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The value and challenges of micro-component domestic water consumption datasets Jo Parker. Working as part of the ESPRC - ARCC water project with the support of Anglian Water Services (AWS). Study aim.

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the value and challenges of micro component domestic water consumption datasets jo parker

The value and challenges of micro-component domestic water consumption datasetsJo Parker

Working as part of the ESPRC - ARCC water project with the support of Anglian Water Services (AWS)

study aim
Study aim
  • Examine the sensitivity of long-term water demand micro-components to climate variability and change.

Jo Parker

estimating forecasting household water demand
Estimating/forecasting household water demand?
  • Traditionally water into supply.
  • Complexity of household water demand.
  • Micro-component data provides us with the ability to investigate water use at the household scale.

Jo Parker

the golden 100
The ‘Golden 100’
  • More than 22million data points.
  • Too large to handle in excel.
  • 100 households.
error checking algorithm
Error checking algorithm
  • Basic error checks.
  • Remove large outliers  percentile approach.
  • Stratification.
  • Second screening.
  • Apply transformation.
  • Regression analysis.
1 basic error checks
1. Basic error checks
  • Remove gross errors.
  • Completeness checks.
  • Dummy variables.
  • Remove 0l/d PCC.
2 percentile approach
2. Percentile approach
  • Remove PCC outliers (0.05% threshold determined via sensitivity testing).
  • e.g., one rogue entry purported 98,020 litres/day for a single occupancy household.
4 second screening
4. Second Screening
  • User defined threshold.
  • e.g., secondary screening (250l/d threshold) removed values such as 131218l/d in bath usage for a 3 occupancy household.
  • Excluding external usage.
5 transformation
5. Transformation
  • The Kolmogorov-Smirnov normality test.
  • Box-Cox transformation.
6 regression one approach doesn t fit all
6. Regression – One approach doesn’t fit all

Basin

Bath

Metered households, East region, single occupancy.

Jo Parker

bath non zero
Bath (non-zero)

Metered households, East region, single occupancy.

Jo Parker

6 regression
6. Regression
  • Analyse the frequency of usage and non-usage (Logistic regression)
    • Is this weather, bank holiday, day of the week etc. sensitive?
  • Analyse the volume used (Multiple linear regression)
    • Is this weather, bank holiday, day of the week etc. sensitive?

Jo Parker

basin water usage vs daily mean temp
Basin water usage vs. Daily mean Temp.
  • Relatively insensitive to Mean T
  • What is causing striations?
  • Understand peak users (>40l/d)?
bath water usage vs daily mean temp
Bath water usage vs. Daily mean Temp.
  • Relatively insensitive to Mean T
  • What is causing striations between 20-60l/d?
  • Understand peak users (>80l/d)?
dishwasher water usage vs daily mean temp
Dishwasher water usage vs. Daily mean Temp.

Metered households

  • Metered
  • Relatively insensitive to Mean T
  • Understand peak users (2 uses per day)?
  • Unmetered
  • Slight negative correlation with Mean T

Unmetered households

shower water usage vs daily mean temp
Shower water usage vs. Daily Mean Temp.
  • If we look at peak cluster  positive correlation with Mean T.
external water usage vs mean temp
External water usage vs. Mean Temp.

Unmetered households

Metered households

  • Non-linear sensitivity to Mean T
  • Where is the tipping point?
slide22

Thank you

Jo Parker