A new data-driven model for urban water demand forecasting

城市用水需求预测的新数据驱动模型

• 批准号: 488921-2015
• 负责人: Chebana, Fateh
• 金额: $ 1.82万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=586418
• 关键词: new; data; driven; model; urban

Despite the relative abundance of water in Canada, water suppliers are interested in managing water demand than ever before. In this sense, our partner, Econics, works with local governments, water utilities and municipalities. Efforts in the Canadian urban water (UW) supply sector move towards more integrated and demand side management approaches to control and expand their water supply systems (WSS). It is recognized the need for new tools that can be used to more effectively and sustainably plan and manage UW supply systems. One such tool is state of art and highly accurate, precise and reliable UW demand (UWD) forecasting models. They provide support for water resources and utility managers to adapt to changes in the short-or long-term UWD forecasts. Data-driven models represent the main type of such modeling. During the last two decades, several of such statistical models have been examined for UWD forecasting mainly for short-term. However, data-driven models have not been examined for UWD long-term forecasting, especially under different climate changes scenarios (CCS). The latter should be examined and their impact on UWD should be evaluated. The main goal of the project is to develop data-driven models for long-term UWD forecasting, while considering long-term CCS and their expected impacts on UWD for a given municipality. To date, no studies have examined the performance of ANN models for long-term UWD forecasting under CCS. Despite the good performance of ANN models, they have limitations, particularly with non-stationary data. Several studies have shown promising performance outcomes when combined with Wavelets (W-ANN) as well as the use of ensemble ANNs (ENN) where ENNs are more robust, consistent and reliable models. The main goal of this research project is to develop W-ENN models for the long-term UWD forecasting under different CCS. The new, highly accurate and reliable UWD long-term forecasting models will provide Econics, and in turn their clients with very useful models that will allow the most accurate, precise and reliable UWD long-term forecasting and will consequently help in effectively and sustainably plan and manage UW supply system.

尽管加拿大的水资源相对丰富，但供水商对管理水需求很感兴趣 比以往任何时候都多从这个意义上说，我们的合作伙伴Econics与当地政府、水务公司和 市.加拿大城市供水部门的努力朝着更加综合和 需求侧管理方法，以控制和扩大其供水系统（WSS）。应认识 需要新的工具，可用于更有效和可持续地规划和管理UW供应 系统.其中一个工具是最先进的、高度准确、精确和可靠的UW需求（UWD）预测 模型它们为水资源和公用事业管理人员提供支持，以适应短期或长期的变化。 长期UWD预测。数据驱动模型代表了这种建模的主要类型。在过去两 几十年来，几个这样的统计模型已被检查为UWD预测主要是短期的。 然而，数据驱动的模型尚未被用于UWD长期预测，特别是在 不同的气候变化情景。应审查后者，并应考虑其对UWD的影响 评估。该项目的主要目标是开发用于长期UWD预测的数据驱动模型， 考虑长期CCS及其对特定城市UWD的预期影响。迄今为止，没有研究 研究了CCS下长期UWD预测的人工神经网络模型的性能。尽管有好 ANN模型的性能，他们有局限性，特别是与非平稳数据。几项研究 当与Wavelet（W-ANN）结合以及使用 集成ANN（ENN），其中恩斯是更鲁棒、一致和可靠的模型。这个的主要目标 研究项目是建立W-ENN模型，用于不同CCS下的UWD长期预测。的 新的，高度准确和可靠的UWD长期预测模型将提供经济学，反过来， 我们的客户拥有非常有用的模型，可以实现最准确，精确和可靠的UWD长期 预测，从而有助于有效和可持续地规划和管理UW供应系统。