Planning & Management

Oral

395268 - Statistical analysis and classification of non-residential water customers to improve parcel-level water demand modeling techniques

Monday, June 4
4:00 PM - 5:30 PM
Location: Greenway GH
Co-Authors: Michael Webber, Austin, TX – The University of Texas at Austin

This presentation details a novel procedure for analyzing water demands in the non-residential (NR) sector (i.e. commercial, industrial, and institutional water demands). Non-residential water demands are characterized by heterogeneity in customer types, water use patterns across time-scales, and end-use technologies deployed which strains the ability of traditional water demand forecasting techniques to provide accurate estimates of water demand at the parcel-level. This presentation presents a pre-classification scheme that categorizes NR customers into “subsectors” based on economic, land use, and property appraisal data and uses these subsectors to improve demand analysis techniques using the City of Austin, TX as a case study. NR customer subsectors are analyzed, and trends both between and within subsectors are presented using a multiple linear regression framework.

Results show that pre-classification of NR customers can improve the explanatory power of statistical models over models without any classification (r-squared = 0.52 and 0.19, respectively). Little improvement is seen by explicitly using the economic, land use and property data on which subsectors are based (r-squared = 0.57), though the latter is more computationally expensive and less likely to be used in practice. The classification scheme is implemented within a monthly mixed-effects model that analyzes monthly variability across customer types over four years, and response of monthly demands to various drivers. This analytical framework can be used by water utilities and municipalities that wish to better understand their NR water customer demands and greatly improve their forecasting tools for future water supply and infrastructure planning efforts.

Bruk Berhanu

Graduate Research Assistant
University of Texas at Austin

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