Profile of a scientist:
Ernesto Arandia, a data scientist with the
IBM Smarter Water team in Dublin, Ireland, grew up half a planet away, in
land-locked Bolivia. It was this life high in the Andes, where water
availability is an issue for all citizens that spurred his interest in water
management, and the pursuit of a PhD in Hydraulic Science at the University of Cincinnati.
parlayed his research and creation of tools to manage water usage via smart
meters and demand forecasting into a job with IBM Research-Ireland in 2013.
Today, he is working on two research applications, a Water Network Analytics
Toolkit and iWIDGET.
Both of which deal with water demand and management by reducing cost and waste.
interested you in the IBM Research-Ireland Smarter Cities Technology Centre
EA: My interests are in modeling urban water and
environmental systems, stochastic processes, and data mining, as well as the
design and optimization of hydraulic and civil infrastructure. With my civil
and environmental engineering background, the IBM lab in Dublin seemed the
perfect place to pursue my research on sustainability projects in the area of
did you focus on for your PhD at the University of Cincinnati?
research in Cincinnati involved experimental and computational research for
accurate estimation of real-time water usage, measured by smart meters. It was
part of an EPA-funded project for the development of a contaminant warning
system after 9/11, which required modelling contaminant transport and water
quality in water distribution systems. The main goal was to develop a fine
resolution model capable of predicting how water is used in an urban water
network across space and time.
The experimental part involved setting up
fixed monitoring stations to collect smart meter data from thousands of utility
customers. Cincinnati Water Works was one of the first utilities in the US to
fully adopt smart meters in their metro area – which created a unique research
opportunity for me.
My first challenge was to find areas where the
stations could monitor the consumption of a sample of users as large and as
diverse as possible. This required a Geographic Information Systems (GIS)
analysis of topography, demographics and socio-economics. The data collection
stage took about two years and provided a dataset rich enough to understand,
characterize, and represent water usage.
One of the main outcomes of the project was a
model for synthetic generation of demands that vary periodically and resemble
real smart meter demands. Another outcome was a model to forecast water demand
aggregated at useful scales, which can be used to estimate and forecast water
consumption in an urban water network in real time.
was the first project you worked on after joining the Smarter Water team?
EA: Our Smarter Water team entered a challenge posed by the
Water Distribution Systems Analysis Conference called the Battle
of Background Leakage Assessment for Water Networks (BBLAWN). BBLAWN asked
for a methodology for recommending changes to the design and operation of a
water distribution system to minimize total expenditure, while meeting service
|Pictured Smarter Water Team L-R:|
Joe Sawaya-Naoum, Ernesto Arandia,
Bradley Eck and Fabian Wirth
entry paper, called A Simulation-Optimization Approach for Reducing Background Leakage in Water Systems, detailed a hydraulic model that simulates background
leakage, custom implementations of heuristic algorithms, and optimization
solvers. The solution methodology we proposed decomposed the overall problem
into smaller more tractable problems aimed at a single type of decision. We
found that examining each problem individually had the advantage of
simplifying implementation of software and interpretation of results, and
allowing parts of the problem to be examined in parallel.
annual expenditure of the simulated town (called “C-Town”) was €3.9 million, but
our models were able to reduce this to €1.5 million. This took into account both
capital expenditure (rehab and infrastructure such as replacing pumps, pipes,
valves, tanks) and operational expenditure (electricity costs as well as
environmental penalties for wastage). Our main recommendation to "C-Town" was to
invest about €600,000 to correct background leakage issues. This greatly
reduced the amount of water loss in the network, as well as costs such as
environmental penalties. This solution illustrates what a city could achieve in
terms of managing leakage and overall operations.
work paid off as we won the competition for having the best combination of
solution methodology, and economy of proposed infrastructural and management
What are you working on now?
I am currently working on a Water Networks Analytics Toolkit. It provides decision support for a range of
operational and planning problems on water networks and building blocks for new solutions. It's a web-based application that knows the optimal time to replace mechanical equipment such as pumps and meters; calculates water bills under various pricing schemes; and makes adaptive water pricing recommendations.
I am also involved with a project called iWIDGET, which uses analytics and smart meters to gather real-time data on water and related energy usage. The aim of the project
is to improve the management of urban water demand by reducing waste, providing
utilities with deeper insights into end-user demand, and ultimately reducing
customer water and energy costs.
information to householders to help them reduce their water usage, and also
provides utility companies better visibility of their customers’ usage. This
improved visibility lets the utility perform more accurate demand forecasting, and
alert customers of suspected leaks.
is the future of smarter water management?
EA: Water is a constrained resource, so utilities have the
dual task of reducing losses through leaks in the network as well as reducing
overall consumption. The best way to tackle these issues is by integrating
government regulation, customer behavior and the utility’s own approach to
improving infrastructure and measurement of usage.
utilities can learn from the energy and utilities sector, as it has a more
highly developed model for pricing and demand management. The energy sector
uses, for instance, an effective pricing mechanism based on “time of use” which
means consumers use less power at peak times to reduce the chance of an outage.
suppliers and distributors are also more proactively optimising their networks,
using demand forecasting to balance energy supply (including renewables) with
consumer demand. The water utilities should adapt and add to these components
from the energy model instead of trying to reinvent the wheel.
What is your advice to future water scientists?
EA: I see great opportunities to do joint research with scientists
working in the energy domain. I think this collaboration can lead to new and more
effective methodologies for water and energy management. It can also yield new
combined strategies to improve water and energy efficiency. The data to support
such cross-discipline research is increasingly becoming available and it is
well known that systems cannot be fully described in total isolation. For
instance, planning the optimal rehabilitation of a water network cannot be
approached effectively with disregard for optimal electricity consumption.
feel privileged to work in an environment that offers an abundance of human and
material resources of quality to address meaningful projects and to conduct
exciting and innovative research. I think that it is important for young
scientists to take the knowledge and solutions they developed and bring them
back to their origins, in my case Latin America.
instance, in Bolivia, we do not have a continuous supply of water, which
impacts our poorer citizens the most. I think that the work I have done can
help make a difference. Technology that has been developed in advanced
economies can be brought to developing countries and make an impact on their
economies and quality of life.
Labels: analytics, demand forecasting, environment, ibm research - Ireland, smarter cities, smarter water