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This project ended in Oct 2020 and is now closed.Dismiss

Virtual Monitoring Data (VM-Data)

Funding mechanismNetwork Innovation Allowance (NIA)
DurationOct 2019 - Oct 2020
Project expenditure£2.749M
Research areaTransition to Low Carbon Future
  • January 2021

    The decision has been taken to terminate the VM Data project at this stage. There are two key reasons that have led to this decision; • Outbreak of Coronavir…


The project will fulfil two key objectives:

Validation and enhancement of the model developed in last year’s LCT Detection NIA project; and
Development of a set of domestic half hourly consumption profiles which can be aggregated and used for virtual network monitoring at feeder level, as well as enabling enhanced network planning and demand prediction.


The operation of the electricity distribution network is complex and evolving – particularly with growing smart technologies and embedded, renewable generation. The increasing number of ‘invisible’ changes (Electric Vehicles, Embedded Generation, Smart Home, Storage) challenges existing network practices to the extent that the status quo is no longer possible. At present, technology change is outpacing changes in modelling and forecasting of consumer uptake of ‘smart’, Distributed Energy Resources (DER) or Electric Vehicle (EV) technology; therefore, it is difficult to monitor or understand the change in requirements on the LV network, under existing arrangements, without monitoring EV and DER impacts directly at source (or substation level).


The overall project method is to use IBM’s cutting-edge Artificial Intelligence (AI) and cognitive analytics capability to further develop a model developed in the previous “LCT Detection” NIA project, which analyses changes in consumption patterns linked to EV/DER proliferation or other factors. The model will use MPAN-level consumption data from the Energy Market Data Hub (EMDH) plus detailed consumption data (half-hourly intervals or less). The detailed consumption data will also be used to create and refine a set of half hourly customer profiles, which will be used to extrapolate EMDH consumption data into virtual daily consumption profiles, which will be aggregated to achieve virtual feeder and transformer half hourly loading profiles.