The rapid adoption of AI platforms such as ChatGPT has raised concerns about the massive energy demands training these technologies requires and the need to keep data centres cool. However, the exact environmental impact remains to be determined due to limited transparency in the sector.
While AI companies like Microsoft, Google, and Amazon are trying to improve energy efficiency and reduce carbon emissions, AI’s overall energy consumption and environmental implications are still a topic of ongoing investigation. Conversely, this energy-thirsty advanced IT technology could also pave the way to manage the rising energy demand better.
With the power grid unable to grow forever, AI has unparalleled potential to accelerate the transition to a sustainable energy future by optimising cost, output and distribution across the value chain. By acting as a system integrator, it can orchestrate the optimisation of a decentralised world. Still, these systems will need to be carefully implemented and managed.
By analysing operational data, AI can accurately predict component failures and identify areas requiring maintenance. This proactive approach reduces downtime, minimises repair expenses, enhances safety measures and optimises operational efficiency.
According to Mark Parsons, a professor of high-performance computing at the University of Edinburgh and head of the UK’s supercomputing centre EPCC (formerly known as the Edinburgh Parallel Computing Centre), we’re at a significant moment. “For the last 30 years, modelling and simulation (M&S) have played a major role in turbine design, wind farm planning and more. It’s now coupled with AI to accelerate precise simulations, directly impacting investment quality in renewable energy production.”
AI will significantly facilitate the integration of renewable energy. For instance, when a cloud obstructs sunlight and casts shade on solar panels, AI can dynamically adjust the remaining exposed solar cells to compensate for the loss. Even a marginal increase of one percent in energy output can significantly enhance operational efficiency and generate optimal revenue for the operator.
However, while AI can help plan and design solar and wind farms, it is just a computing device, not a panacea for all problems, notes Parsons. “For example, traditional M&S will continue to be relied upon for long-term weather prediction. AI can only be trained to make short-term weather predictions based on recent data since it does not understand the underlying meteorological physics to forecast the weather accurately beyond a few days.”
Despite the challenges, one of the prospective benefits of employing AI to address our energy demand challenges is data sharing between electricity providers and the infrastructure and networks used to deliver electricity to consumers. With smart metres being installed in homes and businesses across Europe, the next logical step is to train AI models with this data to predict energy demand better.
“Smart grids already leverage data from smart metres to enhance distribution optimisation, load balancing, and real-time response. However, this data is not yet shared among providers, particularly in the UK. By pooling it, significant opportunities arise to develop larger-scale AI models that provide a broader understanding of energy usage nationwide.
For the last 30 years, modelling and simulation (M&S) have played a major role in turbine design, wind farm planning and more. It’s now coupled with AI to accelerate precise simulations, directly impacting investment quality in renewable energy production.
The energy industry, which traditionally revolved around hardware like engines, solar panels and turbine blades, is transforming. Data and information have taken centre stage. Vesa-Pekka Grönfors, Director of Data and Digitalisation at Wärtsilä Energy, believes that the sector must effectively utilise the value of data, digital software, and AI.
“Despite the perception of AI as a futuristic concept, digitalisation and data-driven solutions are taking significant strides. We have witnessed the emergence of smart tools, like Wärtsilä’s GEMS (GEMS Digital Energy Platform) system, actively contributing to the transition to renewable energy and helping the industry level up.”
He points to AI-powered predictive maintenance as having remarkable potential in extending the lifespan of power plants, energy storage systems and other energy-generating equipment. “By analysing operational data, AI can accurately predict component failures and identify areas requiring maintenance. This proactive approach reduces downtime, minimises repair expenses, enhances safety measures and optimises operational efficiency.”
As technological advancements accelerate, standards and guidelines such as the EU DATA Act strive to keep pace. It is no longer viable for a single company to solely drive all technological innovations. Instead, a more cohesive approach is needed, involving a network of vendors with diverse portfolios of technologies.
It is an approach that Wärtsilä has taken, says Grönfors, as the company goes beyond producing power plants and energy storage solutions, including GEMS. “The complexity of the energy ecosystem demands robust collaboration with stakeholders on the grid side. It is only through collective efforts and a shared vision that we can drive meaningful change.”
As Wärtsilä focuses on the future, the company is introducing an AI-powered decarbonisation service. Data analysis and advanced modelling can simulate a customer’s energy mix for the next three decades, enabling optimal energy utilisation, load balancing, the integration of renewables and alignment with existing decarbonisation plans.
Through a comprehensive approach, Wärtsilä empowers customers to reach their sustainability objectives, ends Grönforst. “It is crucial to invest in ongoing AI research and development, reinforce regulatory frameworks, and engage in discussions surrounding cybersecurity, data attacks and the risks associated with technological advancements. Together, we can pave the way towards a more sustainable and resilient energy future.”
