State of the art of Energy & Artificial Intelligence and New Challenges
To-date, most of the energy sector’s transition efforts have focused on hardware: new low-carbon infrastructure that will replace legacy carbon-intensive systems. Relatively little effort and investment has focused on another critical tool for the transition: next-generation digital technologies, in particular artificial intelligence (AI). These powerful technologies can be adopted more quickly at larger scales than new hardware solutions, and can become an essential enabler for the energy transition.
AI is already proving its value to the energy transition in multiple domains, driving measurable improvements in renewable energy forecasting, grid operations and optimization, coordination of distributed energy assets and demand-side management, and materials innovation and discovery. AI holds far greater potential to accelerate the global energy transition, but it will only be realized if there is greater AI innovation, adoption and collaboration across the industry.
The principles define the actions that are needed to unlock AI’s potential in the energy sector across three critical domains:
- Governing the use of AI:
- Standards – implement compatible software standards and interoperable interfaces.
- Risk management – agree upon a common technology and education approach to managing the risks presented by AI.
- Responsibility – ensure that AI ethics and responsible use are at the core of AI development and deployment.
- Designing AI that’s fit for purpose:
- Automation – design generation equipment and grid operations for automation and increased autonomy of AI.
- Sustainability – adopt the most energy-efficient infrastructure as well as best practices around sustainable computing to limit the carbon footprint of AI.
- Design – focus AI development on usability and interpretability.
- Enabling the deployment of AI at scale:
- Data – establish data standards, data-sharing mechanisms and platforms to increase the availability and quality of data.
- Education – empower consumers and the energy workforce with a human-centred AI approach and invest in education to match technology and skill development.
- Incentives – create market designs and regulatory frameworks that allow AI use cases to capture the value that they create.