Data Sharing challenges for Energy Modellers: outcomes of a workshop

Data Sharing challenges for Energy Modellers: outcomes of a workshop Catherine Jones & EDC Team September 2025 [email protected] Presentation CC-BY Background •UK Energy Research Centre (UKERC) www.ukerc.ac.uk •Independent whole systems research for a sustainable energy future •Energy Data Centre: expert team + discovery portal + research data curation • Responsible for Data Management Planning for UKERC • Energy modelling key to UKERC outputs •Access to energy information, now and for the future •Data Infrastructure for National Infrastructures • DSIT funded research data cloud project • Barriers to researchers sharing data • Project activities: landscape review, case studies, workshops, technical exploration www.ukerc.rl.ac.uk What is “special” about Energy research? • Very multidisciplinary leading to • Different expectations/practices • Different repositories • Cultural change works at different paces in different domains • Several large consortia as well as smaller projects • Energy sector is a heavy user of commercial “real-world” data • Interesting (mostly) domain challenges around the data itself: Elite interviews Models Jupyter notebooks Why energy modelling is important • Tool/approach which impacts on real-life decisions: used by academics, government and industry • Need to be able to keep both the record of results & the process (input data/settings, model version etc) •One size won’t fit all • Many scales of model & resourcing • Many scales of volume of data • Different restrictions •2024 Workshop aimed to explore current state of sharing, pinch points and successes • Report https://doi.org/10.5286/UKERC.EDC.000985 • Follow on from 2023 CREDS/UKERC Data Sharing for Energy Consortia workshop https://doi.org/10.5286/UKERC.EDC.000970 and https://doi.org/10.5286/UKERC.EDC.000971 Framing by Mike Colechin and Andy Boston Key Issues identified Findable Longevity of the availability of data. How emerging technology data , such as costs and environmental impact, becomes findable Locating additional data for the inputs of models is timeconsuming Accessible Data availability, especially from commercial companies, Privacy concerns and confidentiality Tensions between concerns of export controls, protecting IP and protecting critical NI vs open and FAIR data Lack of knowledge of appropriate licensing Interoperable Bringing open source and commercially sensitive data together. Data: formats, cleaning, documentation, standards People collect and think about data in different ways, so the same kind of data may be recorded differently. Reusable Data quality Inadequate documentation and metadata. Lack of common terminology Some industry related data, can be considered to be a “data black box” AI (for explainability). Ethical issues, such as those raised by smart meter data Models Academic funding: a lack of support or incentives for creating models with longevity. Funding for community developed models maintained with project funding. Importance of having communities around specific models Effort needed to run and participate in communities Recommendations •Establish requirements for a registry for energy models. •There is an identified need for a central platform to share data, run models and facilitate communities, DINI should capture these requirements Infrastructure •Enhance EDC guidance on software citation to explicitly cover citation and referencing of models to support discoverability within the literature. •Produce and publicise guidance available from the EDC to support the creation of FAIR data to be used in modelling. Guidance and support •Create a working group to review classifications of energy data for statistics. There could be a role for UKERC/EDC/ DAFNI as a convener of this activity. •Contribute to the AI adoption debate by writing a position paper on this topic, with input from DAFNI, UKERC and other collaborators. Energy Modelling community