The new IPCC report is unequivocal: more action is urgently needed to avert catastrophic long-term climate impacts. With fossil fuels still supplying more than 80% of global energy, the energy sector needs to be at the heart of this action.
Fortunately, the energy system is already in transition: renewable energy generation is growing rapidly, driven by falling costs and growing investor interest. But the scale and cost of decarbonizing the global energy system remain gigantic, and time is running out.
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.
2. As electricity supplies more sectors and applications, the power sector is becoming the core pillar of the global energy supply. Ramping up renewable energy deployment to decarbonize the globally expanding power sector will mean more power is supplied by intermittent sources (such as solar and wind), creating new demand for forecasting, coordination, and flexible consumption to ensure that power grids can be operated safely and reliably.
3. The transition to low-carbon energy systems is driving the rapid growth of distributed power generation, distributed storage and advanced demand-response capabilities, which need to be orchestrated and integrated through more networked, transactional power grids.
Navigating these trends presents huge strategic and operational challenges to the energy system and to energy-intensive industries. This is where AI comes in: by creating an intelligent coordination layer across the generation, transmission and use of energy, AI can help energy-system stakeholders identify patterns and insights in data, learn from experience and improve system performance over time, and predict and model possible outcomes of complex, multivariate situations.
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. But while AI''s application in the energy sector has proven promising so far, innovation and adoption remain limited. That presents a tremendous opportunity to accelerate transition towards the zero-emission, highly efficient and interconnected energy system we need tomorrow.
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. That is why the World Economic Forum has today released Harnessing AI to Accelerate the Energy Transition, a new report aimed at defining and catalysing the actions that are needed.
The report, written in collaboration with BloombergNEF and Dena, establishes nine 'AI for the energy transition principles' aimed at the energy industry, technology developers and policy-makers. If adopted, these principles would accelerate the uptake of AI solutions that serve the energy transition by creating a common understanding of what is needed to unlock AI''s potential and how to safely and responsibly adopt AI in the energy sector.
That response required around ten times the electricity of a Google search, by some estimates. And with 100 million users of ChatGPT every week, the extra energy demand starts to add up. And that''s just users on one platform.
Across the industry, the increasing energy demand, primarily from building and running the data centres used to train and operate AI models, is contributing to global greenhouse gas (GHG) emissions.
Microsoft, which has invested in ChatGPT maker OpenAI and has positioned generative AI tools at the heart of its product offering, recently announced its CO2 emissions had risen nearly 30% since 2020 due to data centre expansion. Google''s GHG emissions in 2023 were almost 50% higher than in 2019, largely due to the energy demand tied to data centres.
AI''s energy use currently only represents a fraction of the technology sector''s power consumption, which is estimated to be around 2-3% of total global emissions. This is likely to change as more companies, governments and organizations use AI to drive efficiency and productivity. Data centres are already significant drivers of electricity demand growth in many regions, as this chart shows.
As these systems gain traction and further develop, training and running the models will drive an exponential increase in the number of data centres needed globally – and associated energy use. This will put increasing pressure on already strained electrical grids.
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