用于水务基础设施脱碳的学习增强在线控制
Learning-Augmented Online Control for Decarbonizing Water Infrastructures
AI总结：
针对水泵系统高能耗与ML方法最坏情况安全性不足的问题，提出学习增强在线控制算法LAOC，通过引入安全动作集设计，在保证安全约束的同时利用ML预测动态调度水泵，有效降低能源与环境成本。
摘要：
水务基础设施对饮用水供应、灌溉、消防及其他关键应用至关重要。然而，将水输送至使用点的水泵系统是关键，其消耗大量能源并每年排放数百万吨温室气体。随着数字水表和传感器在这些基础设施中的广泛部署，机器学习（ML）有潜力优化供水控制并减少温室气体排放。尽管如此，ML方法在最坏情况性能上的固有脆弱性，在部署于关键水务基础设施时引发了安全担忧。为应对这一挑战，我们提出了一种学习增强在线控制算法，称为LAOC，旨在动态调度水泵的激活和/或速度。为确保安全，我们为在线控制问题引入了一种新颖的安全动作集设计。通过利用这些安全动作集，LAOC可证明地保证安全约束，同时利用ML预测来降低能源和环境成本。我们的分析揭示了安全要求与平均能源/环境成本性能之间的权衡。此外，我们在一个建筑供水系统上进行了实验研究，以展示LAOC的经验性能。结果表明，LAOC能在保证安全约束的同时有效降低环境和能源成本。
英文摘要：
Water infrastructures are essential for drinking water supply, irrigation, fire protection, and other critical applications. However, water pumping systems, which are key to transporting water to the point of use, consume significant amounts of energy and emit millions of tons of greenhouse gases annually. With the wide deployment of digital water meters and sensors in these infrastructures, Machine Learning (ML) has the potential to optimize water supply control and reduce greenhouse gas emissions. Nevertheless, the inherent vulnerability of ML methods in terms of worst-case performance raises safety concerns when deployed in critical water infrastructures. To address this challenge, we propose a learning-augmented online control algorithm, termed LAOC, designed to dynamically schedule the activation and/or speed of water pumps. To ensure safety, we introduce a novel design of safe action sets for online control problems. By leveraging these safe action sets, LAOC can provably guarantee safety constraints while utilizing ML predictions to reduce energy and environmental costs. Our analysis reveals the tradeoff between safety requirements and average energy/environmental cost performance. Additionally, we conduct an experimental study on a building water supply system to demonstrate the empirical performance of LAOC. The results indicate that LAOC can effectively reduce environmental and energy costs while guaranteeing safety constraints.
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