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Water consumed by power plants is transferred virtually from producers to consumers on the electric grid. This network of virtual transfers varies spatially and temporally on a sub-annual scale. In this study, we focused on cooling water consumed by thermoelectric power plants and water evaporated from hydropower reservoirs. We analyzed blue and grey virtual water flows between balancing authorities in the United States electric grid from 2016 to 2021. Transfers were calculated using thermoelectric water consumption volumes reported in Form EIA-923, power plant data from Form EIA-860, water consumption factors from literature, and electricity transfer data from Form EIA-930. The results indicate that virtual water transfers follow seasonal trends. Virtual blue water transfers are dominated by evaporation from hydropower reservoirs in high evaporation regions and peak around November. Virtual grey watertransfers reach a maximum peak during the summer months and a smaller peak during the winter. Notable virtual blue water transfers occur between Arizona and California as well as surrounding regions in the Southwest. Virtual grey water transfers are greatest in the Eastern United States where older, once-through cooling systems are still in operation. Understanding the spatial and temporal transfer of water resources has important policy, water management, and equity implications for understanding burden shifts between regions.

 

Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.

 

The residential sector accounts for a significant amount of water consumption in the United States. Understanding this water consumption behavior provides an opportunity for water savings, which are important for sustaining freshwater resources. In this study, we analyzed 1-second resolution smart water meter data from a 4-person household over one year as a demonstration. We disaggregated the data using derivative signals of the influent water flow rate at the water supply point of the home to identify start and end times of water events. k -means clustering, an unsupervised machine learning method, then categorized these water events based on information collected from the appliance/fixture end uses. The use of unsupervised learning reduces the training data requirements and lowers the barrier of implementation for the model. Using the water use profiles, we determined peak demand times and identified seasonal, weekly, and daily trends. These results provide insight into specific water conservation and efficiency opportunities within the household ( e.g. , reduced shower durations), including the reduction of water consumption during peak demand hours. The widespread implementation of this type of smart water metering and disaggregation system could improve water conservation and efficiency on a larger scale and reduce stress on local infrastructure systems and water resources.