Energy Industry Revolution: Importance, Advantages, Applications, and Illustrations
In the rapidly evolving world of technology, Artificial Intelligence (AI) is making significant strides in the energy sector. The focus is on optimising energy production, distribution, and consumption, as the growing power demand, driven by AI itself and broader electrification needs, necessitates innovative solutions.
AI is increasingly being employed to enhance renewable energy integration, improve grid flexibility, minimise carbon emissions, and manage the stresses on aging infrastructure. This is evident in the surge of AI-driven demand, with U.S. data centres alone projected to consume 65 gigawatts of power annually by 2025, doubling by 2030 globally. This paradoxical situation, where AI systems that optimise efficiency also strain energy grids and infrastructure, has prompted significant industry focus on solving this challenge.
Companies like Kraken Technologies are using AI to optimise renewable energy distribution with high accuracy, managing gigawatts of flexible energy and significantly offsetting CO₂ emissions. Bloom Energy deploys hydrogen-powered fuel cells at data centres to meet AI's power needs with a low carbon footprint.
AI models are also increasingly leveraging satellite data, weather sensors, and historical trends to provide real-time predictions of energy output from renewables, thus helping grid operators maintain stability despite variable supply.
Beyond operational optimisation, generative AI is being explored for innovation in developing new energy sources, optimising consumption, detecting equipment anomalies, and streamlining supply chains to further sustainability and cost reduction goals. McKinsey forecasts $550 billion in value creation over coming years across energy and related sectors leveraging generative AI.
The global AI in energy market is growing rapidly, predicted to reach approximately $7.78 billion in 2024, signalling increasing adoption by energy companies and grid operators.
Looking ahead, accelerated grid modernisation will be critical to meet AI-driven demand reliably, with investments in smart grids, energy storage, and more flexible, AI-managed generation sources. Half or more of the expanding electricity demand from data centres and AI-related loads is expected to be met by renewables supplemented by natural gas, nuclear, geothermal, and other low-carbon technologies.
Generative AI will play an increasingly important role in achieving net-zero emissions by innovating energy systems, optimising low-carbon technology deployment, and managing complex supply chains. However, there are uncertainties around how fast AI adoption will grow, impacts on power and cooling efficiencies, regulatory frameworks, and fuel supply—all influencing whether new shortages or surpluses of power arise.
In summary, AI is both a major driver of new electricity demand and a critical tool for transforming the energy sector towards sustainability, efficiency, and reliability. Its future impact hinges on coordinated innovation in AI technologies and energy infrastructure to meet growth without compromising environmental goals.
Machine learning in the finance industry is being used to invest in companies focused on energy and technology, recognizing the significant potential of AI-driven solutions in the energy sector.
Bloom Energy's use of AI for the deployment of hydrogen-powered fuel cells, which meet the power needs of data centers with a low carbon footprint, showcases the expanding role of machine learning in the finance and energy sectors, aiming to reduce emissions and meet the growing power demand of AI systems.
