Short‐Term Forecasting Analysis for Municipal Water Demand

Short-term water demand forecasts inform decisions regarding budgeting, rate design, water supply system operations, and effective implementation of conservation policies. This study develops a Linear Transfer Function (LTF) forecasting model for El Paso, Texas, a growing city located in the desert Southwest region of the United States. The model is used to generate monthly-frequency out-of-sample simulations of water demand for periods when actual demand is known. To measure the accuracy of the LTF projections against viable alternatives, a set of benchmark forecasts is also developed. Both descriptive accuracy metrics and formal statistical tests are used to analyze predictive performance. The LTF model outperforms the alternatives in predicting demand per customer but falls a little short in projecting growth in the customer base. Changes in climatic and economic conditions are found to impact consumption per customer more rapidly than changes in water rates.

[1]  W. Enders.  Applied Econometric Time Series , 1994 .

[2]  S. Johansen.  Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models , 1991 .

[3]  H. Theil,et al.  Economic Forecasts and Policy. , 1959 .

[4]  Mohammed Lu’ay Jamal Froukh,et al.  Decision-Support System for Domestic Water Demand Forecasting and Management , 2001 .

[5]  Alexandros K. Mentes,et al.  Modeling Residential Water Demand Using Household Data: A Cubic Approach , 2004 .

[6]  Bruce R. Beattie,et al.  On the Specification of Price in Studies of Consumer Demand under Block Price Scheduling , 1981 .

[7]  Econometric Modeling: A Likelihood Approach , 2007 .

[8]  Short‐term water dynamics in Chihuahua City, Mexico , 2003 .

[9]  Judith A. Clarke,et al.  A comparison of some common methods for detecting Granger noncausality , 2006 .

[10]  David R. Maidment,et al.  Transfer Function Models of Daily Urban Water Use , 1985 .

[11]  M. Friedman.  The Interpolation of Time Series by Related Series , 1962 .

[12]  T. Cargill,et al.  A vector autoregression model of the Nevada economy , 1988 .

[13]  Kostas Bithas,et al.  Estimating Urban Residential Water Demand Determinants and Forecasting Water Demand for Athens Metropolitan Area, 2000-2010 , 2006 .

[14]  Michael Nieswiadomy,et al.  Estimating urban residential water demand: Effects of price structure, conservation, and education , 1992 .

[15]  William O. Maddaus,et al.  Forecasting Wholesale Demand and Conservation Savings , 2006 .

[16]  D. Rubinfeld,et al.  Econometric models and economic forecasts , 2002 .

[17]  Koichiro Ito,et al.  Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing , 2012 .

[18]  Fernando Arbués,et al.  Estimation of residential water demand: a state-of-the-art review , 2003 .

[19]  M. Nieswiadomy,et al.  A Note on Price Perception in Water Demand Models , 1991 .

[20]  E. M. Pint.  Household Responses to Increased Water Rates during the California Drought , 1999 .

[21]  Inmaculada Villanúa,et al.  Price impact on urban residential water demand: A dynamic panel data approach , 2004 .

[22]  Céline Nauges,et al.  Privately-Operated Water Utilities, Municipal Price Negotiation, and Estimation of Residential Water Demand: The case of France , 2000 .

[23]  J. McGuckin,et al.  NONPRICE WATER CONSERVATION PROGRAMS AS A DEMAND MANAGEMENT TOOL 1 , 1999 .

[24]  James G. MacKinnon,et al.  Testing for Consistency using Artificial Regressions , 1989, Econometric Theory.

[25]  M. Renwick,et al.  Demand Side Management Policies for Residential Water Use: Who Bears the Conservation Burden? , 1998 .

[26]  D. Agthe,et al.  Equity, Price Elasticity, and Household Income Under Increasing Block Rates for Water , 1987 .

[27]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[28]  Lon-Mu Liu,et al.  Forecasting residential consumption of natural gas using monthly and quarterly time series , 1991 .

[29]  Patricia A. Tennyson,et al.  El Paso's Citizens Embrace the Value of Water and Reap Big Rewards , 2007 .

[30]  R. Cumby,et al.  Testing for market timing ability: A framework for forecast evaluation , 1987 .

[31]  Stuart White,et al.  Urban water demand forecasting and demand management: Research needs review and recommendations , 2003 .

[32]  Maximilian Auffhammer,et al.  The Impact of State Level Building Codes on Residential Electricity Consumption , 2011 .

[33]  Céline Nauges,et al.  Is all domestic water consumption sensitive to price control? , 2004 .

[34]  R. Bruce Billings,et al.  A Simultaneous Equation Demand Model for Block Rates , 1986 .

[35]  T. Fullerton,et al.  An Empirical Analysis of Tijuana Water Consumption , 2007 .

[36]  Shouke Wei,et al.  Modeling and simulation of industrial water demand of Beijing municipality in China , 2010 .

[37]  Jeong-Shik Shin,et al.  Perception of Price When Price Information Is Costly: Evidence from Residential Electricity Demand , 1985 .

[38]  Roberto Tinajero,et al.  Short-Term Water Consumption Patterns in Ciudad Juárez, Mexico , 2006 .

[39]  A. Walke,et al.  An empirical analysis of Halifax municipal water consumption , 2012 .

[40]  M. Asce,et al.  Forecasting For Urban Water Demand Management , 1999 .

[41]  Richard Schmalensee,et al.  Advertising and aggregate consumption: an analysis of causality , 1980 .

[42]  C. Sims.  MACROECONOMICS AND REALITY , 1977 .

[43]  Thomas M. Fullerton,et al.  Short‐term water consumption dynamics in El Paso, Texas , 2004 .

[44]  Regional econometric assessment of aggregate water consumption trends , 2004 .

[45]  Terrence Kinal,et al.  A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy , 1986 .

[46]  Richard Ashley,et al.  To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models , 2009, Int. J. Data Anal. Tech. Strateg..

[47]  Helmut Lütkepohl,et al.  Applied Time Series Econometrics , 2004 .

[48]  Inmaculada Villanúa,et al.  Potential for Pricing Policies in Water Resource Management: Estimation of Urban Residential Water Demand in Zaragoza, Spain , 2006 .

[49]  Peter Nijkamp,et al.  Price and Income Elasticities of Residential Water Demand: A Meta-Analysis , 2003, Land Economics.

[50]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[51]  Robert C. Merton,et al.  On Market Timing and Investment Performance Part II: Statistical Procedures for Evaluating Forecasting Skills , 2015 .

[52]  Philip H. Carver,et al.  Short‐ and long‐run effects of price on municipal water use , 1980 .

[53]  David H. Moreau,et al.  Estimating the Effects of Conservation on Demand During Droughts , 1991 .

[54]  The demand for urban water by customer class , 1986 .

[55]  T. Fullerton.  Specification of a Borderplex Econometric Forecasting Model , 2001 .

[56]  William W. S. Wei,et al.  Time series analysis - univariate and multivariate methods , 1989 .

[57]  Roque B Fernandez,et al.  A Methodological Note on the Estimation of Time Series , 1981 .

[58]  Martin Williams.  Estimating urban residential demand for water under alternative price measures , 1985 .

[59]  Jonas Schmitt,et al.  Likelihood Based Inference In Cointegrated Vector Autoregressive Models , 2016 .

[60]  J. Moncur.  Urban water pricing and drought management , 1987 .