Produced in Association with SERIES 22 / Module 01 AI and Energy Management


and continuously adjust operational parameters to maximise efficiency. For instance, in a manufacturing plant, AI could control machinery and equipment, adjusting their operation based on real-time data to minimise energy consumption. This level of automation not only reduces energy waste but also frees up human resources for more strategic tasks. An example of AI being used in


industry is BMW and its use of Car2X and AIQX AI technologies14. The BMW Group is using AI to transform the models in production into communicative aids that, for instance, offer continuous information about their assembly status or independently identify and report assembly mistakes. As with buildings, AI can also facilitate


proactive predictive maintenance strategy in industrial settings. This is particularly important in the industrial sector where some industries are business critical to the wider economy and national infrastructure. And again, as with buildings, AI makes digital twinning possible even in the most of complex industrial settings.


AI and transportation The transport sector is one of the largest consumers of energy and accounts for around one-fifth of global carbon dioxide (CO₂) emissions15. AI can optimise route planning for


transport vehicles. By analysing traffic patterns, weather conditions, and other factors, and it can suggest the most efficient routes for drivers. This not only saves time but also reduces fuel consumption. AI can help manage fleets by determining the optimal number of vehicles needed for a particular route or time, thereby avoiding unnecessary energy use. In electric vehicles (EVs), AI can


enhance energy management by optimising battery usage. It can predict the energy consumption based on driving patterns and route information, allowing for more efficient use of battery power. Furthermore, AI can assist in smart charging strategies for EVs. It can determine the best times to charge based on factors like electricity rates and grid load, helping to balance the demand on the power grid and save costs. AI can also facilitate energy-efficient


driving behaviours. For example, it can provide real-time feedback to drivers on their driving habits, suggesting improvements that could lead to lower fuel consumption. Many EV cars (and


some modern ICE cars) have integrated speed and distance control. When this is used, the AI systems within the car maximise efficiency and range-saving energy. The ‘sat nav’ can continuously compute the most energy efficient journey based on real time data. Brought together, EVs and AI combined have enormous potential to save energy and decarbonise fleet vehicles.


AI in energy systems Possibly more than any other sector, AI has the potential to revolutionise our energy systems and many of the capabilities and opportunities already discussed throughout this article are applicable to the energy sector. The decarbonisation of energy


systems, including electrical infrastructure and heat networks, is critical for the world to meet international agreements on limiting the worst impacts of climate change. AI can be used to facilitate and enhance the operation of smart grids, heat networks and heat and power networks. Smart grids are electricity networks


that use digital technology to monitor and manage the flow of electricity from all generation sources to meet the varying electricity demands of end users. AI can be used to analyse data from sensors and smart meters to detect anomalies, predict failures, and


optimise the flow of electricity. This can improve the reliability and resilience of the grid. AI can be used to manage distributed energy resources, such as rooftop solar panels and battery storage systems. AI algorithms can determine when to store electricity in batteries for later use or when to feed it back into the grid. AI is key to maximising the financial


worth of renewable energy and facilitating its easier integration into the grid. An example of this type of approach is Google and its AI subsidiary DeepMind which developed a neural network in 2019 to enhance the accuracy of forecasts for 700 MW of wind power capacity. Based on historical data, the network developed a model to predict future output up to 36 hours ahead with significantly greater precision than was previously achievable16. AI can also help to efficiently


‘manage out’ or optimise older fossil fuel power stations as we transition to low carbon fuel sources. Australian technology company Synengco has utilised its AI-enabled technology, SentientSystem17, to optimise operations in complex industrial and power station infrastructure systems for over twenty years. Finally, UK Power Networks has


been able to speed up its electricity connection upgrade services through


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a partnership with Google involving digitalisation of its electricity cable maps18. Google’s DeepMind engineers have partnered with the distribution network operator (DNO) to create the digital versions of its electricity cable maps, which span over 180,000km of electricity cables. Digital maps mean UKPN can provide better, faster services to customers who apply for upgraded electricity connections when, for example, they need to connect renewable energy sites or electric vehicle (EV) charging hubs. In keeping with the theme of this article, the co-author is ChatGPT.


References 1. https://www.gov.uk/government/consultations/ ai-regulation-a-pro-innovation-approach-policy- proposals/outcome/a-pro-innovation-approach- to-ai-regulation-government-response#fn:7


2. https://www.iea.org/articles/case-study- artificial-intelligence-for-building-energy- management-systems


3. https://youtu.be/oxD4Wv74G4Q


4. https://www.europarl.europa.eu/news/en/ press-room/20240308IPR19015/artificial- intelligence-act-meps-adopt-landmark-law


5. https://www.whitehouse.gov/briefing-room/ presidential-actions/2023/10/30/executive- order-on-the-safe-secure-and-trustworthy- development-and-use-of-artificial-intelligence/


6. https://www.standards.org.au/news/standards- australia-adopts-the-international-standard-for- ai-management-system-as-iso-iec-42001-2023


7. https://www.weforum.org/agenda/2016/01/ the-fourth-industrial-revolution-what-it-means- and-how-to-respond/


8. https://www.pwc.co.uk/services/sustainability- climate-change/insights/how-ai-future-can- enable-sustainable-future.html


9. https://www.iea.org/reports/digitalisation-and- energy


10. https://knowledge.energyinst.org/new-energy- world/article?id=138332


11. https://www.trade.gov/market-intelligence/ united-kingdom-artificial-intelligence- market-2023


12. https://ukgbc.org/our-work/climate-change- mitigation/


13. https://www.iesve.com/digital-twins


14. https://www.bmwgroup.com/en/news/ general/2023/aiqx.html


15. Hannah Ritchie (2020) - “Cars, planes, trains: where do CO₂ emissions from transport come from?” Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/co2- emissions-from-transport’


16. https://deepmind.google/discover/blog/ machine-learning-can-boost-the-value-of-wind- energy/


17. https://sentientsystem.com/category/case- studies/


18. https://www.current-news.co.uk/google- develops-world-first-ai-powered-electricity- cable-map-software-with-uk-power-networks/


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