A Behavioural Approach to Stochastic End Use Modelling
Mark Thyer, Tom Micevski, George Kuczera
Matt Hardy, Hugh Duncan, Peter Coombes and Bill Pascoe
Outline
• Motivation• Model Overview and Results• Applications and Implications• Future Research
Urban Water Use is Changing
• Increase in efficient appliances/prices/restrictions• Increase in IUWM to reduce mains water demand• Requires a flexible approach that can adapt
(Beatty and O'Brien, 2008)
Integrated Urban Water Management
• Requires robust models of household end-water use at small temporal and spatial scales
Behavioural End-Use Stochastic Simulator (BESS)
Cluster/Regional Scale Water Use Dynamics – Spatial Variability
Household SizeDistribution
Drivers
DemographicsPolicyClimate
Appliance Type Distribution
Attitude to Water Use
Sub-daily Water Use
Appliance Type
Indoor Water Use Event Dynamics
Shower/BathWashing MachineToiletTap etc.
Household Size Dynamics
Household Composition/ Age Distribution
Outdoor Water Use Event Dynamics
Allotment Scale Water use Dynamics – Temporal Variability
Features:• Simulate multiple individual houses
(1-1000’s)– Differences in appliance type and
household size between houses
• Indoor water use time series– Shower, WM, Toilet, Tap etc.
(Duncan and Mitchell, 2008)
- Sub-daily time steps
• Outdoor water use time series– Probabilistic behavioural
response to rain and temp. (Micevski et al, 2011)Benefits:
• Flexible approach adapt to changes in future water use behaviour
• Utilise new datasets as they become available
• Model scenarios of predicted changes in future
BESS: Indoor Water Use
• Different appliances have different water use patterns and volumes
• Parameters based on Yarra Valley Water smart metering study of 100 homes (Roberts, et al, 2007)
- Stochastically simulates differences in house size and appliances between houses
Model Evaluation
WashingMachine
For each indoor water use event type simulated
matches observed daily totals
Obs Simulated-BAU AAA Shower AAA shower & 6A WM
010
020
030
040
0
Indo
or W
ater
use
Per
Cap
ita(L
/day
)
BESS: Outdoor Water Use• Probabilistically model daily outdoor water use
– Simulates individual households at daily time step – Behavioural approach to capture response of outdoor
water use occurrence and volume to weather– Based on concepts of Coombes et al (2001)
Daily time series of outdoor
water use
Daily Rainfall
Weather Drivers
Max. Temperatu
re
Will outdoor water be
used today ?
P (Watering)
Yes
No
How much water will be used today?
Vol (watering)
Monthly Avg. Outdoor Water
Use
Avg. Behaviour
Outdoor Water Use: Calibration Results
• Hunter Water Data set – Outdoor water use for 130 homes over 10 years
• Existing approaches– Underestimate observed variability (56%) – Over-parameterised => too many un-identifiable parameters
• Enhanced Behavioural Approach – Underestimates variability by only 8%– Parsimonous => parameters well-identified
0 5 10 15 20 25 30 35
010
2030
Mean (kL/mon), R2=0.98, grad=1
obs
sim
0 5 10 15 20 25 30
05
1015
Std Dev (kL/mon), R2=0.67, grad=0.44
obs
sim
0 20 40 60 80 100 140
020
4060
Max (kL/mon), R2=0.76, grad=0.57
obs
sim
0 10 20 30 40
010
30
Min (kL/mon), R2=0.67, grad=1.04
obs
sim
0 5 10 15 20 25 30 35
010
2030
Mean (kL/mon), R2=0.94, grad=1.06
obs
sim
0 5 10 15 20 25 30
010
2030
Std Dev (kL/mon), R2=0.78, grad=0.92
obs
sim
0 20 40 60 80 100 140
040
8012
0
Max (kL/mon), R2=0.82, grad=0.93
obs
sim
0 10 20 30 40
010
2030
Min (kL/mon), R2=0.73, grad=0.66
obs
sim
Existing Models BESS
20
kilometres
0 10
NELSON BAY
NEWCASTLE
CESSNOCK
MAITLAND
MARYLAND
NEW LAMBTON
MEREWETHER
STOCKTON
MAY FIELD
CHARLESTOWN
TORONTO
Insights on Drivers of Outdoor Water use
• Drivers of P(Watering)– For 80% of houses is increases in response to the long
dry periods (days with rainfall)– For 20% of houses increases response to long hot
periods (degree days)
• 30% of houses, delay watering after a significant rainfall/watering event
• Parameters are site specific and vary with climate• More research/data is needed to understand
variability in outdoor water use
• Final year engineering honours project• Little basis for setting of current rebate levels• Is a $10 rebate on showerhead, better than $200 rebate on washing
machine?• $100’s millions spent on rebate schemes in Australia in past decade
Applications: Optimising subsidies for reducing domestic water consumption
BESS + Urban Developer to estimate water savings
Previous rebate programs to forecast uptake
Multi-objective optimisation to identify Pareto optimal rebate policy
• Water Savings from BESS + Urban Developer
Applications: Optimising subsidies for reducing domestic water consumption
• Current estimates wide variation
• Water Savings from BESS + Urban Developer
• Current estimates wide variation
Applications: Optimising subsidies for reducing domestic water consumption
Identifying Optimal Rebate Policies: Multi-objective optimisation
Improved Program Cost/Water Savings between of 40-70%:
Current schemes
Applications: Undergraduate Teaching
• Env Eng: Fourth Year Course on WSUD/IUWM • MUSIC and Urban Developer used as a tool to
apply key concepts to real world project• Positive student feedback • Students commented learning and using MUSIC
and UD was best aspect of course
BESS: Practical Implications
• Impact of water use variability on mains water saving estimates from tanks– Water savings from tanks will be sensitive to variations in demand– BESS can model increased in outdoor water use variability at end of hot, dry
periods (when tank is low)– Current approaches are likely to over-estimate water savings
• Simultaneously model changes in appliances with rainwater tanks and grey-water re-use scenarios
• Impact of changes in water use on peak demands– Water infrastructure is designed to service peak demands
=> driven by water use variability – As BESS captures variability, potential to evaluate changes in peak.– Further research is needed on drivers of peaks demands– Strategic approaches to reduce peak and
defer infrastructure costs
Future Research
• Understanding behavioural drivers of water use
– Outdoor– Behavioural Change– Appliance Uptake– Price – Attitude/Demographics
• Combining end-use monitoring and behavioural surveys
• Proposed monitoring programs in Adelaide and Newcastle
– 300- 350 homes with hi-resolution smart meters
Cluster/Regional Scale Water Use Dynamics – Spatial Variability
Household SizeDistribution
Drivers
DemographicsPolicyClimate
Appliance Type Distribution
Attitude to Water Use
Sub-daily Water Use
Appliance Type
Indoor Water Use Event Dynamics
Shower/BathWashing MachineToiletTap etc.
Household Size Dynamics
Household Composition/ Age Distribution
Outdoor Water Use Event Dynamics
Allotment Scale Water use Dynamics – Temporal Variability
Summary
• Behavioural End-use Stochastic Simulator – Stochastically simulates end uses for individual houses
– Differences/Changes in Appliance type and household size
– Indoor water use events subdaily time steps
– Outdoor water use – probabilistic behavioural response to rainfall and temperature – Simulations capture observed statistics (variability)
• Flexible approach – Adapted to changes in water use behaviour
– Predict impact of changes
– Utilise new datasets in the future - promote data collection
• Integrated into Urban Developer– eWater product for cluster scale urban water management solutions
• Future Research– Behavioural drivers of water use variability