Comms & IT
Understanding data is critical for today’s modern businesses. In the early 2000s, the retail sector began to change the way they sold produce from simply having items on their shelves, to tailor and target individual shoppers with specific offerings.
The introduction of store cards gave access to specific buying habits of individuals, with consumers happy to provide this data in return for points, discounts, or offers on relevant products. Similarly, the rise of Google has been founded on understanding the specific needs and wants of its consumers, by monitoring and acting upon data received, whether it be through its search engines or software.
Again, consumers, are happy for the organisations to use this data, as we get a direct benefit by way of free software to organise our life.
It is a parallel that can be drawn to many customer-facing organisations. To operate, it requires an understanding that consumers are different, and need to be grouped to a much finer degree than they have in the past.
By taking statistical techniques developed for the retail sector and applying them to electricity, it may be possible to more accurately forecast demand in a system design, network planning and pseudo real-time manner. Ultimately this could provide evidence to change design policy and release additional network head room. Thereby optimising investment plans with a more granular and targeted approach to reinforcement.
The project is split into four key tasks:
- Task 1 The characterisation of domestic demand
Task 2 The forecasting of demand (for network planning purposes)
Task 3 The development of adaptive forecasts (for network operational purposes)
Task 4 The development of control algorithms for pseudo-real time operations.
The outcomes of the project will be fed into the Thames Valley Vision (TVV) Tier 2 Low Carbon Network Fund (LCNF) project. Ultimately it is envisaged that the output of this will facilitate the development of new DNO specific policies and UK wide engineering standards.