted provided information on all attributes. The obtained results showed that the generated clusters are fit to be used for the future load shedding schedule problem in the SEGL.Index Terms-data mining, decision support system, k-means algorithm, load shedding, clusters.