AI Smart Building Technology Emerges as Key to Carbon Reduction
Global building operations waste approximately 2,800 terawatt-hours of electricity annually, equivalent to 10% of the world's projected total electricity consumption in 2025. A recent study by Lawrence Berkeley National Laboratory (LBNL) reveals that fully deploying AI smart management technology in commercial and residential buildings could reduce global energy use and carbon emissions by 19% by 2050, saving around 1,600 terawatt-hours of electricity. This figure not only surpasses Gartner’s estimate of 448 terawatt-hours for global data center electricity consumption in 2025 but also highlights the vast potential of AI technology in building energy efficiency.
The International Energy Agency (IEA) notes that global electricity demand grew at an above-average rate in 2024, with the expansion of AI infrastructure as one of the key drivers of rising consumption. However, building operations remain the largest energy-consuming sector globally, projected to account for 30% of the world’s total electricity use in 2025, or approximately 8,400 terawatt-hours. Research further reveals that nearly one-third of this energy is wasted during use, primarily due to inefficiencies in air conditioning, lighting, and equipment management.
How AI Technology Enables Building Energy Efficiency
AI smart building technology leverages real-time monitoring and predictive algorithms to precisely control energy use within buildings. By integrating sensors and machine learning models, the system can automatically adjust air conditioning temperatures, lighting brightness, and elevator operation modes based on foot traffic, weather conditions, and electricity price fluctuations, avoiding unnecessary energy consumption. The LBNL research team estimates that if this technology were adopted globally, the energy savings by 2050 would be equivalent to shutting down 400 coal-fired power plants or reducing carbon dioxide emissions by 1 billion tons.
For example, Google implemented an AI energy-saving system in its data centers, successfully reducing cooling energy consumption by 30%. The technology optimizes cooling equipment operation by analyzing historical data and real-time environmental parameters, achieving significant energy savings. However, compared to the billions of buildings worldwide, the current adoption of AI smart technology remains limited.