Energy security and AI - New Government paper

Advising the Government on how Artificial Intelligence (AI) and Machine Learning (ML) can be applied to the energy system.

GettyImages-1401906779_Andriy Onufriyenko

Researchers from an interdisciplinary group at the University have contributed to a recent Government POSTnote. The POSTnote explores how Artificial Intelligence (AI) and Machine Learning (ML) can be applied to the energy system, barriers to wider implementation, the challenges likely to be encountered, and policy considerations proposed by sector.

Contributors include:

Key points from the report:

  • Artificial intelligence (AI) and machine learning have a range of current and emerging applications within the energy sector, with the potential to optimise and accelerate energy planning, generation, and use.
  • AI could use data from devices such as smart meters and substation monitoring to help address current regional renewable connection delays and excessive network congestion.
  • There are technical and infrastructural barriers to wider adoption of AI in the energy system, including data access, regulation, skills gaps, and availability and reliability of the physical infrastructure that supports AI.
  • Stakeholders have raised concerns around privacy, cyber security, energy use, fairness, ethical use, and operational challenges.

The full report can be downloaded here. 

The team from the University of Sheffield research revolves around modelling and forecasting different components of the electricity system in the UK. Their research involves collaborating with multiple industry partners to develop machine learning models and deploy them in production for industry use. PV Live is a service for providing modelled Photovoltaic (PV) Generation developed by the Sheffield Solar team. They also research how AI solutions can be implemented in a trustworthy manner across the industry and how these solutions interact with existing regulations. 
 

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