Enhanced load profiling for residential network customers

Bruce Stephen, Antti Mutanen, Stuart Galloway, Graeme Burt, Pertti Jarventausta

Research output: Contribution to journalArticle

68 Citations (Scopus)
409 Downloads (Pure)


Anticipating load characteristics on low voltage circuits is an area of increased concern for Distribution Network Operators with uncertainty stemming primarily from the validity of domestic load profiles. Identifying customer behavior makeup on a LV feeder ascertains the thermal and voltage constraints imposed on the network infrastructure; modeling this highly dynamic behavior requires a means of accommodating noise incurred through variations in lifestyle and meteorological conditions. Increased penetration of distributed generation may further worsen this situation with the risk of reversed power flows on a network with no transformer automation. Smart Meter roll-out is opening up the previously obscured view of domestic electricity use by providing high resolution advance data; while in most cases this is provided historically, rather than real-time, it permits a level of detail that could not have previously been achieved. Generating a data driven profile of domestic energy use would add to the accuracy of the monitoring and configuration activities undertaken by DNOs at LV level and higher which would afford greater realism than static load profiles that are in existing use. In this paper, a linear Gaussian load profile is developed that allows stratification to a finer level of detail while preserving a deterministic representation.
Original languageEnglish
Pages (from-to)88-96
Number of pages9
JournalIEEE Transactions on Power Delivery
Issue number1
Early online date11 Nov 2013
Publication statusPublished - Feb 2014


  • residential network customers
  • enhanced load profiling

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