Learning-Augmented Online Control for Decarbonizing Water Infrastructures

TL;DR

This paper introduces LAOC, a learning-augmented online control algorithm that safely optimizes water pump operations to reduce energy use and greenhouse gas emissions in water infrastructures, balancing safety and efficiency.

Contribution

The paper presents a novel safe action set design and an online control algorithm that integrates machine learning predictions with safety guarantees for water infrastructure management.

Findings

LAOC reduces energy and environmental costs effectively.

It guarantees safety constraints in water pump control.

Experimental results validate its practical benefits.

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 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.