Smart Water Metering Customers – empowerment, informed behaviour Policy – billing bands, future investment, planning Environmental – 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 Goal To develop techniques for utilizing sensor data to optimize the efficiency and safety of urban water systems Areas 1.Data mining of patterns from smart water meters 2.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 dataRaw data
RESEARCH AIMS AND QUESTIONS INDUSTRY 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 use Are 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)
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 WA 100 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
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