 |
| Traditional domestic water meter |
Water meters are fitted in every
home and are vital for customer billing and water conservation. Historically,
meter readings are taken manually and infrequently, only when a new bill needs
to be generated. However, due to technological advancements, water meters have
now become much more advanced. Automated meter readings (AMR) and automated
metering infrastructure (AMI) systems are increasingly being adopted by utility
providers. Such meters reduce the manual
labour required for water metering and the costs to consumers, while also
providing much larger amounts of data on water flow. With large datasets, there
is the potential to generate and gather large amounts of information from
customer meters. Such information can benefit more flexible payment plans for
customers (further reducing costs to consumers) and knowledge on the
performance of water networks. This is extremely useful to water providers as
such knowledge facilitates improvements to the quality and consistency of water
services.
With such large data-sets and
variations on what is considered “normal-use”, converting this data into valuable
and informative information is a challenge. One method for analysing water flow
datasets, such as those produced by customer water meters, is Comparison of Flow
Pattern Distribution (CFPD) analysis. This method works by firstly ensuring
that all data is in a consistent format and comparable, then ordering each
dataset from smallest to largest and finally plotting the datasets alongside
one another against a reference dataset. This facilitates the comparison of
datasets to identify both inconsistent and consistent changes over temporal and
spatial scales. This method has previously been applied at whole-system levels,
but not for the analysis of domestic customer data.
 |
| Transforming big data into valuable information to inform water companies and direct management practices |
Research by STREAMer Lucy Irons has
attempted to apply this method to domestic customer datasets. In the analysis,
datasets from 2000 Anglian Water customers were evaluated using CFPD analysis
to test if this the information generated could be used to identify events of
inconsistent change, such as a period of inoccupancy. Two approaches were
tested: block analysis and rolling average reference (RAR) analysis. The block
analysis was able to identify different types of leakages at the customer site,
as well as periods of non-occupancy. RAR
analysis was able to also identify leaks and provide information on their
duration.
The study was able to
successfully demonstrate the potential use of CFPD analysis when applied to
customer meter datasets to generate valuable information, with further
refinements needed before application by water companies. If such analysis were
able to be refined and effectively utilised by water companies, this would
progress and facilitate better management of water leaks, reducing costs to
both water companies and customers, and reducing water stress and water loss.
References:
Irons, L. M. et al. (2015) Data
driven analysis of customer flow meter data, Procedia Engineering. Elsevier,
119, pp. 834–843.
