IEEE Power & Energy Magazine - May/June 2018 - 106

deposit actual as well as synthetic data.
In the United States, for example, shar-
ing real grid data is limited by restric-
tions pertaining to critical energy infra-
structure information, as defined by
the Patriot Act of 2001. Therefore, it
would become critical for the research
community to build realistic scale yet
synthetic data to allow for testing of new
algorithms and ideas.
The IEEE Power & Energy Society
(PES) community recognized the chal-
lenges and opportunities in big data
as soon as the term "big data" began
to pick up momentum. After an initial
proposal in July 2012 (during the PES
General Meeting in San Diego), the
community decided to establish a task
force to study the issue of big data and
analytics for grid operations; the sub-
ject is now under the newly restructured
Analytical Methods for Power Systems
Committee. The committee has es-
tablished several working groups and
task forces, focusing on data access,
distribution systems analytics, and an

IEEE-wide webinar series. The group
has just successfully organized the first
IEEE Utility Big Data Workshop in San
Antonio, with more than 120 attendees
from around the world. An education
monograph is under development to in-
troduce data sciences to PES. Cumula-
tively, ten panels and more than 1,000
participants have been involved in the
activities organized by this group. This
committee participated in the IEEE
Transactions on Smart Grid special is-
sue on big data, published in September
2016. There are several technical re-
ports under development with the vol-
unteers from this committee. The group
welcomes more volunteers.
Overall, I am extremely optimistic
about the possibilities that data may
offer to power and energy systems.
This could become as important to the
power industry as the introduction of
the digital computer to the power sec-
tor, which transformed the entire elec-
tric power industry. The introduction of
data and data intelligence might trans-

form our industry even further. We are
lucky to be at a very exciting moment
in history.

For Further Reading
Y. LeCun, Y. Bengio, and G. Hinton,
"Deep learning," Nature, vol. 521, pp.
436-444, May 2015.
M. Wu and L. Xie, "Online detec-
tion of low-quality synchrophasor mea-
surements: A data driven approach,"
IEEE Trans. Power Syst., vol. 32, no. 4,
pp. 2817-2827, July 2017.
X. Geng and L. Xie, "Learning the
LMP-load coupling from data: A sup-
port vector machine based approach,"
IEEE Trans. Power Syst., vol. 32, no. 2,
pp. 1127-1138, Mar. 2017.
T. Hong, C. Chen, J. Huang, N. Lu,
L. Xie, and H. Zareipour, "Guest edito-
rial big data analytics for grid modern-
ization," IEEE Trans. Smart Grid, vol.
7, no. 5, pp. 2395-2396, Sept. 2016.
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