能源效率是去碳化至关重要但未被充分利用的工具，这主要是由于识别浪费的复杂性所致。然而，人工智能（AI）正透过管理大量不可预测的数据来解决这些复杂问题。借由分析工业设备上的传感器，AI 可以优化能源消耗并自动进行效率调整，否则这将需要大量的人力资源投入。

将 AI 驱动的数位双生（digital twin）模型——最初为 NASA 的太空船模拟而开发——应用于再生能源设施已展示出显著的数据效益。研究显示，这些模型可以减少 35% 的非计划停机时间，并提高 8.5% 的能源产量。此外，该技术将故障检测准确度提高了 98%，并降低了 26% 的整体能源成本。

虽然 AI 提供了实质性的收益，但其实施受到自身高耗电量以及对支持性基础设施需求的限制。Sam Kimmins、César Quilodrán-Casas 和 Stephen Horrax 等专家强调，AI 必须与电气化、基础设施投资和政策支持相结合。如果得到妥善整合，AI 可以加速全球能源效率的进展，特别是在工业领域。

Energy efficiency is a vital yet underutilized tool for decarbonization, primarily due to the complexity of identifying waste. However, artificial intelligence (AI) is addressing these complexities by managing large volumes of unpredictable data. By analyzing sensors on industrial equipment, AI can optimize energy consumption and automate efficiency adjustments that would otherwise require significant human resource investment.

Applying AI-powered digital twin models—originally designed for NASA spacecraft simulations—to renewable energy facilities has demonstrated significant numerical benefits. Research shows these models can reduce unplanned downtime by 35 percent and boost energy production by 8.5 percent. Additionally, this technology improves fault detection accuracy by 98 percent and decreases overall energy costs by 26 percent.

While AI offers substantial gains, its implementation is constrained by its own high power consumption and the necessity for supportive infrastructure. Experts like Sam Kimmins, César Quilodrán-Casas, and Stephen Horrax emphasize that AI must be combined with electrification, infrastructure investment, and policy support. If integrated properly, AI could step up global energy efficiency progress, particularly in industry.