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

世界各地老化的饮用水系统(DWS)面临着越来越大的压力，要求它们减少非收入用水(NRW，估计超过140亿美元/年)，最大限度地减少能源成本，并减少温室气体排放(GHGs)。然而，据美国水务协会2019年估计，到2039年，美国更换这一老化基础设施的资本成本将超过472亿美元，这一成本令人望而却步，引发了对创新解决方案的需求，以解决这些重大问题。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，地块级别的消费者用水需求)的高频流，从而产生大数据，并促使传统DWS向智能水网络(SWAN)的转换，从而能够缓解次优DWS操作(例如，通过最小化能源使用)。SWANS依靠基于数据驱动模型的专家系统(例如，机器学习)来确定满足DWS管理人员目标的最佳运营决策，他们在面对众多不确定性(例如，不断变化的水需求)时难以可持续地运营DWS。然而，天鹅仍处于初级阶段，还没有能够以计算高效的方式处理大数据并解决DWS中的不确定性的专家系统。我的长期愿景是通过使用Swans来优化DWS。在接下来的5年里，我的研究计划将把重点放在实现专家系统以应对这些重要挑战的最紧迫需求上。专家系统将通过三个短期目标顺序建立：1)将探索新的最先进的数据驱动模型，用于对与天鹅相关的大数据(例如，地块级别的消费者用水需求)进行预处理和准确预测；2)这些模型将被合并到一个新的随机框架中，以考虑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)前两个阶段将与强化学习相结合，以根据关键操作目标(最大限度地减少NRW、能源成本和/或温室气体)来优化天鹅(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自来水公司目前采用的最先进的方法和基准方法进行严格的比较。这项拟议的研究将推进有关优化天鹅的新知识，并为水务公司提供一种新颖的专家系统，以应对紧迫的挑战，有可能为公用事业公司每年节省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在水务公司(如渥太华)和投资于天鹅公司的私营公司(如Innovyze)产生重大影响所需的技能，为水资源的可持续管理做出贡献，并使加拿大处于天鹅研究的前沿。

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.