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• W2892061058 abstract "Rapid population growth over the 20th century and changing climate has put manyurban water supply systems under pressure around the world. Such pressure alsoexerted on most of the Australian water supply systems, which has led to theintroduction of water use restrictions to ensure environmentally sustainable watersupply. To operate cost effective and reliable urban water supply systems, analysingurban water use and forecasting future water demand is an essential task.Generally, the urban water use classified as residential and non-residential water usebased on different activities. In Melbourne (Australia), water authorities have usedend-use models to forecast water demand, in which the residential component isextensively modelled. In these end-use models, the total household water use is brokendown to the end-use level (e.g. toilets, showers, washing machines, etc.) for forecastingwater demand in the residential sector. However, a simple historical trend-based annualwater demand is considered for the non-residential sector, as a whole. No temporal (i.e.quarterly or monthly) and spatial disaggregation were considered in the non-residentialwater demand forecasts in these end-use models. It was also found that the existingwork around the world on water demand modelling mainly focused on residentialwater use modelling. However, a significant portion of urban water usage is nonresidential.For example, around 25% of the total water use in Melbourne is used bythe non-residential sector. Therefore, the modelling of non-residential urban water usehas significant importance for effective water supply system in any urban area.Considering this knowledge gap for effective urban water supply, this project aims toforecast short term (i.e. month to year) non-residential water demand which is usefulfor system operation as well as budgeting and financial management.To achieve this aim, the water use billing data for each non-residential customerlocated in the Yarra Valley Water service area (in Melbourne, Australia) were used fordeveloping non-residential water demand models in this research. All customers weredisaggregated into several groups based on the homogenous water activity such asSchools, Sports Grounds, Councils, Restaurants, Hospitals, Hotels, and Laundries. Thehigh water users (>50 ML/year) were also considered as a separate group in this studynamed as High Water Users. All customers in the homogenous groups were furtherdivided into smaller groups based on the annual water use (>20 ML, >15-20 ML, >10-15 ML, 5-10 ML, and <5 ML). Data analysis was then carried out for each of theseuser groups to identify the water use pattern. Data analysis showed that there weresome seasonal effects on Schools, Sports Grounds and Councils. Therefore, water useamong these groups was modelled using the Multiple Linear Regression (MLR)technique with the available climatic variable and water restrictions data. In theremaining groups no seasonal variations were identified during data analysis.Moreover, most of their water uses are for indoor purposes and therefore, water usemodelling was carried out for these remaining groups with the past water use data onlydue to unavailability of data for other influential factors. All forecasting modelsdeveloped in this research were validated with the observed data and the modelperformance was measured with the Nash-Sutcliffe efficiency criteria. Results showedthat most of these developed models performed well except for few cases. Some issuesand challenges were also identified during models development among thehomogenous groups in non-residential sectors. All these issues and challenges arelisted in this thesis for future research.The major innovation of this study was the development of the disaggregationapproach for sector based non-residential water demand modelling. This approach issuccessfully demonstrated in this research by disaggregating customers based on theiractivity and their annual water use. The development of non-residential water demandmodels at individual customer level is also the knowledge advancement, as limitedwork was found in this area." @default.
• W2892061058 created "2018-09-27" @default.
• W2892061058 creator A5073067061 @default.
• W2892061058 date "2018-01-01" @default.
• W2892061058 modified "2023-09-27" @default.
• W2892061058 title "Non-residential Urban Water Demand Modelling – a Disaggregation Approach" @default.
• W2892061058 cites W1988293539 @default.
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• W2892061058 hasPublicationYear "2018" @default.
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