Year: 2025
Type: conference-paper
Abstract: 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 learningaugmented 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.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: DESC: Type I: Enabling Carbon-Zero Colocation Data Centers via Agile and Coordinated Resource Management, Collaborative Research: DESC: Type I: A User-Interactive Approach to Water Management for Sustainable Data Centers: From Water Efficiency to Self-Sufficiency, Collaborative Research: CNS Core: Medium: Dynamic Data-driven Systems - Theory and Applications, Collaborative Research: National Science Foundation Expeditions in Computing: Carbon Connect -- An Ecosystem for Sustainable Computing, Collaborative Research: CNS Core: Small: Optimizing Large-Scale Heterogeneous ML Platforms, Collaborative Research: NGSDI: CarbonFirst: A Sustainable and Reliable Carbon-Centric Cloud-Edge Software Infrastructure, Collaborative Research: CPS: Medium: Enabling DER Integration via Redesign of Information Flows
OpenAlex ID: W4411345943