The value and challenges of micro-component domestic water consumption datasets Jo Parker
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Transcript of The value and challenges of micro-component domestic water consumption datasets Jo Parker
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)
Jo Parker
Study aim
• Examine the sensitivity of long-term water demand micro-components to climate variability and change.
What are micro-components?
Source: Ofwat
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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.
The ‘Golden 100’
Micro-components Socio-economic variables Meteorological variables Other variables
Bath Occupancy rate Minimum temperature (oC) Day of week
Shower Region Maximum temperature (oC) Month of year
Basin Billing type Rainfall (mm) Bank holiday
WC ACORN classification Sunshine (hours per day)
Kitchen sink Rateable value
Washing machine
Dishwasher
External tap
• More than 22million data points.• Too large to handle in excel.• 100 households.
The ‘Golden 100’
Error checking algorithm
1. Basic error checks.2. Remove large outliers percentile approach.3. Stratification.4. Second screening.5. Apply transformation.6. Regression analysis.
1. Basic error checks
• Remove gross errors.• Completeness checks.• Dummy variables.• Remove 0l/d PCC.
Sunday 0 0 0 0 0 0Monday 1 0 0 0 0 0Tuesday 0 1 0 0 0 0
Wednesday 0 0 1 0 0 0Thursday 0 0 0 1 0 0
Friday 0 0 0 0 1 0Saturday 0 0 0 0 0 1
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.
3. Stratification
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
• The Kolmogorov-Smirnov normality test.
• Box-Cox transformation.
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6. Regression – One approach doesn’t fit all
Metered households, East region, single occupancy.
Basin Bath
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Bath (non-zero)
Metered households, East region, single occupancy.
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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?
Variables modelledObserved data input (subpopulation)
Micro-components modelled
Explanatory variables used
Metered Bath Mean temperature (oC)
Unmetered Shower Temperature range (oC)
Basin Sunshine (hr)
WC Rainfall (mm)
Kitchen sink 7 day rainfall (mm)
Washing machineRegional soil moisture deficit
index (mm)
Dishwasher Day of week
External tap Month of year
Year
Bank holiday
Occupancy rate
ACORN category
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.
• 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.
Metered • Relatively insensitive to
Mean T• Understand peak users
(2 uses per day)?
Unmetered• Slight negative
correlation with Mean T
Metered households
Unmetered households
Shower water usage vs. Daily Mean Temp.
• If we look at peak cluster positive correlation with Mean T.
External water usage vs. Mean Temp.
• Non-linear sensitivity to Mean T
• Where is the tipping point?
Metered households Unmetered households
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Thank you