DEL GUAYAS, A LOS VEINTE DÍAS DEL MES DE SEPTIEMBRE DEL AÑO DOS

In most cases, the median daily-average flow (QM) is less than QA due to large outlier

flow events that inflate the QA value. The least amount of difference between QM and QA is

observed for the multi-residential building, which indicates observed hourly water flowrates follow a normal distribution and should contain fewer outlier flow events. QM values for the

commercial building were often 0 for Saturdays when irrigation occurred as a short duration event. The elementary school and community center have the largest shift in QM values

compared to QA values due to the short-duration high-flowrate peaks observed for the

elementary school and numerous outlier events observed at the community center. For all sites, the trends in water use over the year in terms of QM follow the average monthly trend for

QA values.

The difference in QM and QA for each building location is also evident by the standard

deviation (σ) for each site. The multi-residential building has the highest daily-average standard deviation in absolute terms, but the smallest deviation when normalized to QA. The great

difference in QM and QA for the remaining sites is reiterated by the high σ values for the

commercial building, elementary school, and community center. Low σ values observed for weekends for the commercial and elementary school buildings are a result of prolonged periods of no flow during these time periods.

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Figure 5.12: Median hourly flow (QM) and standard deviation (σ) by day for each month for the

multi-residential (RES), commercial (COM), elementary school (ELM), and community center (CTR) sites.

5.6 Conclusion

The water management shift from strict supply-side provider to integrated operations regarding demand-side management has driven the need for the efficient collection and evaluation of high-resolution water data. The smart meters using AMR technology in this study have been shown to adequately collect, record, and disseminate water use data at an hourly timestep, which provides sufficient resolution to capture diurnal water use trends for unique building locations for a fair time duration that may capture seasonal trends. The resultant diurnal water use curves were exclusive to each building, and hourly-average curves contained expected features that aligned with diurnal curves from literature (e.g., two-peak residential curve and plateauing commercial curve). However, the hourly-average curves smoothed attributes of the daily water use curves, such as QP, FP/A, TP, TQ>QA, and NP. Separating diurnal

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time. Throughout the year the multi-residential building had the least variation while the multi- use functions of the community center resulted in the most variation among values. Seasonal water use was clearly evident at the elementary school, where water use fell during summer months when school is not in session for students. However, evaluating diurnal curves over time shows that water use is dynamic and individual to each study site.

Buildings were chosen as a representative of a building type, but do not aim to depict the usage profile of every building within its associated type. Each building exerts a unique water demand profile impacted by the building design, occupants, and climate. The variety of drivers of water use within each building supports the need for sub-metering in order to understand where water is consumed within the building and which end-uses are having the largest impact on overall building water use over time, especially during intense water use events. Knowledge about water end-use consumption will allow for more precise demand-side management strategies directed at high-use activities and efficient allocation of available water sources to meet specific demands. For example, pricing schemes that raise the appeal of using reclaimed water rather than potable water for irrigation may reduce the amount of potable water directed to a low-quality end-use while preserving the potable source to meet other high-quality end-uses. Understanding real building water demand profiles is necessary for supply-demand prioritization matching discussed in Chapter 4, and the real demand profiles will be used to evaluate resilience in Chapter 7.

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6 WATER RESILIENCE ASSESSMENT MODEL FRAMEWORK

6.1 Note to Reader

This chapter is based on the published article “Decision support modeling for net-zero water buildings” that appeared in the Proceedings of the 2014 Winter Simulation Conference, pages 3176-3187, IEEE Press (Joustra and Yeh, 2014). Permission is included in Appendix A.

6.2 Introduction

Previous sections have defined the need for decision support tools in water sector and the need for high-resolution data tracking prerequisite for demand-source matching. In order to evaluate resilience the fulfillment of building water functions must be understood; therefore, there is a need for a tool that can evaluate demand-source interactions which indicates the degree of function fulfillment. Sub-metered data not readily available, so the tool needs to be flexible enough to emulate different building water cycles based on information available and future information inputs. The objective of this chapter is to develop a building water cycle modeling framework that allows for flexible interactions among water demands and supplies. The framework is based on the algorithm presented in Chapter 4, but expands upon the algorithm with the inclusion of storage capacities that allow for delayed application of water sources to fulfill demands at the time required. The resultant model is referred to as the water resilience assessment model (WRAM) and will be used to evaluate the resilience of building water cycle scenarios in Chapter 7.

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