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Supporting data and tools for "Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge"
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|Storage:||The size of this resource is 13.5 MB|
|Created:||Jun 28, 2021 at 11:01 p.m.|
|Last updated:|| Aug 04, 2021 at 7:23 p.m.
|Citation:||See how to cite this resource|
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The files provided here are the supporting data and code files for the analyses presented in "Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information ath the Edge," an article submitted to the Sensors journal. The data included in this resource were collected in a field deployment of the Cyberinfrastructure for Intelligent Water Supply (CIWS) Computational Node, an open source, low cost device capable of collecting, processing, and transferring high temporal resolution data on magnetically driven water meters. The code included allows replication of the findings presented in Sections 4.3 and 4.4 of the article, and the raw and processed data included allow for extension of the analyses conducted.
This HydroShare resource is organized as follows:
Node_Data Folder: contains the raw data recorded by the Node along with the disaggregated water end use events that were derived automatically from the raw data using the computational capabilities of the Node and the CIWS Disaggregator algorithm. The naming convention for raw data collected at 4 second resolution files is siteID_YYYYMMDDTHHmmss.csv. The naming convention for disaggregated water end use event files is siteID_YYYYMMDDTHHmmss-DISAGGREGATED.csv.
Water_Use.ipynb Python Jupyter Notebook file: The Python Jupyter Notebook used to generate water use constitution per end use type presented in Sections 4.3 and 4.4 of the article.
Accuracy_Testing.csv: contains the manually labeled events used to quantify the classification accuracy of the device against the output from the computational node.
Instructions for Reproducing Results
To reproduce the results presented in Sections 4.3 and 4.4 of the manuscript, perform the following steps:
- Download the complete resource.
- Leave the files together in the folders to ensure the paths to the files remain correct.
- Open the Water_Use.ipynb Jupyter Notebook and execute it.
The Output of the script should match the results presented in Sections 4.3 and 4.4 in the paper.
The code provided in this resource was developed using Python 3.7.3. The following Python packages are required for running the provided Jupyter Notebook:
- pandas - Version 1.2.3.
- glob - Version 7.1.7.
- numpy - Version 1.20.3.
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||CAREER: Cyberinfrastructure for Intelligent Water Supply (CIWS): Shrinking Big Data for Sustainable Urban Water||CBET 1552444|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/