Intelligent Urban Water Systems

Jin WangThe University of Western Australia16-09-2014

INDUSTRY CONTEXT AND PROBLEM ENVIRONMENT

CRC for Water Sensitive Cities • CRCWSC researches urban water reform required to

transform our cities into liveable, sustainable and productive cities.

• Our research over the next nine years will guide capital investments of more than $100 Billion by the Australian water sector and more than $550 Billion of private sector investment in urban development over the next 15 years.

© CRC for Water Sensitive Cities 2012

Smart Water Metering

Customers – empowerment, informed behaviourPolicy – billing bands, future investment, planningEnvironmental – reduce water use, carbon footpr.Operational – reduce OHS costs, delay/avoid new

infrastructure

Smart meter = asset (install, maintain)

Smart metering = discovery of knowledge to support decision making

Intelligent Urban Water Systems

GoalTo develop techniques for utilizing sensor data to optimize the efficiency and safety of urban water systems

Areas1. Data mining of patterns from smart water meters2. Optimization of pumping from multiple alternative

sources of water 3. Optimizing sensor selection and placement for

meeting information goals e.g. detecting leaks in pipeline systems.

Smart Water Meter Data

Raw data demo: Raw data

RESEARCH AIMS AND QUESTIONSINDUSTRY AIMS AND QUESTIONS

Data mining

Data mining is the process of discovering interesting patterns and knowledge from large amounts of data

Modes of enquiry: • explore (what)• explain (how)• predict• plan

Data mining = searching for interesting patterns in data in order to:

• Characterize and discriminate categories • Identify frequent patterns, associations and

correlations• Predict future behaviour using classification and

regression• Discover clusters in labelled and unlabelled data• Analyse outliers and detect anomalies

[Data Mining, Han, Kamber & Pie (2006)]

Data mining example- characterize and discriminate• Example from smart metering

– Continuous flow patterns were prevalent in the Kalgoorlie sample with 84% ( 157 / 188 ) of households having at least one day of continuous flow. Continuous flows accounted for 10% (3 / 29 megalitres) of all water used by houses in the sample population.

Data mining example-identify frequent patterns, associations and correlations

• Example from smart metering

– For meter 99 on 50 / 170 days recorded water use was relatively high, totaling 15 megalitres (30 %) of 99’s overall water use. This high water use occurred most frequently on Mondays and Fridays, between 6am and 12 noon on those days.

Q 1 : What types of water use occur in Kalgoorlie? Karratha? Perth? Melbourne? Brisbane? (explore)

Industry problem:Discover “unknown unknowns” of water useAre the assumptions behind water saving campaigns correct? What are the new opportunities for customer engagement?

Approach:Build a conceptual data model for water-use activities using (only) hourly observations

Q 2: Identify temporal patterns of peak demands and the activities behind them (explain + plan)

Industry problem:Infrastructure planning: delay or avoid $M upgrades by engaging with customers to modify or reduce their use e.g. offset their watering schedules

Approach:Data mining models to automatically query temporal patterns in populations

Q 3 : Identify “inefficient” garden watering activities. How much / when is water is used this way? (explain)

Industry problem:Target and engage a small number of significant customers e.g. those with “inefficient” watering habits. Are all inefficient garden users also high overall water users? Is highly inefficient garden use prevalent or isolated users?

Approach:Searching for relatively rare patterns

(eg 1 hour per 168 hours in a week, needle in haystack)

Searching for complex temporal patterns (e.g. every Mo,Tu,Sa at 2am, every even day of the month at 3am, twice a week on different days, during summer but not winter)

CASE STUDY

Case Study 1: Kalgoorlie-Boulder WA238 properties (of 13,800) 14 months

Raw Data

Q 1 : What types of water use occur in Kalgoorlie? (explore)

Research Contribution: a conceptual data model for water-use activities using (only) hourly observations

Findings (1): Anomalies are interesting

One-off exceptions on accounted for 31% of all water use

94% of users have continuous flows (leaks)

Findings (2): Temporal patterns are interesting

Regular high use on Mon, Wed, Fri but only in summer

Case Study 2: Karratha WA100 households, 111 days

• Q: Is water demand related to land size? A: Possibly yes (work in progress)

Q 2: Identify temporal patterns of peak demands and activities behind them (explain + plan)

Q 2: Identify temporal patterns of peak demands and activities behind them

Q: Would adjusting a few customers’ recurrent habits achieve smoothed demand peaks? A: Possibly yes (work in progress)

Q 2: Identify temporal patterns of peak demands and activities behind them

Q: Would adjusting a few customers’ irrigation habits achieve smoothed demand peaks? A: Possibly yes (work in progress)

Q 3: Identify “inefficient” garden watering activities. How much water is used this way? When? (explain)

• “Intentions to save water are shaped by attitudes, beliefs, habits and routines, personal capabilities and contextual factors”[Russell S, Fielding K (2010) Water demand management research: A psychological perspective. Water Resources Research 46(5)]

• Human behaviour (calendar patterns) are complex• Human behaviours are non-stationary• Problem: how to detect habit patterns?• Aim: reliable, automatic generation of evidence for

decision making

Finding patterns – where is the irrigation?

Water Use Habits (Irrigation Patterns 1)

Water Use Habits (Irrigation Patterns 2)

IrrigationActual use136 kLBudget 12 kL

INDUSTRY IMPACT AND BENEFITS

Industry Benefits (1): Water Use Sig Patterns

• Kalgoorlie trial was a response to safety for meter readers (OHS)

• Independent report: “Smart meters have reduced water use in Kalgoorlie-Boulder by reducing the average duration of leaks [using] … suspected leak letters…. saved an estimated 13,713 kL in 2012/13

• There remains significant scope to leverage the information provided by smart meters

• Targeted customer engagement is critical”‘Intelligent networks - Smart Metering and Data Logging Programs Evaluation’, Report for Water Corporation by Marsden Jacob Associates Pty Ltd, January 2014

Industry Benefits (2): Water Use Habits

• Ranked list of customers for engagement• Identified potential for savings from inefficient

irrigation – just the excess (53%)• Configurable, automatic search supports

evidence-based decision-making• Answers new queries: – All high irrigators = all high water users ?

• Answers new queries: – What are the temporal patterns for peak demand? Is

there scope to adjust ? (delay infrastructure)

Summary

• Developed a novel model of water use signature patterns for medium-resolution smart metering

• Pattern discovery and description is automated, making the approach scalable for large user populations over long time periods.

• Novel way to identify calendar habit patterns• Supportive industry partnership – ongoing

work towards technology transfer

© CRC for Water Sensitive Cities 2012