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Artificial Intelligence for the Energy Transition

AI providers for renewables, battery, energy efficiency and smart grid

AI providers for renewables, battery, energy efficiency and smart grid

Artificial Intelligence is going to have a tremendous impact on our lives and the energy sector will be impacted significantly. To new college graduates, Bill Gates said in 2017 that “If I were starting out today… I would consider three fields. One is Artificial Intelligence. We have only begun to tap into all the ways it will make people’s lives more productive and creative. The second is Energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change.”


Artificial Intelligence (AI) is the science of making machines do things in the same way that a human being would do them by using learning capabilities, organization of memory and critical reasoning. It refers to a wide range of technology applications: robotics, image recognition, natural language understanding and generation, machine learning amongst others.

In the energy sector however, AI has been trialed but not scaled up yet.


AI initiatives have emerged in the last two years to improve renewables performance and unlock energy efficiency potential

The energy market gets more and more complex to run with the rise of decentralized generation. Energy players have turned to digital solutions and in particular AI to address these new challenges. In a recent survey made by Capgemini, we found that AI has started with the following applications:

  • Predictive maintenance and forecasting are priority use cases for AI in renewables: most startups like Nnergix, Greenbyte focus on reducing O&M costs and improving efficiency as it’s the greatest source of cost savings for renewable plants;
  • AI to better understand energy usage: AI gives more accurate energy consumption at the appliance level, which then enable the algorithm to predict future energy consumption;
  • AI, a powerful tool for smart grid and virtual power plants: Stem, Autogrid and the alike have built cloud-based platforms to manage renewable assets, battery storage and match with energy demand;


AI comes with many challenges to address for policymakers and energy regulators

The way AI will be used will be shaped by policy and regulatory decisions. Some questions to bear in mind are:

  • Data regulation: What level of data access will organizations have? What rules do we set up to protect data from cyberattacks? How to ensure data privacy? In Europe the General Data Protection Regulation (GDPR) enforces strict limits to the use of personal data;
  • Market design: How far do we want decentralized market operations to go? For example, will regulators encourage peer-to-peer platforms in every power market? At neighborhood, regional, country level? What will be the role of distribution system operators within these platforms?
  • Risks sharing and responsibility: In case of accidents related to AI, who will be held responsible?

The scale up of AI will require organizations to set up a proper AI strategy in line with business objectives

In spite of all the initiatives we are observing, very few energy players have deployed at scale AI across their organization.

  • Data is not systematically collected nor structured and the proper governance is not in place. You need a large amount of data, representing different features and coming in good quality. The data needs to be organized and you should have the right procedures in place;
  • Then AI initiatives may be isolated and not aligned with top management objectives. On the contrary, organizations should start with setting up the business objectives, identifying business needs and then assess if AI is right for them;
  • AI expertise is often hard to get. Instead, organizations can work with a partner to help them test and scale AI solutions;


There are more use cases for AI but collaboration between the different stakeholders is required

For example, AI can help automatize manufacturing and design of the equipment. As seen in other industries, back-office operations can be streamlined thanks to automation. Network operators, as demonstrated by Siemens grid control center in Germany, have just started to grasp the opportunities offered by AI to build auto-pilot grid operations, detect anomalies and incorporate self-healing capabilities. These use cases will require sharing of data between equipment manufacturers, network operators and utilities.

Capgemini Invent helps energy leaders to exploit the full potential of artificial intelligence. For more insight check


Marianne Boust,
Manager Energy, Utilities & Chemicals,

The ees International Magazine is specialized on the future-oriented market of electrical energy storage systems, not only from a technological-, but also a financial and application-oriented point-of-view. In cooperation with ees Global, the ees International Magazine informs the energy industry about current progress and the latest market innovations.

Contact: Xenia Zoller - zoller(at)