Collaboration among data centers and grid stakeholders is essential for the creation of sustainable AI, according to Schneider Electric.
The U.S. artificial intelligence (AI) industry is poised for significant growth, with energy demand projected to increase substantially by 2030. However, this growth must be managed sustainably to avoid undue impacts on the environment and the broader economy. According to Schneider Electric, strategic site selection and supply planning are crucial to achieving this goal.
Schneider Electric's "Sustainable AI" scenario predicts AI power demand will reach 33.8 GW by 2030. To achieve this scenario, the company outlines several integrated strategies emphasizing energy efficiency, digitalization, and collaborative innovation.
One key strategy is optimizing data center operations with AI. Companies are using predictive AI technologies to enhance maintenance and operational efficiency, lowering energy consumption and reducing costs. This includes modular construction and design innovations that accelerate delivery and improve sustainability outcomes.
Another strategy is maximizing the use of renewable energy. Leading organizations, such as Equinix, are achieving near-complete renewable energy coverage for their data centers through power purchase agreements (PPAs) and investments in renewable capacity. This approach significantly reduces the carbon footprint of AI infrastructure.
AI-driven sustainability analytics is another important strategy. Utilizing AI platforms to analyze and optimize resource utilization in manufacturing and logistics can reduce emissions and improve operational efficiency. Lenovo’s deployment of intelligent sustainability solutions exemplifies this strategy.
Collaborative ecosystem engagement is also emphasized. Schneider Electric stresses the importance of collaboration among partners, customers, and suppliers to combine electric, digital, and automation solutions that enable the broader energy transition necessary for sustainable AI development.
Schneider Electric is also committed to decarbonization and net-zero goals. The company aims for a 25% absolute carbon reduction across its value chain by 2030 and net-zero emissions by 2050. Their Sustainability Impact program supports customers in saving and avoiding hundreds of millions of tons of CO₂ emissions, showcasing scalable sustainability impact.
Digital transformation in energy management is another strategy. Integrating AI, automation, and energy management drives efficiency in electricity usage across sectors, including AI applications in smarter homes and energy storage systems to improve sustainability and resilience.
However, predicting AI power demand growth is challenging due to the numerous and complex variables involved. Differences in demand modeling approaches affect projections, with utilities often overestimating future demand.
Centralized data center campuses in the "Abundance Without Boundaries" scenario each require up to 5 GW of dedicated power supply, potentially hindering economy-wide electrification efforts and exacerbating environmental harm. Schneider Electric's "Abundance Without Boundaries" scenario envisions unchecked AI growth across the economy, leading to uncoordinated and inefficient infrastructure development.
In contrast, the "Energy Crisis" scenario ultimately causes a significant contraction in AI demand. This scenario is driven by "insufficient grid planning, inaccurate AI demand forecasting, uncoordinated AI governance, and the reliance on computationally intensive techniques like synthetic data and multimodal learning."
In conclusion, the sustainable AI strategy in the U.S. industry promoted by Schneider Electric involves leveraging AI for operational efficiency and emissions reduction, investing in renewable energy, and fostering collaborative innovation across the ecosystem to enable a systemic transition towards low-carbon AI infrastructure and practices. The decisions made now will determine whether AI's growth helps or hinders the U.S. electricity system and broader economy.
The strategic incorporation of AI in data center operations, as suggested by Schneider Electric, can help lower energy consumption and reduce costs through the use of predictive AI technologies for maintenance and operational efficiency. Additionally, AI-driven sustainability analytics can optimize resource utilization in manufacturing and logistics, thereby reducing emissions and improving operational efficiency, as demonstrated by Lenovo's deployment of intelligent sustainability solutions.
