Characterizing Water and Water-Related Energy in Multi-Unit Residential Structures with High Resolution Smart Meter Data


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Created: Jul 16, 2020 at 2:26 a.m.
Last updated: Mar 02, 2021 at 9:36 p.m.
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Abstract

As global populations continue to increase and become more urbanized, relationships between water and energy are becoming more important. Both are limited in supply, but both are required to satisfy the needs of residential water users. In the context of urbanization and residential water use, domestic hot water (DHW), which is a resource consumed in nearly every residential structure in the developed world, represents one of the most significant water-related uses of energy. However, quantifying hot water use and the energy associated with heating it can be difficult. Water and energy use are typically evaluated separately, and paired datasets that enable direct evaluation of hot water use and its associated energy consumption are rare. Yet, quantifying water and water-related energy use are important in better understanding how they are linked and in identifying opportunities for conservation. We collected high resolution water use and water temperature data within five multi-unit residential structures on a college campus and then developed a water and energy budget model for quantifying water and water-related energy consumption within each building. Results showed varying behavioral consumption patterns across the buildings. Results also showed tradeoffs between data volume and ability to quantify use associated with sampling and data recording frequency. This resource is the result of an effort to establish reproducibility of the methods undertaken to quantify and characterize water and water-related energy with high-resolution smart meter data in multi-unit residential structures. This undertaking was a part of the research obligations associated with a Masters thesis completed at Utah State University in Aug 2020.

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Utah State University Living Learning Community
Longitude
-111.8151°
Latitude
41.7424°

Temporal

Start Date:
End Date:

Content

readme.md

README

This resource contains the data required to reproduce the work included within Chapter 3 of the following MS thesis:

Brewer, J.C. (2020). Characterizing Water and Water-Related Energy Use in Multi-Unit Residential Structures with High Resolution Smart Metering Data, All Graduate Theses and Dissertations, 7976, https://digitalcommons.usu.edu/etd/7976

The following files are included:

  • raw_data.csv - This file contains the raw water use and water temperature data that was collected in the Living Learning Community residential buildings on Utah State University's campus. This file contains the following data columns:
    • time: The time stamp associated with each observation in the following format yyyy-mm-dd hh:mm:ss
    • buildingID: The identifier for the LLC building in which the data were collected. Buildings are labeled A - F.
    • coldInfFlowRate: The flowrate of the cold water supply to the building in gal/min
    • coldInTemp: The temperature of the cold water supply to the building in degrees C.
    • hotInFlowRate: The flowrate of the hot water supply to the building in gal/min
    • hotInTemp: The temperature of the hot water supply to the building in degrees C.
    • hotOutFlowRate: The flowrate of the hot water return from the building in gal/min
    • hotOutTemp: The temperature of the hot water return from the building in degrees C.
  • hydroshare_loadData.py - This Python file provides code required to load the raw data CSV file into an InfluxDB database.
  • hydroshare_QC.py - This Python file contains the code required to perform quality control on the raw data. It can be executed once the raw data have been loaded into an InfluxDB database.
  • hydroshare_tempCalibration.py - This Python file contains a function that returns temperature calibration factors required by the hydroshare_QC.py script.
  • hydroshare_waterEnergyBalance.py - This Python file calculates water and water-related energy use from the quality controlled high-resolution data. Justifications for the assumptions and derived equations are detailed in the related text (Brewer, 2020).
  • hydroshare_viz.py - This Python file generates the visualizations of water and water-related energy use data after it has been loaded into an InfluxDB database and QC has been performed.

The Python scripts in this resource have the following Python dependencies:

  • Python 3.X: The scripts were written for Python 3
  • Pandas: Version 1.2.2. A fast, powerful, flexible and easy to use open source data analysis and manipulation tool.
  • matplotlib: Version 3.3.4. A comprehensive library for creating static, animated, and interactive visualizations in Python.
  • influxdb: Version 5.3.1. A client for interacting with InfluxDB.

Instructions for Reproducing Results

Given the size of the high resolution water use data, we used InfluxDB to organize and query the data. The included code files assume that the data has been loaded into an InfluxDB database. To reproduce the results shown in Chapter 3 of the above cited thesis, do the following in order:

  1. Download this HydroShare resource. Transfer the "contents" folder of the resource to a machine on which you have created a Python environment that satisfies the above dependencies.
  2. Install InfluxDB on a machine using the instructions available at https://portal.influxdata.com/downloads/. We used InfluxDB version 1.8.4 for our testing.
  3. Run the hydroshare_loadData.py script to create an InfluxDB database and load the data from raw_data.csv. You will have to supply the hostname and port for accessing the InfluxDB. If you enable authentication in InfluxDB (it's turned off by default) you will also have to supply a username, and password for your InfluxDB connection within this script before you run it.
  4. Run the hydroshare_QC.py script on your new InfluxDB database. This script will perform all of the quality control steps on the raw data. You will have to supply the connection information your InfluxDB database at the top of the script.
  5. Run hydroshare_waterEnergyBalance.py script, which runs the water and energy balance model for each of the buildings. You will have to supply the connection information for your InfluxDB at the top of the script.
  6. Run the hydroshare_viz.py script to generate the visualizations of water and water-related energy use. You will have to supply the connection information for your InfluxDB at the top of the script.

References

Related Resources

The content of this resource serves as the data for: Brewer, J.C. (2020). Characterizing Water and Water-Related Energy Use in Multi-Unit Residential Structures with High Resolution Smart Metering Data, All Graduate Theses and Dissertations, 7976, https://digitalcommons.usu.edu/etd/7976

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Cyberinfrastructure for Intelligent Water Supply (CIWS): Shrinking Big Data for Sustainable Urban Water 1552444
Utah Water Research Laboratory

How to Cite

Brewer, J., J. S. Horsburgh (2021). Characterizing Water and Water-Related Energy in Multi-Unit Residential Structures with High Resolution Smart Meter Data, HydroShare, http://www.hydroshare.org/resource/b6bbdcd9b120430b9a54974a798961f1

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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