期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：702-705
语种：English
出版社：Ayushmaan Technologies
摘要：Electricity load forecasting has been an important risk management and planning tool for electric utilities. Load forecasting is necessary for economic generation of power. Classification of load pattern is an important task for load forecasting of customers and grouping them into classes according to their load characteristics. The different unsupervised clustering algorithms (modified follow-the-leader, k-means, fuzzy k-means and two types of hierarchical clustering) and the Self Organising Map to group together customers having a similar electrical behaviour. In the approach, all load curves of customers are first clustered with the clustering algorithms under a given number of clusters. This paper shows supervised and unsupervised algorithms for classification of electricity customers.
