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MS_THESIS_DATA_MAHMUD


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Created: Oct 05, 2020 at 12:31 a.m.
Last updated: Feb 24, 2021 at 9:01 p.m.
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Abstract

In recent years, most of the studies used the disaggregated smart meter data for demand modeling and identifying factors affecting residential water end-uses. Almost all of these studies tried to fit the data to known probability distributions while acknowledging that such distribution may not be applicable for a different dataset as water end-use varies with region, season, demography, climate, culture, etc. Few studies tried to utilize the high-frequency disaggregated end-use data as a feedback tool with the assumption that providing just the water-use data may help the users change their behavior. But behavioral transformation theories state that understanding users' intention towards environment, building conservation attitude, showing peers' conservation behaviors, and regular communication between the users and the water managers are essential factors for behavioral change which act in combination with the self-observation data supplied through the feedbacks (Ajzen 1985, Bandura 1991, Thibodeau et al., 1992, De Young, 1993). To fill the gap of prior studies, this research proposes to use a three-step approach for conservation practices. Steps include developing a set of baseline questions for understanding users' perception of conservation and environment, followed by segmentation of water-users based on users' separate and combined technological and behavioral potential by ranking water-use, technical efficiency (flowrate), and behavior (intensity and duration of use), and finally developing customized messages to encourage high water users to conserve by reviewing leading behavioral transformation theories.

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readme.txt

This repository is a part of my MS thesis. This is an ongoing research which focuses on utilizing high frequency water end use data for encouraging the households
to conserve water by employing different behavioral transformation methods. The strategey which was applied is called "Social Marketing" that creates differet user group
based on users' attitiude towards environmental wellbeing, intention towards water conservation, and conservation potential. The attitudinal part is analyzed by examining the survey 
responses and the conservation potential is determined by examining the water end-use events. Insead of fitting the data to known distributions, the water end-use data are ranked based
on daily volumetric use and Lorenz Curves and Gini Coefficient was used to identify the eccentricity of the behaviors to calculate the water saving potential. For more information,
check "Proposal_February_2021_mahmud_3.docx" whcih is currently under review.


The repository contains several scripts written in R and data files in MS Access and Excel format.
Short description of the scripts and the data files are provided herewith.

i. Data files:
   
   a. WRF - Residential End Uses of Water 2016-main.accdb
   This data file contains the traced water use data. The project "Residential End Uses of Water, Version-2, 2016" (REU-2016) was implimented by Water Research Foundation from 
   2011 to 2013 and the data was published in 2016 (https://www.waterrf.org/research/projects/residential-end-uses-water-version-2).
   Two tables from this data file was used:
	a1. "REU2016_Main_Meter_Events"
		Has all the main meter data which was recorded at a 10-second pulse for 762 households.
		Each household was traced for 10-14 days.
		The interpretation of the column headers are as follows;
		1.KEYCODE = Household Identifier Number, each KEYCODE refers to a specific household
		2.SumAs = The disaggregated algorithm put the name of the appliance (or use) in this column, e.g., Leak, Toilet, Faucet, etc.
		3.CounAs = Same as SumAs
		4.StartTime = the time and date of the event (listed as character, contains date and time)
		5.Duration = Duration of each event (in seconds)
		6.Peak = Peak flowrate (gallons per minute)
		7.Volume = Volume used during that logged event (in gallon)
		8.Mode = The flowrate which was maintained for the most of the time during an end-use event.
		
	a2. REU2016_Survey-End Use Sample
		Has the survey data comprising the number of appliances at each households, household size (number of adult, teen, etc.), employment record,
		age of the house, size of the lawn, outdoor water use activities, responses related to questions focusion on past conservation actions and 
		attitude towards water conservation, etc. Details of the survey questions can be found in "factors.xlsx" which is also uploaded in this repository.
	
   b. Population, Carbon Emission (kT), and income data of different countries 
	   The data was downloaded from the world bank website (https://data.worldbank.org) for illustrating the use of Lorenz curves and Gini Index.

ii. Scripts:
	
	The scripts are written in R. I have used RStudio Version 1.3.1073.
	Althougth most of the libraries used in the scripts for calculation and illustration can run on both 32 and 64-bit systems, I had difficulty running some libraries
	such as the "RODBC" in 64-bit system for some reason. I have included instructions where such complications occurred.
	Please follow the chronology when runnig the scripts.
	
	1. Thesis_Analysis_of_inequality_REUS2016_V03_OthersExcluded.Rmd
		A rmarkdown file which uses the "REU2016_Main_Meter_Events" and the "REU2016_Survey-End Use Sample" table from "WRF - Residential End Uses of Water 2016-main.accdb" and
		the carbon emission, population and the income data.
		The script can
			- convert the 10-second trace data to daily values and compute parameters such as gallons/household/day, gallons/ person/ household/ day.
			- convert the 10-second trace data to daily end-use (appliance) wise parameters such as gallons/ household/ day, gallons/ person/ household/ day,
			  events/ day, events/ person/ day, minutes/ day, minutes/ person/ day, minutes/ event, flow rate (gallons/ minute), volume per event (gallons/ event) etc.
			- create appliance wise Lorenz curves and compute the Gini Coefficient for all the parameters listed above.
			- Can identify the households with water saving potentials and the potential volume of saved-water by using the EPA and Federal Guideline, 
			  and given threshold behavioral value.
		This script is currently being updated.
	
	2. Calculation_for_Fixing_the_threshold_behavioral_value_for_major_indoor_appliances.Rmd
		This script must be run after Thesis_Analysis_of_inequality_REUS2016_V03_OthersExcluded.Rmd.
		This script uses the disaggregated daily end-use data created in Thesis_Analysis_of_inequality_REUS2016_V03_OthersExcluded.Rmd to
		identify the proper behavioral threshold value for each major indoor water appliance.
		This script is currently being updated and future versions will contain a smaller function.
		For the time being all the calculations are done separately for each appliance.
	
	3. Cumulative_WaterSaved_Plot.R
	   This script must be run after Thesis_Analysis_of_inequality_REUS2016_V03_OthersExcluded.Rmd and 
	   Calculation_for_Fixing_the_threshold_behavioral_value_for_major_indoor_appliances.Rmd scripts This script calculates appliance wise volume conserved 
	   by adopting different behavioral and technological conservation actions mentioned in the above mentioned scripts, and creates a cumulative bar-chart by 
	   ranking the users from high to low users while illustrating the potential volume that can be conserved by adopting a technological or behavioral conservation action.
	   Every color in the plot represents an appliance. The darker shed of a color represents the cumulative volume conserved if the behaviors are changed.
	   The lighter shed of a color represents the cumulative volume conserved inf the appliance is retrofitted with an efficient one.
	
	4. OpportunityWise_WaterSaved_Plot.R
		This script must be run after Thesis_Analysis_of_inequality_REUS2016_V03_OthersExcluded.Rmd and
		Calculation_for_Fixing_the_threshold_behavioral_value_for_major_indoor_appliances.Rmd scripts
		This script calculates appliance wise volume conserved by adopting different behavioral and technological conservation actions mentioned
		in the above mentioned scripts, and creates a bar-charts by ranking the users from high to low users based on the number of opportunities
		while illustrating the potential volume that can be conserved by adopting a technological or behavioral conservation action.
		Every color in the plot represents an appliance
		The darker shed of a color represents the cumulative volume conserved if the behaviors are changed
		The lighter shed of a color represents the cumulative volume conserved inf the appliance is retrofitted with an efficient one.
		
	

Related Resources

The content of this resource is derived from Residential End Uses of Water- Version 2, 2016
The content of this resource is derived from World Bank Population, Income and Carbon Emission Data

Credits

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
David E Rosenberg Utah State University

How to Cite

Aveek, M. R. (2021). MS_THESIS_DATA_MAHMUD, HydroShare, http://www.hydroshare.org/resource/eb77a88a5bfa4608a361e06b04fcf2ae

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

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

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