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ABSTRACTSmart water metering promises many benefits including customer empowerment, support for policy-making and environmental savings. This paper gives an overview of a new activity pattern model that can be learnt automatically from collected water meter readings. The model is used to identify and explain water use patterns. A case study from 188 houses in the Western Australian mining town of Kalgoorlie-Boulder is used to demonstrate the potential of activity pattern modelling for effective smart metering.

WHY SMART WATER METERING?Smart sensors are being deployed throughout the water industry to collect information about the state of assets. Smart sensors measure properties such as water quality, pipeline condition and metered water use. For example, household smart meters can record hourly water usage by end users, with time series data from the sensors collected continuously and automatically. The data from smart meters can be used to prepare water bills in the same way as traditional meter readings are used. However, this new technology also offers the opportunity to improve water efficiency through better evidence for decision-making. Extracting valuable information from the data provided by smart meter company assets and sharing this information within the business and with customers is known as smart metering (Water Corporation, 2010).

Several cost benefit studies have detailed the business potential of smart water metering (Marchment Hill, 2010; Water Corporation, 2010). Smart metering empowers water customers by providing more information on how and when water is used, enabling them to save water and so reduce their water use charges. Reduction in demand and, therefore, water supplied will reduce operating costs and contribute towards the deferment of capital asset upgrades. It also provides information to support decisions on water billing bands

and future investment for water providers.

The environmental benefits of smart

metering include water conservation and

reduced carbon footprints, which contribute

to climate resilience. A further significant

operational benefit of smart metering is the

low staff maintenance costs for smart meters

compared with traditional meters.

However, although the benefits are well

known, practical techniques to support

smart metering are just emerging. New

techniques are needed to analyse collected

water meter readings in order to identify

and explain water use patterns. This paper

presents a new model for this purpose.

DATA MINING FOR WATER USE PATTERNSOur goal is to discover water usage patterns

automatically from smart meter readings.

This section gives an overview of data

mining techniques that can be used to

achieve this goal and summarises some

challenges for doing this.

TECHNIQUESData mining is a research field of

Computer Science concerned with finding

interesting patterns in large data sets

(Han et al., 2006). Data mining methods

use heuristics and efficient algorithms to

discover interesting patterns automatically.

Efficiency and automation are important

because real-world data sets are large and

complex. Interesting patterns are defined

to be those that can be used to inform

policy and strategy because they provide

information relevant for decision-making.

That is, whether a pattern is interesting

or not depends on the application. For a

household user, an interesting pattern is:

On 61/170 days (36%), recorded water use was relatively high (average of 2.78KL per day), totalling 169KL (52%) of your overall water use for the period. This high water use occurred most frequently on Wednesdays and Saturdays, between 8am and 9am on those days.

For water providers, the following pattern is interesting because it differs from findings in other studies. Furthermore, continuous flows are interesting for users because they are unexpected: users may be unaware of a continuous flow and so water is wasted where waste could be avoided.

In the sampled population, the prevalence of continuous flow days (potential leaks) was high: 84% of metered houses registered at least one day of continuous flow. Continuous flows accounted for 10% of all water used by this population and were more common in summer than in winter.

PATTERN DISCOVERYData mining algorithms search for patterns in a given set of observations. Each observation is characterised by its features. For smart water meters, features include a meter identifier, date of use, land use type, and volumes used per hour of the day. In addition, new features can be derived from the raw data. For example, the day of the week or season of the year can be derived from date of use. The total volume used per day, the minimum hourly flow per day, and the aggregated volume per quarter-day can all be derived from hourly volumes.

Patterns are simply groups of observations that are similar in some way. For example, the set of all days for a given meter in which the minimum flow is at least two litres per hour forms a group that can be characterised as a continuous flow, potentially a leak. As well as common sense rules of this type, groups can be discovered using data mining clustering algorithms (Cardell-Oliver, 2013). For example, a set of observations for a particular meter can be partitioned by volume into relative categories of extreme, high or low water use for that user. Grouping data in this way is known as unsupervised learning since the patterns are discovered automatically by a clustering algorithm, rather than by an explicit rule as in a continuous flow group (Han et al., 2006).

MAKING SENSE OF SMART METERING DATA A data mining approach for discovering water use patternsR Cardell-Oliver, G Peach

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PATTERN EXPLANATIONHaving identified potentially interesting patterns as groups of related observations, the next step is to characterise these groups in terms that are understandable to human users. Since any subset of features and constraints on those features will define a group, the challenge is to identify the interesting patterns. Attribute oriented induction is a data mining technique for describing a group in terms of the distribution of its attributes (Han et al., 2007). For example, a group of high water use days can be partitioned by the day of the week on which the water is used. The frequency distribution of those days gives a signature that characterises the group. Threshold frequencies can then be used to define which patterns are likely to be interesting to users.

KALGOORLIE-BOULDER SMART METERING STUDYTo illustrate the application of activity pattern models in practice, we present a case study of metered water use from the Western Australian city of Kalgoorlie-Boulder. Situated 595 kilometres inland, east of Perth, in the Eastern goldfields of Western Australia, Kalgoorlie-Boulder is surrounded by semi-arid countryside. The climate is hot and very dry, with an average annual rainfall of 264mm and an annual evaporation rate of 2943mm. The main business was and is mining, from the gold rush of 1893 to large open-cut mining today. Since 1903 the city’s water has been pumped from Mundaring Weir in Perth via a pipeline to Kalgoorlie.

The study uses hourly water meter readings for 239 meters selected from 13,800 properties metered in the 2011–12 Kalgoorlie Smart Meter Trial by the Water Corporation of Western Australia (Water Corporation, 2010). The raw data from the trial comprises a meter number, land use type for that meter, and a sequence of hourly time-stamped readings giving the meter reading for each hour. This data is converted into daily observations, given as 24-tuples of the number of litres of water used per hour.

DATA MINING CHALLENGESLEVEL OF DETAIL

Figure 1 illustrates the sequence of hourly water usage for two households in the case study. Patterns of water use are not evident at this level for two reasons. First the data is too detailed, in that it does not show higher-level conceptual views for time (e.g. day or week or season) or volume (e.g. extreme use or use associated with a continuous flow).

On the other hand, the data is not detailed enough because many interesting details are hidden. Concurrent activities such as taking a shower while the washing machine is running, and sequential activities such as showering and breakfasting while getting ready for work in the morning, are all aggregated into single, hourly water volume measurements. Nonetheless, interesting temporal and spatial patterns are to be found in these time series, as will be shown in the following sections.

NOISE

Real-world data sets are typically noisy in that they have missing data and contain erroneous values, and the data set is not exceptional in this regard. The main noise feature is missing data points arising from the initial set-up of new wireless meters. The data set is taken from 205 days of the year (from mid-January to early August 2012). Nineteen per cent of possible hourly reading data points were unavailable. Most of the missing data is from days during the set-up phase when readings were not received by any meters. These gaps can be seen in Figure 1. In the following analysis we include only full days of data from each meter: that is, days for which the full 24 hours of readings were available.

Another challenge is how to identify errors in the data. The data includes a number of exceptionally high or low readings that may be erroneous. In many data mining applications, such outliers are treated as errors and removed from the data set. However, inspection of our data set by water use experts suggests that the readings are unusual but correct, and so we have not removed these outliers from the data set.

RESULTSUSING COMMONSENSE KNOWLEDGE

Logical rules can be used to define patterns that capture commonsense knowledge. For example, a continuous flow is said to occur when at least two litres of water is metered in every hour over a 24-hour period. Continuous flows may indicate an undetected leak in a pipe or appliance. This rule is used to select continuous flow days for each user. Then the set of continuous flows can be characterised using attribute-oriented induction (Han et al., 2006). The properties of continuous flows can also be summarised across the whole population. From the Kalgoorlie-Boulder data set the following patterns can be mined:

Meter 90 has a frequency of 16% (28/170 days) of continuous flow days. The significance of water identified as continuous flows for this meter was 2% (3/152 kilolitres) of all water used by this household.

Meter 132 has a frequency of 52% (89/170 days) of continuous flow days. The significance of water identified as continuous flows for this meter was 14% (56/412 kilolitres) of all water used by this household.

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 Figure 1. Raw data: hourly meter readings for two metered households.

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accounted for 10% (3/29 megalitres) of all water used by houses in the sample population.

INDIVIDUAL ACTIVITY PATTERNSClustering is a data mining technique for classifying observations into subgroups of closely related values. Clustering algorithms use heuristics to search automatically for

the best way of dividing a set of values. The algorithms ensure that values in the same cluster are close to one another, but each cluster is far from the other clusters. For daily water use volumes, the k-means clustering algorithm is used to classify an individual’s water use volume as extreme (X), high (H) or low (L), relative to the volumes used by that individual. Details of the k-means algorithm can be found in Han et al. (2006).

Figure 2 shows the discovered usage clusters for two households: 90 and 139. The clusters are X (shown as crosses), H (shown as triangles) and L (shown as circles). The figure shows that both households have three clear volume clusters delineated by horizontal dotted lines. The temporal division between summer and winter is shown by the vertical line. For most meters X and H activities are biased to summer. The mean daily use for meter 90 is close to the mean for this population, while meter 139 shows high water use relative to this population. Higher overall use for meter 139 can be attributed to the longer run of high use days and a higher volume range for both extreme and high clusters.

Often individual users have strong usage patterns within their clusters. For example, the H or X clusters may be biased to particular days of the week, most likely days in which the household is allowed to use garden sprinklers. Day-of-week patterns are discovered by separating the days in a given volume cluster into days of the week. Figure 3 shows the day-of-week frequencies for high water use for meters 90 and 139. The most frequent high use days of the week for meter 90 are Sunday (31%) and Wednesday (28%). The most frequent high use days of the week for meter 139 are Saturday (38%) and Wednesday (31%).

WATER USE IN CONTEXTLAND USE

Each water meter is associated with a land use for that property. There are 10 land use types represented in the Kalgoorlie-Boulder case study: house, home unit, duplex unit, multiplex unit, rest home, club, sports ground, park, sewage treatment works and residential vacant land. Figure 4 compares the range of water used per day for each category. The differences between land use categories are statistically significant. Examples in this paper focus on the 188 houses in the sample.

CLIMATE

It is to be expected that weather conditions will affect the volume of water used, with more water being used in the summer and less in the winter. The Kalgoorlie-Boulder case study supports that hypothesis. But how closely correlated is water use with daily weather conditions? Figure 5 summarises daily minimum and maximum temperatures and daily rainfall totals for Kalgoorlie-Boulder. Readings are from the Bureau of Meteorology station 12838 in Kalgoorlie available from www.bom.gov.au.

Figure 2. Individual household water use clustered by volume (X, H, L) and season (summer, winter). Cluster boundaries are shown by the dotted lines.

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Figure 3. Day-of-week patterns in the high volume (H) clusters for meters 90 (left) and 139 (right). High water use for each meter shows a strong bias to particular days of the week.

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Resthome(N=1)

Club(N=3)

Sports gnd(N=1)

Park(N=2)

Sewage Wks(N=1)

Vacant(N=2)

Figure 4. Water use (litres per meter per day) versus land use type (outliers not shown).

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Many of Australia’s coastal cities have a rainy and a dry season, but Kalgoorlie has low rainfall at all times of the year. There is a statistically significant, seasonal, temperature-based difference in water use between summer and winter, but we found no correlation between the rare rainfall events and water use.

Mean daily temperature is correlated

with water use across the whole population

(corr = 0.75, p-value = 2.2e-16). However,

this daily correlation statistic is somewhat

misleading because water use actually

follows a broader trend, rather than daily

responses. The correlation between the

average weekly mean daily temperature and average daily volume per week is stronger (corr = 0.86, p-value = 3.797e-09). At a broader scale, the seasonal bias between summer and winter can be measured for selected activities of individual households. For example:

The seasonal bias (summer/winter) of meter 90 for days with continuous flows (N=28) is 8%/92%. For extreme days (N=10) the bias is 100%/0%, for high use days (N=32) the seasonal bias is 87%/13%. For low use days (N=128) the bias is is 39%/61%.

RELATED WORKAppliance models and socio-economic models are the state-of-the-art techniques for understanding and explaining end user water usage.

Appliance models explain overall water use in terms of the water used by washing machines, showers, baths, household taps, outdoor use and so on. The types of questions that can be answered by appliance models are statistical characteristics of water use.

For example, in Southern Queensland “on average, showers [account for] (42.7L/p/d: 29%) [Litres per person per day of water use] ... and clothes washers (31L/p/d: 21%)” (Beal et al., 2011). In this context, leakage can be regarded as an appliance. 

In California, the “average leakage rate in the study homes was 31 gphd [US gallons per household per day = 117 L/h/d], while the median rate was 12 gphd [45 L/h/d]. The wide disparity between these values shows that a small group of homes are leaking at very large rates, and this increases the average for the entire study group.” (DeOreo, 2011).

Appliance models are developed using a mixed-methods methodology. Data is gathered from interviews with householders, water use diaries, appliance audits, land use surveys, and fine-grained water meter readings. Once the data for an appliance model has been collected, then the data is analysed to construct a model.

The different viewpoints are used to triangulate observations. Fine grained water meter traces are manually labelled with activity names such as taking a shower, flushing the toilet, or watering the garden.

Software such as Trace Wizard (www.aquacraft.com) can be used to assist with the labour intensive process of labeling activities in a water use trace. In addition, contextual information such as household

Figure 5. Seasonal patterns of water use: Kalgoorlie-Boulder weather observations (top) with daily temperature maxima (red top) and minima (blue lower) and rainfall (green bars) [Source: BOM] and per-week distribution of water use volumes for all users (bottom).

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type, block size, number of householders, social demographic, as well as interviews and water-use diaries, are used to help identify individual activities linked to water use.

Social and economic models capture the relationships between customers’ water use and social factors such as conservation motives or economic factors such as income or home ownership. The premise underlying this type of model is that the “success of household water demand management strategies is dependent on how well we understand how people think about water and water use” (Jorgensen et al., 2009).

The effect of incentives, regulations, and property, household and personal characteristics, as well as trust, have all been studied. For example, in a Perth study Syme et al. (1990) found statistically significant correlations between attitudes about garden importance and household water use. Socio-economic models are developed using interview data and water use records. Methods such as regression analysis and analysis of variance are used to characterise the strength and statistical significance of direct and indirect drivers of water use.

Both appliance and socio-economic models give rich explanations of historical water use. However, neither of these models is well suited for providing continuous, real-time monitoring and feedback for large populations of water users, because both types of model require specialised data collection with user involvement in interviews and diaries.

The activity pattern model proposed in this paper thus complements existing models by providing a new way of viewing end-user water use, using only automatically collected measurements for large populations and time spans.

CONCLUSIONThe goal of smart metering is to discover and characterise interesting patterns of water use by end users. To this end, we have developed a novel activity pattern model for smart metering. The model uses data mining techniques to select and characterise the activities of individual users and of user populations. Activities are discovered from hourly, per meter, water use readings. Pattern discovery and description can be automated, making the approach scalable for large user populations over long time periods.

This paper presents an overview of activity pattern modelling and the data mining techniques used for activity discovery.

A case study from 188 houses in the Western Australian mining town of Kalgoorlie-Boulder is used to illustrate the practicalities of activity pattern modelling. Key findings from this case study for its stakeholders are:

1. The population of Kalgoorlie-Boulder is known to have a high average water use, with an average household consumption of 359 kilolitres against a state average of 270 kilolitres. The unique situation of this mining town population and its arid climate are likely contributing factors. Our case study sample of hourly water use volume over a time span of nearly six months (170 days), for a population of 188 houses, has provided a new and detailed picture about the patterns of water use that occur in practice.

2. Continuous flow patterns were prevalent in the Kalgoorlie-Boulder sample, with 84% (157/188) of households having one or more days of continuous flow. Continuous flows accounted for 10% (3/29 megalitres) of all water used by houses in this sample population.

3. Many individuals have strongly identifiable water use patterns characterised by usage volume or day of week. Identifying such patterns is valuable for targeted feedback to achieve water efficiency goals.

Smart water metering promises many benefits including customer empowerment, support for policy-making and environmental savings. Findings from the Kalgoorlie-Boulder case study demonstrate the potential of activity pattern models to help realise this promise. In future research we plan to:

• Apply activity pattern modelling with different populations;

• Develop methods for making activity discovery more robust and general;

• Investigate the potential of using additional contextual information such as geo-spatial information; and

• Develop methods for visualisation and communication of activity patterns to customers and providers.

Another area for future research is the use of smart metering to inform future design upgrades by assessing peak hourly flows and analysing correlations between temperature and water use and other relevant contextual information.

ACKNOWLEDGEMENTSThis research was supported by the Cooperative Research Centre for Water Sensitive Cities and the Water Corporation of Western Australia.

THE AUTHORS Rachel Cardell-Oliver (email: rachel.cardell-oliver@uwa.edu.au) is a Professor of Computer Science & Software Engineering at the University of Western Australia. With the Australian

Co-operative Research Centre for Water Sensitive Cities, she is developing novel data mining techniques to discover activity patterns in household water meter data. Her research focuses on end-to-end reliability of sensor networks, and she has worked on deploying field sensor networks, reliable communication protocols, and analysis of sensed data.  

Garry Peach (email: Garry.Peach@watercorporation.com.au) has been managing the Smart Metering initiatives being undertaken by the Water Corporation of WA. The success

of a recent trial in Kalgoorlie-Boulder involving almost 14,000 properties and including the installation of a fixed wireless collection system has resulted in a further 14,500 smart meters being installed in towns in the Pilbara region.

REFERENCES

Beal CD, Stewart RA, Huang T & Rey E (2011): SEQ Residential End Use Study. Journal of the Australian Water Association, 38, 1, pp 92–96.

Cardell-Oliver RM (2013): Discovering Water Use Activities for Smart Metering. To appear in 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (IEEE ISSNIP 2013), Melbourne, Australia, April 2013.

DeOreo WB (2011): California Single-Family Water Use Efficiency Study, Aquacraft Inc. Water Engineering & Management, www.aquacraft.com

Han J & Kamber K (2006): Data Mining: Concepts and Techniques (2nd ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Jorgensen B, Graymore M & O’Toole K (2009): Household Water Use Behavior: An Integrated Model. Journal of Environmental Management, 91(1), pp 227–236.

Marchment Hill Consulting Pty Ltd (2010): Smart Water Metering Cost Benefit Study. Retrieved May 2011 from www.swan-forum.com/uploads/5/7/4/3/5743901/smart_metering_cost_benefit.pdf.

Syme GJ, Seligman C & Thomas JF (1990): Predicting Water Consumption From Homeowners’ Attitudes. Journal of Environmental Systems, 20, pp 157–168.

Water Corporation (2009): Water Forever: Towards Climate Resilience (Summary). Retrieved October 2012 from www.watercorporation.com.au.

Water Corporation (2010): Kalgoorlie Smart Metering Trial Frequently Asked Questions. Retrieved February 2012 from www.watercorporation.com.au.