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| Reference Number | NIA2_NGESO071 | |
| Title | AI Centre of Excellence GB Energy Industry Data Science Fellowship | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 90%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 10%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 February 2024 | |
| End Date | 30 June 2024 | |
| Duration | ENA months | |
| Total Grant Value | £230,000 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid plc (100.000%) |
| Industrial Collaborator | Project Contact , National Grid plc (0.000%) |
|
| Web Site | https://smarter.energynetworks.org/projects/NIA2_NGESO071 |
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| Objectives | The overall programme of work for the development of an energy data science fellowship scheme has been developed as a series of phases, delivering knowledge and experience as it progresses. This project forms the first of these programme phases and will establish the fellowship programme design. The programme design will be completed across three core work packages as follows:WP1 DiscoveryStakeholder mapping: Identify stakeholders critical to design and delivery of the fellowship programme and conduct an assessment to determine which engagement approach is most appropriate for each stakeholder.Review of existing programmes: Complete desktop research exercise to identify global best practise and benchmark of successful programmes and collate existing programmes within the sector that can be adapted to suit this scheme. Summarise lessons learnt from identified existing programmes to inform the design of this new energy data science fellowship programme.Needs assessment: Define the needs of the fellowship and assess why these cannot be covered by existing programmes identified.Vision definition: Define the vision and guiding principles to establish the purpose and key intent of the programme.WP2 Stakeholder EngagementEngagement planning: Design the engagement approach for each stakeholder as identified in WP1 and identify key stakeholder contacts.Engagement execution: Gather initial feedback on the programme, collate any applicable lessons learnt, and gather ideas on priority projects for the programme. This task will also identify the best parties for involvement in the first and subsequent future iterations of the programme.Initial partnership agreements: Develop high level agreements for participation to input into mobilisation of the programme.Engagement communications: Develop a communication framework that will define how stakeholders are engaged, including frequency and by what medium of engagement.WP3 Programme DesignDesign options: Develop a long list of options for the programme using the outputs of WP1 and WP2.Design assessment framework: Design an assessment framework with KPIs that will be used to refine the long list into a short list of options.Conduct feasibility assessment: Conduct an assessment against the short list of design options to determine which is most likely to deliver against requirements and can be established within the desired timeframe.Conduct developed design: Develop the chosen option by designing the operating model by which it will be administered and delivered.In line with the ENA"s ENIP document, the risk rating is scored Low:TRL steps = 1 (1 TRL step)Cost = 1 (<£500k)Suppliers = 1 (1 supplier)Data assumptions = 1Total = 4 This project will focus on the energy data science fellowship programme design and will set the overall vision and objectives for the scheme. The project will conduct a review of existing related programmes to collate learnings and ideas, followed by a needs assessment to establish which BAU activities, strategic initiatives and business units would most benefit from data science capabilities.Development and implementation of a clear stakeholder plan will help to identify and engage with potential stakeholders and partnerships across public sector, private sector and academia who could contribute to this programme. The project will carry out desktop research and stakeholder engagement to identify appropriate incentives to attract and retain best and brightest talent. The programmedesign delivered will include the delivery model, commercial model, operational model, high level agreements with programme partners, feasibility assessment, and a high-level business case in event of positive feasibility assessment. Define the vision and principles for the energy data science fellowship programme.Identify and engage with stakeholders critical to design and delivery of the fellowship programme.Design the delivery model, commercial model, and operational model for the programme.If feasibility assessment is positive, complete high level business case for the programme. | |
| Abstract | The success of the Net Zero transition requires attracting and retaining individuals with specialist data science skills and capabilities, as articulated in the UK"s National AI Strategy. The current ESO data science talent pipeline is decentralised and lacking coordination among industry, academia, tech partners and other various stakeholders. This project will aim to establish the design of an enduring and mutually beneficial fellowship to create a steady pipeline for data science skill and capabilities for ESO and the energy sector. This will create a mechanism for industry engagement and support addressing the skills gap across private, public, and academic institutions required for the net zero transition. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
