Schneider 執行長：AI 可大幅節省電力

• Resource Advisor+ utilizes 'Sera' as the lead AI agent with capabilities to detect anomalies, run calculations, flag risks, and recommend strategies[1] • Carbon Performance product provides GHG Protocol-aligned calculations across Scope 1, 2, and 3 emissions with scenario modeling and reduction initiative management[1] • Supply Chain product (formerly Zeigo Hub) enables supplier engagement, customized decarbonization programs, target-setting, and individual supplier progress tracking[1] • EcoStruxure Microgrid Advisor uses predictive analytics and dynamic optimization of solar, storage, and backup generation, recognized in MINDS Cohorts 1 and 2 by the World Economic Forum[2][3] • Advanced distribution management systems (ADMS) and Distributed Energy Resource Management Systems (DERMS) enable real-time grid management and response to distributed resources[3] • Liquid cooling solutions address thermal management challenges as AI chips become hotter and more compact, accounting for up to 40% of data center cooling costs[7]

The convergence of AI demand and energy efficiency creates a critical inflection point for enterprise energy strategy. Organizations must transition from fragmented sustainability tools to integrated platforms that leverage AI for real-time optimization. The Energy Technology Coalition's focus on demand-side technologies suggests industry-wide recognition that grid resilience depends on intelligent consumption management rather than supply expansion alone. As AI workloads intensify, companies adopting predictive analytics and microgrid technologies will gain competitive advantages in cost control and emissions reduction. The 2026 energy landscape indicates that energy intelligence—not just energy supply—will become a core business competency, particularly for data-intensive industries.