TY - GEN
T1 - A Visual Analytics System for Water Distribution System Optimization
AU - Li, Yiran
AU - Musabandesu, Erin
AU - Fujiwara, Takanori
AU - Loge, Frank J.
AU - Ma, Kwan Liu
N1 - Funding Information:
This research is supported in part by the U.S. National Science Foundation through grant IIS-1741536. We acknowledge funding from the California Energy Commission Electric Program Investment Charge, agreement number EPC-16-062. We would also like to thank Moulton Niguel Water District for their partnership on this project and other research regarding the water-energy nexus. This document was prepared as a result of work sponsored by the California Energy Commission. It does not necessarily represent the views of the Energy Commission, its employees, or the State of California. The Energy Commission, the State of California, its employees, contractors, and subcontractors make no warranty, express or implied, and assume no legal liability for the information in this document; nor does any party represent that the use of this information will not infringe upon privately owned rights. This report has not been approved or disapproved by the Energy Commission nor has the Energy Commission passed upon the accuracy of the information in this report.
Funding Information:
This research is supported in part by the U.S. National Science Foundation through grant IIS-1741536. We acknowledge funding from the California Energy Commission Electric Program Investment Charge, agreement number EPC-16-062. We would also like to thank Moulton Niguel Water District for their partnership on this project and other research regarding the water-energy nexus.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system's effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.
AB - The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system's effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.
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U2 - 10.1109/VIS49827.2021.9623272
DO - 10.1109/VIS49827.2021.9623272
M3 - Conference contribution
AN - SCOPUS:85123749808
T3 - Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
SP - 126
EP - 130
BT - Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Visualization Conference, VIS 2021
Y2 - 24 October 2021 through 29 October 2021
ER -