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