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Projects: Projects for Investigator
Reference Number	NPG_SIF_003
Title	Artificial Forecasting
Status	Completed
Energy Categories	Other Power and Storage Technologies(Electricity transmission and distribution) 100%;
Research Types	Applied Research and Development 100%
Science and Technology Fields	PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 20%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 80%;
UKERC Cross Cutting Characterisation	Not Cross-cutting 100%
Principal Investigator	Project Contact No email address given Northern Powergrid
Award Type	Network Innovation Allowance
Funding Source	Ofgem
Start Date	01 April 2023
End Date	01 July 2023
Duration	3 months
Total Grant Value	£160,000
Industrial Sectors	Power
Region	Yorkshire & Humberside
Programme
Investigators	Principal Investigator	Project Contact , Northern Powergrid (99.998%)
	Other Investigator	Project Contact , Northern Powergrid (Yorkshire) plc (0.001%) Project Contact , UKPN London Power Networks plc (0.001%)
	Industrial Collaborator	Project Contact , UK Power Networks (0.000%) Project Contact , Northern Powergrid (0.000%)
Web Site	https://smarter.energynetworks.org/projects/NPG_SIF_003
Objectives
Abstract	"This project addresses Challenge 2: ""Preparing for a net zero power system"", theme 1. Novel ways to reliably support low stability systems.As DNOs transition to DSOs, the current annual load forecasting process must become increasingly frequent (monthly, weekly, and daily), to support flexibility dispatch. The scope must also extend at least SO-fold to capture HV/LV substations, as low-carbon technologies connect to LV systems. Given networks have typically employed manual/disaggregated approaches to forecast load and account for the diversified contributions of new loads, novel approaches are required to enable system flexibility and support network stability under these new conditions. This project will develop innovative Al-based approaches to augment load forecasting capability. In turn, flexibility will become more realistic as a reinforcement option, and the available capacity in the network for new low-carbon loads will expand, increasing the speed, and lower the cost, of decarbonisation.Specifically, this project will:1. Test machine learning algorithms to produce load forecasts at EHV-to-HV transformation points, suitable for the shorter-term forecasting DSO systems require.2. Produce HV-to-LV forecasts, and develop Al techniques for modelling the connection of load. Capabilities delivered: This approach will enable: The ability to integrate, ex-ante, demands on the network such as EV charging, local PV and heat pumps; The understanding needed to promote targeted reinforcement options such as flexibility, moving from annualised to e.g. daily timescales. A step change in the scope of load forecasting, needed as the 230/400V system becomes the focal point of the energy system, without a step change in technical staff requirements.User needs: This project would benefit a wide set of users and the system at large, including: Connectees of low-carbon load who, given better network capacity information, would see reduced timescales and costs associated. Flexibility providers and controllers, who will have a better understanding of the likely value, call-off and effectiveness of flexibility services. Network customers who will see lower price pressure, given more effective and efficient flexibility and network reinforcement investment. Other electricity distribution companies, who benefit from the knowledge­ sharing mechanisms inherent to the SIF. Partners: Northern Powergrid is the electricity distribution system company for Yorkshire and the Northeast. Faculty Science Limited specialises in the implementation of custom Al systems for critical national infrastructure.UKPN, the electricity distribution system company for South East England, the East of England and London."
Publications	(none)
Final Report	(none)
Added to Database	01/11/23