Urban Water Demand Regression Modeling for California Water Suppliers

Urban water demand modeling with regression identifies explanatory factors of water use in cities. A generalized demand modeling approach was developed for over 400 urban water supply agencies in California. Using standardized data from self-reported sources for agencies across the state, a batch-processing approach was used to create standardized urban water demand models. The models were developed to test the validity of a simplified and generalized demand modeling approach using monthly available data. Semilog, multivariate regression models were developed for each urban water supply agency. Consumption from residential (single- and multi-family), commercial, industrial, and institutional water use were considered as outcome variables. Explanatory variables include indicator variables for months in a calendar year, periods of water conservation requirements during a 2011-16 severe drought, population, and water rates. The models were of reasonable fit, with adjusted R-squared values ranging from 0.6-0.99. Visual inspection revealed that the monthly models captured trends with reasonable accuracy. The time frame for models was 2013-18, a period with standardized available data through statewide reporting. The modeling approach has been subsequently further extended to incorporate additional climate variables (precipitation and evapotranspiration) for sector-specific models. The models are intended to understand explanatory factors of demand through a generalized modeling approach and not intended to be used for water supply operations without further refinement and testing. The approach can be adapted to many types of cities.本数据集聚焦城市需水建模研究，通过回归分析识别城市用水的解释性影响因子。研究针对加利福尼亚州400余家城市供水机构开发了通用化需水建模方法：采用批处理（batch-processing）流程，基于全州各机构自报的标准化数据构建标准化城市需水模型，旨在验证基于月度可用数据的简化通用化需水建模方法的有效性。针对每家城市供水机构，均构建了半对数多元回归模型，研究将住宅（单户及多户住宅）、商业、工业及公共机构的用水量作为因变量（outcome variables），解释变量（explanatory variables）涵盖日历年度各月份的指示变量（indicator variables）、2011-2016年严重干旱期间的节水要求时段、人口规模与水价。模型拟合效果良好，调整决定系数（adjusted R-squared）取值区间为0.6至0.99，目视检验结果表明月度模型能够以合理精度捕捉用水趋势。模型的时间跨度为2013-2018年，该时期内全州统一上报的数据均已完成标准化。后续该建模方法进一步得到扩展，纳入降水量与蒸散发量（evapotranspiration）等额外气候变量以构建分部门模型（sector-specific models）。本系列模型旨在通过通用化建模方法解析需水的影响因子，未经进一步优化与测试，不得直接用于供水调度运营，该建模方法可适配多数类型的城市场景。