Smart Meter Data Characterization and Clustering for Peak Demand Targeting in Smart Grids

Oyedokun, J., Bu, S. , Xiao, Y. and Han, Z. (2018) Smart Meter Data Characterization and Clustering for Peak Demand Targeting in Smart Grids. In: 2018 IEEE PES Innovative Smart Grid Technologies Coneference Europe (ISGT Europe), Sarajevo, Bosnia and Herzegovina, 21-25 Oct 2018, ISBN 9781538645055 (doi: 10.1109/ISGTEurope.2018.8571875)

Abstract

The increasing popularity of smart meters deployed at customer sites provides a vital opportunity for network operators to efficiently support and implement demand response (DR) solutions to consumers. Currently, one focus for DR research is to extract knowledge from the smart meters data using data analytics techniques. Defining correct attributes is vital to access and target customers for DR. In this paper, a novel characterization model is proposed for peak load targeting of consumers. This model specifically describes customers' demand variations over one day period with consumption levels ranging from 0 to 1. A k-medoid clustering algorithm and a dynamic time warping (DTW) distance measure are proposed to cluster the characterized smart meter data. The COP index cluster validation technique is used to derive the optimal number of clusters. The proposed model is applied on the publicly available Irish smart meter data, and results show a well-defined grouping of customers based on their variation and peak load contribution.

• Item Type: Conference Proceedings
• Additional Information: The authors would like to thank the Petroleum Technology Development Fund (PTDF), Nigeria and the National Universities Commission (NUC), Nigeria for their valuable financial support to this work. The research is partially supported by US MURI, NSF CNS-1717454, CNS- 1731424, CNS-1702850, CNS-1646607.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Oyedokun, James and Bu, Dr Shengrong
• Authors: Oyedokun, J., Bu, S., Xiao, Y., and Han, Z.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781538645055
• Published Online: 13 December 2018