The increasing connectedness of everyday devices, objects and technologies, along with crowdsourced platforms such as social media, mobile phones and apps, often create extremely large and potentially unwieldy volumes of data. Artificial intelligence (AI) tools have emerged in many cases as the most effective approach to process and analyse this ‘big data’, making sense of it to inform what is happening now, what might happen next and what decisions to take in due course. AI has already been shown to yield better results for the prediction the variability of wind generation, as well as informing improved asset maintenance decisions based on huge data sets. AI’s influence is expanding beyond these initial applications into other areas to answer ever more complex questions: what opportunities does AI present in understanding consumer’s energy behaviours and preferences? Can AI predict the UK’s changing energy mix in near real-time at a low geographical resolution? How could humans and AI interact and make decisions in transmission/distribution network control rooms? Could and how can blockchain transform energy markets? Could AI and blockchain improve consumer engagement with energy systems?
At Revolutionizing Energy Systems: AI and Blockchain, we will investigate what they can and will do within the energy industry. This joint event by Cranfield University’s Centre for Energy Systems and Strategy and IBM will be a thought-provoking day, drawing on the most recent insights from the implementation of real world AI and blockchain systems in energy and power sector. Following lunch, it will unpack the risks, opportunities and challenges AI and blockchain might bring to the digitisation of energy systems.
Tea/Coffee on arrival
|09:45-10:00||Welcome & Introduction|
Erwin Frank-Schultz, IBM Artificial Intelligence - Setting the scene:
what are AI and blockchain and what they can do
|10:20-10:40||Kevin Gill, IBM - Blockchain technologies|
Dr Chao Long, Cranfield University - Blockchain Distributed Ledger Technology in Electricity Sector
• When and where to use DLT
• Application of DLT in energy trading
• Application of DLT in network operation
• Application of DLT in asset management
Session 2: Applications of AI and Blockchain in energy systems
James Kelloway, National Grid ESO
Decarbonising Electricity and a ML Solar Case Study
• Overview of Progress to date in Electricity Decarbonisation
• Solar in GB
• Forecasting with Machine Learning
Giulia Escher, Powervault
Powervault smartSTOR - Using Machine Learning to optimise the use of domestic batteries
• Combining Powervault batteries with Octopus Agile tariffs
• Using Machine Learning to predict customers' solar generation and electricity demand
• Automatically calculating optimum schedule for batteries to minimise cost
• Results of the trial so far
• Next steps and possible developments
Prof Jianzhong Wu, Cardiff University - How AI fits into the smart energy future
• AI in machine learning and forecasting (using load forecasting and load estimation as an example)
• AI in modelling (using Digital Twins as an example)
• AI in optimal decision making (using network reconfiguration, DLT based shared control, and Peer to Peer Energy trading as examples)
• Emerging risks with applications of AI
Panel discussion: Risks, Opportunities and Challenges - Alex Mahon, UK Power Networks; Dhara Vyas, Citizens Advice; Erwin Frank-Schultz, IBM; Steven Steer, Ofgem; Nina Klein, BEIS
Dr Chris Harrison, Energy Systems Catapult - Applications of AI and blockchain in energy systems
• AI in the energy sector – moving from innovation to business as usual
• Areas where AI innovation is occurring
• Factors affecting the scaling up of AI innovation
• Ways for energy sector to overcome the challenges
|15:00-15:30||Adam Jones, IBM & Alex Mahon, UKPN - Perspectives on how energy sector might look in 25 years|
|15:30||Wrap up & close|
Location and travel details
Cranfield University is located at the very heart of the UK – within the innovation triangle between London and the cities of Oxford and Cambridge.
Our central location provides easy access from the M1, excellent main line rail service as well as proximity to key international airports. Set in rolling countryside, Cranfield offers a rich, rural landscape complemented by thriving towns and picturesque villages.
Road: we are just 10 minutes from Junctions 13 and 14 of the M1 motorway. There is free parking on campus.
Rail: Milton Keynes or Bedford
Air: London Luton (22 miles), Heathrow (50 miles) or Birmingham (70 miles)
For further travel details please visit our location page.
Who should attend
Industry, academia, policy makers.