knowledgediscovery fromhouseholdmeters)

Knowledge discovery from household meters R Cardell-Oliver & J Wang Watermatex June 2015

Background & Related Work

© CRC for Water Sensi/ve Ci/es

Ageing Infrastructure

Popula;on Growth

Big picture: Drinking Water

Business Costs

Rainfall Changes

Motivation: reduce potable water use

Source: WA Water Corpora/on Water Forever 50 year plan

use less water

Current Practice

Annual average residen;al

water supplied

446

kL/property

Smart Metering 1: Data Collection

+


Smart Metering 2: Data Mining

Output: KNOWLEDGE Ac/onable insights

Input: RAW DATA Smart meter /me series

Data mining

Smart metering: who cares?


Water U;li;es: How frequently do habits occur? How intensely is water applied? Do end-‐users follow the rosters? Where to intervene?

End-‐users: Why is my bill so high? How can I save money? Is my irriga/on ok? Are there any leaks?

Planners and Designers: When is demand highest? Why? What are the seasonal trends? How is most water being used? How much could planning decisions (e.g landscaping) reduce demand?

?

Water Use Habits


Data mining for … water use habits ~ 1500 L/h every day at 5am from Nov to Apr ~ 500 L/h every We & Sa at 10am from Jan to Dec Why habits?

•  High water use •  Likely discretionary •  Prevalent •  Recognise recurrence •  Psychology of

behaviour change

1. Mining end-use for leaks + peaks + habits


2. Features for customer segmentation Feature Example

•  Total volume 29 kL •  Occurrences 26 times •  Elapsed days 60 days •  Average Intensity 1130 L/h •  Frequency 4 times/week •  Hour of day 3am •  Days of week Tu,Th,Fr,Sa •  Confidence 84% •  Days = roster ? No •  Efficient ? Yes


Research Questions

•  How do households differ in their high-magnitude water use habits?

•  How do populations differ?

•  What are the implications for managing water demand?


Case Study Results


Identify End-Use Break-down


Planners and Designers: How is most water being used? By how much could planning decisions (e.g landscaping) could reduce demand?

Leaks

Ad hoc peaks Habits

0 2 4 6 8 10 12 14 16 18 20 22

Kalgoorlie L/h/hh in Dec'14

Hour of Day

Mea

n L/

hh/h

050

100

150

200

Top 25 RHM usersRemaining RHM usersOther Use

0 2 4 6 8 10 12 14 16 18 20 22

Karratha L/h/hh in Dec'14

Hour of Day

Mea

n L/

hh/h

050

100

150

200

Top 25 RHM usersRemaining RHM usersOther Use

Habits in Peak Hour of day of Peak Month


Can this peak be moved ? Planners and Designers: When is demand highest? Why? Can infrastructure upgrades be delayed?

Habit demand vs Garden size


Habit demand (kL/house/year)

Garde

n size (m

2 /ho

use)

Water U;li;es: Which users have inefficient watering?

Habit demand vs Garden size


Inefficient irriga;on

Conclusions & Future Work


Implications for managing water demand

•  Populations differ (a lot) – so metering is important •  Plenty of scope for reducing demand without loss of

amenity

•  Many opportunities for water-sensitive precinct planning

•  Providing the right information, in the right way, can empower customers – research still needed


Future: Smart-er meters

•  Immediate feedback is the most effective •  Detect deviations from “normal” use •  At the household meter •  Report results in a usable ways


amphiro.com self-‐powered water + energy shower meter

Future: water-sensitive precincts

Use smart-metering techniques to save water at the precinct-scale


More applications for habit detection


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watersensitivecities.org.au Synthesis Report (June 2015), Fact sheets, Talks, Publications

2012 - 2021


knowledgediscovery fromhouseholdmeters)

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