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International Energy Agency (IEA): New report about Energy and AI

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International Energy Agency (IEA): New report about Energy and AI

The 'World Energy Outlook Special Report on Energy and AI', developed by the International Energy Agency (IEA), underscores the rapid ascent of artificial intelligence (AI) and its profound implications for the global energy sector, including the applications of AI in buildings.
Editorial Team

The Report on Energy and AI developed by the International Energy Agency (IEA) underscores the rapid ascent of artificial intelligence (AI) and its profound implications for the global energy sector. AI has transitioned from an academic endeavour to an industry with trillions of dollars at stake, propelled by advancements in computing power, data availability, and algorithmic breakthroughs. This evolution has significant consequences for the energy sector, particularly in terms of electricity demand. Data centres, which are central to AI development and deployment, accounted for approximately 1.5% of global electricity consumption in 2024. Meeting the growing electricity demand for AI will necessitate a diverse mix of energy sources, including renewables, natural gas, nuclear, and geothermal.

The report also delves into the application of AI in buildings, focusing on its potential to enhance energy efficiency and demand response. Buildings represent a significant share of global energy consumption, primarily for heating, cooling, lighting, and appliances. AI can optimise heating, ventilation, and air conditioning (HVAC) systems, lighting, and other building operations to reduce energy consumption and costs. For instance, AI can analyse data from sensors and weather forecasts to adjust HVAC settings in real-time, thereby improving comfort and efficiency. Similarly, AI can use occupancy sensors and natural light levels to adjust artificial lighting, reducing energy use. Additionally, AI can predict equipment failures and schedule maintenance proactively, which reduces downtime and extends the lifespan of building systems.

AI also enables buildings to participate in demand response programs, adjusting energy use during peak periods to reduce strain on the grid and lower energy costs. However, the adoption of AI in buildings is limited by factors such as fragmented ownership, lack of digitalisation, and inadequate incentives. Overcoming these barriers requires investment in digital infrastructure, regulatory support, and incentives for building owners and operators.

Energy and AI

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