Leveraging Big Data to develop an expert system for the optimal operation of smart water networks

全球老化的饮用水系统（DWS）面临着越来越大的压力，需要减少非收入用水（NRW，估计每年超过140亿美元），最大限度地降低能源成本，并减少温室气体排放（ghg）。然而，据美国水务协会（American Water Works Association）估计，到2039年，更换这些老化基础设施的资本成本将超过4720亿美元，这一成本令人望而却步，因此需要创新的解决方案来解决这些重大问题。由于无线传感器的最新发展，许多DWS现在正在不同的空间和时间尺度上收集关键变量（例如，包裹级消费者用水需求）的高频流，从而产生大数据，并促使传统DWS向智能水网（SWANs）的转变，从而能够减轻DWS的次优操作（例如，通过最大限度地减少能源使用）。swan依赖于基于数据驱动模型（如机器学习）的专家系统，以确定满足DWS管理人员目标的最佳运营决策，这些管理人员在面临众多不确定性（如不断变化的水需求）的情况下努力实现DWS的可持续运营。然而，swan仍处于起步阶段，目前还没有专家系统能够以高效的计算方式处理大数据并解释DWS中的不确定性。我的长期愿景是通过使用swan来优化DWS。未来5年，我的研究计划将集中在实现专家系统以应对这些重要挑战的最迫切需求上。专家系统将通过三个短期目标依次建立：1)将探索新的最先进的数据驱动模型，用于预处理和从与SWANs相关的大数据中做出准确预测（例如，包裹级消费者用水需求）；2)这些模型将被纳入一个新的随机框架，以解释几个重要的不确定性来源（例如，模型结构）和时间相关性，以提高专家系统的可靠性；3)前两个阶段将结合强化学习，根据关键操作目标（最小化NRW、能源成本和/或温室气体）优化SWANs（重点是泵的调度优化）。为了证明它们的优越性，每个目标的新发展将与当前最先进的方法和水务公司采用的基准方法进行严格比较。拟议的研究将推进优化swan的新知识，并为水务公司提供一种新的专家系统，以应对紧迫的挑战，有可能为水务公司每年节省46亿美元的运营成本。通过我的项目，8名学生研究人员将获得必要的技能，在水务公司（例如渥太华市）和投资于SWANs的私营公司（例如Innovyze）产生重大影响，为水资源的可持续管理做出贡献，并将加拿大置于SWANs研究的前沿。

Aging drinking water systems (DWS) worldwide are under increasing pressure to reduce non-revenue water (NRW, estimated at over $14B USD/year), minimize energy costs, and cut greenhouse gas emissions (GHGs). However, the capital costs to replace this aging infrastructure, estimated by the American Water Works Association in 2019 to over $472B USD in the USA by 2039, is cost-prohibitive - triggering a need for innovative solutions to address these significant issues. Due to the latest developments in wireless sensors, many DWS are now collecting high-frequency streams of key variables (e.g., parcel level consumer water demand) at varying spatial and temporal scales, resulting in Big Data and prompting the conversion of traditional DWS to smart water networks (SWANs) that enable the mitigation of sub-optimal DWS operations (e.g., by minimizing energy usage). SWANs rely on expert systems based on data-driven models (e.g., machine learning) to identify optimal operational decisions that meet the goals of DWS managers, who struggle to sustainably operate DWS in the face of numerous uncertainties (e.g., changing water demand). However, SWANs are still in their infancy and there are no expert systems that can handle Big Data and account for uncertainty in DWS in a computationally efficient manner. My long-term vision is to optimize DWS through the use of SWANs. The next 5 years of my research program will focus on the most immediate needs to realize an expert system to address these important challenges. The expert system will be built sequentially through three short-term objectives: 1) novel state-of-the-art data-driven models will be explored for pre-processing and making accurate forecasts from Big Data associated with SWANs (e.g., parcel level consumer water demands); 2) these models will be incorporated in a novel stochastic framework to account for several important uncertainty sources (e.g., model structure) and temporal correlations to improve the reliability of the expert system; and 3) the previous two stages will be coupled with reinforcement learning for optimizing SWANs (with a focus on pump schedule optimization) according to key operational goals (minimizing NRW, energy costs, and/or GHGs). To demonstrate their superiority, the novel developments from each objective will be rigorously compared against current state-of-the-art methods and benchmark approaches adopted by water utilities. The proposed research will advance new knowledge on optimizing SWANs and provide a novel expert system for water utilities to address pressing challenges, with the potential to save utilities $4.6B USD/year in operation costs. Through my program, 8 student researchers will gain the skills necessary to make significant impacts at water utilities (e.g., City of Ottawa) and private firms invested in SWANs (e.g., Innovyze), contributing to the sustainable management of water resources and placing Canada at the forefront of research in SWANs.