ΑΙhub.org
 

AI as an accelerator of the energy transition, opportunities for a carbon-free energy system


by
23 September 2021



share this:
NL AI coalition report front cover

In the next ten years, the Netherlands aim to take major steps towards increasing the amount of renewable energy produced and the electrification of heat demand and mobility. This desire requires a complete and highly complex transformation of the energy system. The fossil, central energy system is changing into a decentralised system based on renewable energy. Algorithms and AI can make a significant difference in accelerating this transition and in achieving an efficient and sustainable energy system.

When making decisions about the energy transition, such as investing in infrastructure or placing renewable sources, much depends on predictions, often based on limited data, and there are many different interests. New AI techniques can support these kinds of investment and design questions in order to better base them on facts, take better account of multiple interests, and also better explain the choices made and related uncertainties. In addition, there are opportunities for AI in automating energy system operations, supporting energy services, and predicting and automating maintenance. The opportunities and challenges for AI in this context were mapped out in a position paper, AI as an accelerator of the energy transition, opportunities for a carbon-free energy system. This document provides a guideline for next steps in research and innovation, and can serve as a framework of reference for opportunities and challenges that we cannot yet foresee.

This paper is written on behalf of the working group on Energy and Sustainability from the Netherlands AI Coalition (NL AIC). The aim of this working group is to support the development of new AI solutions for challenges in the energy sector and around sustainability and thereby stimulate the Dutch economy. This is achieved by bringing together expertise in AI and from the relevant sectors, and by supporting new collaborations to tackle these types of challenges. Please visit this page for more information.

Reference

The position paper AI as an accelerator of the energy transition, opportunities for a carbon-free energy system is written by Pallas Agterberg, Maarten Bijl, Johann Hurink, Han La Poutré, Gerdien van de Vreede, Mathijs de Weerdt and Tijs Wilbrink on behalf of the working group Energy and Sustainability of the NL AIC.

AIhub focus issue on affordable and clean energy

tags: ,


Mathijs de Weerdt is Associate Professor on Algorithms for Planning and Optimization at Delft University of Technology.
Mathijs de Weerdt is Associate Professor on Algorithms for Planning and Optimization at Delft University of Technology.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

monthly digest

AIhub monthly digest: February 2026 – collective decision making, multi-modal learning, and governing the rise of interactive AI

  27 Feb 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

The Good Robot podcast: the role of designers in AI ethics with Tomasz Hollanek

  26 Feb 2026
In this episode, Tomasz argues that design is central to AI ethics and explores the role designers should play in shaping ethical AI systems.

Reinforcement learning applied to autonomous vehicles: an interview with Oliver Chang

  25 Feb 2026
In the third of our interviews with the 2026 AAAI Doctoral Consortium cohort, we hear from Oliver Chang.

The Machine Ethics podcast: moral agents with Jen Semler

In this episode, Ben and Jen Semler talk about what makes a moral agent, the point of moral agents, philosopher and engineer collaborations, and more.

Extending the reward structure in reinforcement learning: an interview with Tanmay Ambadkar

  23 Feb 2026
Find out more about Tanmay's research on RL frameworks, the latest in our series meeting the AAAI Doctoral Consortium participants.

The Good Robot podcast: what makes a drone “good”? with Beryl Pong

  20 Feb 2026
In this episode, Eleanor and Kerry talk to Beryl Pong about what it means to think about drones as “good” or “ethical” technologies.

Relational neurosymbolic Markov models

and   19 Feb 2026
Relational neurosymbolic Markov models make deep sequential models logically consistent, intervenable and generalisable

AI enables a Who’s Who of brown bears in Alaska

  18 Feb 2026
A team of scientists from EPFL and Alaska Pacific University has developed an AI program that can recognize individual bears in the wild, despite the substantial changes that occur in their appearance over the summer season.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence