FALL 2015
technologies of scale—in this case, the
world’s 1.75 billion smartphone users—
can yield large socioeconomic benefit
at a low cost. By combining advances
in machine learning, distributed datastream processing, and complex network
analysis, the GridWatch team seeks to
demonstrate that crowd-sourced data
streams can leapfrog some of thorniest
energy problems in the world’s least
industrialized countries.
will have a data set that was previously
unavailable—one that could allow
utilities, governments, and citizens to
study and improve grid performance and
management across different populations,
geographies, and time periods.
To achieve this goal, the GridWatch team
has been working with utility companies
in developing countries to identify
With support from USAID’s Development
Impact Lab, GridWatch is currently being
designed as a free and open-source
mobile app. Its sensing strategy is
based on the following: When a phone is
charging, the phone is connected to both
the power grid and cellular network.
When an outage occurs, the phone stops
charging, prompting the GridWatch app
to take measurements from on-phone
sensors, to try to confirm that the phone
stopped charging due to an outage and
not from normal use. If the GridWatch
app senses an outage, it will send a
report containing the time and location
of that outage to a central server. Finally,
software on this server will look for
multiple reporting phones and other
patterns, to corroborate reports and
measure the size of the outage.
Podolsky argues that GridWatch could
provide ratepayers better service. “And if
that didn’t happen,” said Podolsky, “they
would have some concrete data about
outages to put pressure on politicians
and government. It would be evidence for
customers to say: ‘These outages happen
regularly and are not acceptable.’”
As for researchers, said Podolsky, they
In August 2015, the Siebel Energy Institute
awarded a seed grant to the GridWatch
team to develop its system. Should the pilot
in Kenya go through, GridWatch would be
installed on the cellphones of KPLC and
IBM Research Africa employees before
being rolled out to a wider population.
Klugman notes that the GridWatch app is
being designed to protect users’ privacy;
it allows them to control the specific
sensor data shared as well as to view and
delete all measurements collected by the
app.
The GridWatch team expects the Kenya
deployment would allow them to answer
their first set of research questions and
then implement improvements.
“Along with questions about sensing on
phones, questions remain about how to
take advantage of the data returned by
GridWatch,” said Podolsky. “For example,
this data could help utilities decide
the optimal location of future sensors,
map the low-voltage grid, or even help
anticipate and prevent failures in the
future.”
One advantage of GridWatch is that it
does not require any new or expensive
hardware.
“GridWatch costs nothing to deploy
other than the little bit of data it takes to
download the app,” explained Klugman.
“Yet GridWatch is not a replacement for
smart meters, because smart meters
will tell you exactly how much power
you’re using in a house. GridWatch will
give you a binary signal: It will tell you
whether the power is on or off.”
has a high frequency of power outages,
averaging 10,000 monthly in Nairobi alone.
GridWatch aggregates data on household electricity
use, captured via grid-connected mobile phones, to
detect power losses and grid failures in near realtime.
potential partners to pilot and deploy the
app. The team is in active conversations
with the Kenya Power and Light Company
(KPLC), which sells electricity to over 2.6
million customers, and IBM Research
Africa. GridWatch data is appealing to
KPLC because it would enable the utility
to localize faults and improve efficiency
in scheduling maintenance and repair
crews. For the GridWatch team, Kenya
is an ideal location because the country
has wide cellular coverage and an 82
percent cellphone ownership rate. It also