Understanding Climate Risk in Future Energy Systems: An Energy–Climate Data Hackathon

Accurate quantification of weather and climate risk on scales relevant to energy networks is therefore essential in supporting a rapid transition to low carbon and renewable energy systems, a key COP26 priority theme, though there remain many technical and scientific challenges (Bloomfield et al. 20...

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Published inBulletin of the American Meteorological Society Vol. 103; no. 5; pp. E1321 - E1329
Main Authors Fallon, James C., Bloomfield, Hannah C., Brayshaw, David J., Sparrow, Sarah N., Wallom, David C. H., Woollings, Tim, Brown, Kate, Dawkins, Laura, Palin, Erika, Houben, Nikolaus, Huppmann, Daniel, Schyska, Bruno U.
Format Journal Article
LanguageEnglish
Published Boston American Meteorological Society 01.05.2022
Subjects
Algorithms
Climate models
Climate science
Climatic data
Conferences
Data exchange
Energy
Forecasting techniques
Photovoltaic cells
Renewable energy
Renewable resources
Research projects
Resource management
Software
Teams
Time zones
Uptake
United Kingdom > UK
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Summary:Accurate quantification of weather and climate risk on scales relevant to energy networks is therefore essential in supporting a rapid transition to low carbon and renewable energy systems, a key COP26 priority theme, though there remain many technical and scientific challenges (Bloomfield et al. 2021b). The Energy–Climate Data Hackathon, initiated by the Universities of Oxford and Reading and supported by the Met Office, therefore sought to address three aims: investigate the risks and challenges facing future energy systems, to develop novel solutions to support exchange of both data and science understanding, and to foster the growth of an integrated community working at the interface of energy and climate research. Projects within the hackathon’s scope included developing decision support aids, producing improved solutions for data exchange between energy and climate science, model development, and creating novel end-user applications. The premise of this project was that with the recent uptake of rooftop solar photovoltaics, more accurate very short-term forecasting techniques are needed to assist in load balancing within networks.
ISSN:0003-0007
1520-0477
DOI:10.1175/BAMS-D-21-0305.1