AI in Energy

Artificial Intelligence (AI) is transforming the energy sector. In this white paper, we highlight key areas of impact both now and those that need to be transformed in the next five years.

At Energy Systems Catapult we take a whole systems approach to considering the future impact of energy sector changes. We work with a wide range of stakeholders to understand and communicate both approaches to innovation and best practice within Energy, Machine Learning, Data Science and AI.

Key points

Artificial Intelligence (AI) is transforming the energy sector. In this white paper, we highlight key areas of impact both now and those that need to be transformed in the next five years.

At Energy Systems Catapult we take a whole systems approach to considering the future impact of energy sector changes. We work with a wide range of stakeholders to understand and communicate both approaches to innovation and best practice within Energy, Machine Learning, Data Science and AI.

This White Paper: Transforming Energy through AI, discusses the following points:

  • Energy markets – the pivotal question is how best to design markets which help motivate the market players to optimally establish the flexibility and ability to balance supply and demand. This is in a new world where there are more ways to consume electricity on the demand side and greater variability in the timing of the supply from renewable sources.
  • Energy networks – have challenges around ensuring the supply is managed in this new world of increased flexibility and greater numbers of market players. And there is a real opportunity to provide this through better use of data and AI, for example through improved fault detection, improved power flow, the use of new performance measures and increased stability.
  • Transport – key themes include: improving travel efficiency and vehicle efficiency. The key contributions to these themes are the use of AI to improve route planning, EV battery design, congestion easing, and optimising charging across fleet vehicles.
  • Domestic energy use – behind the meter, there will increasingly be a move from gas to electricity and an increase in the numbers of smart devices controlling the consumption (EVs, heat pumps etc) and local supply (solar panels). AI solutions or interoperable sets of solutions are needed to solve both the needs of hiding the complexity for the individual in the home in controlling the myriad of energy devices and creating an optimal space for networks to smooth the increasing demand peaks from more devices.

In each of these areas, there is increased complexity. AI can both hide this complexity from the end user and optimise within these systems. A whole systems approach is required to consider the multiple stakeholders and system demands and constraints within these complex systems. AI, Machine Learning, Data Science and whole systems thinking are key areas in which Energy Systems Catapult already supports the transition of the energy system.

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AI in Energy

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