Show simple item record

dc.contributor.authorLu, Junhao 16:32:48 (GMT) 16:32:48 (GMT)
dc.description.abstractForecasting water demand requires quantifying potential relationships between relevant statistics and ambient conditions such as water price and weather. Dr Enouy (2018) demonstrates that discrete histograms can be parameterized into continuous probability density functions. Consistent parametrization allows regression analysis to be applied to the PDF statistics, thus able to reproduce PDFs through time. This work briefly introduces Dr Enouy’s (2018) methodology and mainly investigates the applicability of this method. It formalizes the implementation details of residential water application in terms of data culling, optimization and regression analysis. A modified version of this method is employed as an adaptation to the analysis of commercial water demand. This thesis also discusses the possibility of employing the scheme of software development, to assure the robustness and correctness of this implementation.en
dc.publisherUniversity of Waterlooen
dc.subjectWater Demanden
dc.titleWater Demand Forecasting Model Applicationen
dc.typeMaster Thesisen
dc.pendingfalse and Environmental Sciencesen Sciencesen of Waterlooen
uws-etd.degreeMaster of Scienceen
uws.contributor.advisorUnger, Andre
uws.contributor.affiliation1Faculty of Scienceen

Files in this item


This item appears in the following Collection(s)

Show simple item record


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages