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Water consumption patterns as a basis for water demand modeling

机译：用水模式作为需求模型的基础

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摘要

Future water demand is a main consideration in water system management. Consequently, water demand models (WDMs) have evolved in past decades, identifying principal demand-generating factors and modeling their influence on water demand. Regional water systems serve consumers of various types (e.g., municipalities, farmers, industrial regions) and consumption patterns. Thus, one of the challenges in regional water demand modeling is the heterogeneity of the consumers served by the water system. When a high-resolution, regional WDM is desired, accounting for this heterogeneity becomes all the more important. This paper presents a novel approach to regional water demand modeling. The two-step approach includes aggregating the data set into groups of consumers having similar consumption characteristics, and developing a WDM for each homogeneous group. The development of WDMs is widely applied in the literature and thus, the focus of this paper is to discuss the first step of data aggregation. The research hypothesis is that water consumption records in their original or transformed form can provide a basis for aggregating the data set into groups of consumers with similar consumption characteristics. This paper presents a methodology for water consumption data clustering by comparing several data representation methods (termed Feature Vectors): monthly normalized average, monthly consumption coefficient of variation, a combination of the monthly average and monthly variation, and the autocorrelation coefficients of the consumption time series. Clustering using solely normalized monthly average provided homogeneous and distinct clusters with respect to monthly consumption, which succeed in capturing different consumer characteristics (water use, geographical location) that were not specified a-priori. Clustering using the monthly coefficient of variation provided different, yet homogeneous clusters, clustering consumers characterized by similar variation trends that were closely related to consumer water use type. The concatenation of these two Feature Vectors provided further insight into the relationship between consumption patterns and variability of consumers. An autocorrelation Feature Vector provided results that can form a basis for constructing a time-series model that is based on a group of resembling time series. The approaches presented here are steps toward utilizing the increasing amount of available water consumption data and data analysis techniques to facilitate the modeling of water demands in larger and heterogeneous regions with sufficient resolution.
机译：未来的用水需求是水系统管理中的主要考虑因素。因此，水需求模型（WDM）在过去的几十年中得到了发展，它确定了主要的需求产生因素并模拟了它们对水需求的影响。区域供水系统为各种类型的消费者（例如市政当局，农民，工业地区）和消费模式提供服务。因此，区域需水模型的挑战之一是供水系统所服务的消费者的异质性。当需要高分辨率的区域WDM时，考虑这种异质性就变得尤为重要。本文提出了一种新的区域需水模型。两步方法包括将数据集聚合到具有相似消费特征的消费者组中，并为每个同类组开发WDM。 WDM的发展在文献中得到了广泛的应用，因此，本文的重点是讨论数据聚合的第一步。研究假设是，原始或转换形式的耗水记录可以为将数据集汇总到具有相似耗水特征的一组消费者中提供基础。通过比较几种数据表示方法（称为特征向量），提出了一种耗水量数据聚类的方法：月归一化平均值，月耗量变异系数，月平均值和月度变异的组合以及耗水时间的自相关系数系列。使用仅归一化的月平均数进行的聚类提供了关于月度消费的同质且不同的聚类，这成功地捕获了未指定先验的不同消费者特征（用水，地理位置）。使用月度变异系数进行聚类可提供不同但均一的聚类，以具有与消费者用水类型密切相关的相似变化趋势为特征的消费者聚类。这两个特征向量的串联提供了对消费模式与消费者可变性之间关系的进一步了解。自相关特征向量提供的结果可为构建基于一组相似时间序列的时间序列模型奠定基础。此处介绍的方法是朝着利用越来越多的可用水消耗数据和数据分析技术迈进的步骤，以方便以足够的分辨率对较大且异质区域的用水需求进行建模。

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  • 来源
    《Water resources research》 |2015年第10期|8165-8181|共17页
  • 作者

    Avni Noa; Fishbain Barak; Shamir Uri;

  • 作者单位

    Technion Israel Inst Technol, Dept Environm Water & Agr Engn, Haifa, Israel|Technion Israel Inst Technol, Dept Environm Water & Agr Engn, Fac Civil & Environm Engn, Haifa, Israel;

    Technion Israel Inst Technol, Dept Environm Water & Agr Engn, Haifa, Israel|Technion Israel Inst Technol, Dept Environm Water & Agr Engn, Fac Civil & Environm Engn, Haifa, Israel;

    Technion Israel Inst Technol, Dept Environm Water & Agr Engn, Fac Civil & Environm Engn, Haifa, Israel;

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