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

利用大数据开发智能水网优化运行专家系统

• 批准号: RGPIN-2021-03194
• 负责人: Quilty, John
• 金额: $ 1.89万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742429
• 关键词: Leveraging; Big; Data; develop; expert

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.

全世界衰老的饮用水系统（DWS）面临减少非收入水的压力（NRW，估计每年超过14B美元），最小化能源成本和减少温室气体排放（GHGS）。但是，替代这项老化基础设施的资本成本是由2019年美国水工厂协会估计到2039年在美国的472B美元以上的资本成本，这是成本良好的 - 触发了创新解决方案以解决这些重大问题的需求。由于无线传感器的最新发展，许多DW现在正在在不同的空间和时间尺度上收集高频流的关键变量（例如，包裹水平的消费者水需求），从而获得了大数据，并促使传统DWS转换为智能水网络（SWAN）（SWAN），从而通过智能水网（SWAN）进行了缓解的能源，从而促进了Sub-opptimal d.gs decemaim decemage nim decemime dec。天鹅依靠基于数据驱动模型（例如机器学习）的专家系统来确定满足DWS经理目标的最佳操作决策，他们在面对许多不确定性（例如，供水需求变化）的情况下努力可持续运营DWS。但是，天鹅仍处于起步阶段，没有专家系统可以以计算有效的方式处理大数据并说明DWS的不确定性。我的长期愿景是通过使用天鹅来优化DW。我的研究计划的接下来的5年将重点介绍实现专家系统以应对这些重要挑战的最直接需求。专家系统将通过三个短期目标依次构建：1）将探索新的最新数据驱动模型，以预处理并从与Swans相关的大数据（例如，包裹水平的消费者用水需求）中进行准确的预测； 2）这些模型将纳入新的随机框架中，以说明几种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）根据关键的运营目标（最小化NRW，能源成本和/或温室气体），前两个阶段将结合使用加固学习以优化天鹅（重点介绍泵计划优化）。为了证明它们的优势，将严格地将每个目标的新发展与当前的最新方法和基准方法进行比较。拟议的研究将促进有关优化天鹅的新知识，并为水公用事业公司提供新颖的专家系统，以应对紧迫的挑战，并有可能节省公用事业的运营成本$ 4.6B美元。通过我的计划，8位学生研究人员将获得必要的技能，以对水电（例如渥太华市）和投资天鹅（例如Innovyze）投资的私人公司产生重大影响，这为水资源的可持续管理和将加拿大放置在天鹅的研究最前沿做出了贡献。