Analysis of clustering techniques on load profiles for electrical distribution

Akperi, B.T.; Matthews, P.C.

Abstract

The classification of electrical load profiles has become increasingly important as a driver for distribution companies in understanding substation data. The daily load profile can often give great insight into the types of customers connected to the substation and can assist with developing a long-term forecast. The literature in this area often uses data mining and clustering techniques to determine a load diagram representative for a subset of customers or substations. The type of technique used can often lead to representative load diagrams of unique shapes with differing numbers of customers belonging to each group. This paper analyses clustering techniques on representative load diagrams for primary substations at the distribution level. In particular, this paper will analyse clustering techniques in terms of their performance and effect on load profile groupings. The results show that K-means clustering showed the best performance in generating unique, well-populated cluster groups. This gives a greater understanding of the divisions between substations which can be used for future forecasting.

Citation

Akperi, B., & Matthews, P. (2014, October). Analysis of clustering techniques on load profiles for electrical distribution. Presented at 2014 International Conference on Power System Technology, Chengdu, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2014 International Conference on Power System Technology
• Start Date: Oct 20, 2014
• End Date: Oct 22, 2014
• Acceptance Date: Apr 1, 2014
• Publication Date: Oct 22, 2014
• Deposit Date: Oct 27, 2015
• Publicly Available Date: Nov 5, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1142-1149
• Series Title: Power System Technology (POWERCON), 2014 International Conference on
• Book Title: POWERCON 2014 Chengdu : 2014 International Conference on Power System Technology : Towards green, efficient and smart power system. Proceedings of a meeting held 20-22 October 2014, Chengdu, China.
• ISBN: 9781479950324
• DOI: https://doi.org/10.1109/powercon.2014.6993986
• Keywords: Clustering methods, Load modeling, Power distribution.
• Public URL: https://durham-repository.worktribe.com/output/1152548
• Additional Information: Date of Conference: 20-22 Oct. 2014