IEEE Power & Energy Magazine - May/June 2018 - 73
Two important justifications for a tenure-track faculty
position are job market demand and funded research projects. power companies can help from both aspects. while the
strong demand for energy data scientists in the job market can
speak for itself, it is nontrivial for power companies to fund
academic research projects. a natural gap exists between the
scientific research objectives of the universities and the analytic needs of industry. Utilities are trying to solve real-word
problems by applying analytic techniques that are well known
in a new way. Many ph.d. programs, however, are focusing
on creating something that has never been there before, rather
than looking for innovative solutions to existing problems. if
the research outcome can be a valid solution to a real-world
problem utilities care about, there are plenty of opportunities
for them to fund academic research.
data accessibility is a common barrier in conducting energy
analytics research. a few issues have to be addressed before
utilities can share data with the universities, such as security
issues from the critical energy infrastructure information data,
privacy issues from smart-meter and customer information
data, as well as the issues from giving away potential competitive information about the company. although a publicly available data repository is badly needed, many of these issues cannot be resolved by a single entity, such as a university or a power
company. it will again take a village.
The advanced research projects agency-energy (arpae), one of the funding agencies under the U.s. department
of energy, was exploring the feasibility of launching a largescale grid-optimization competition. after several precompetition workshops in 2014 and 2015, it became clear to the
attending experts and arpa-e program officers that the data
are critical to the success of the grid-optimization competition
as well as the advancement of power system research in general. To address the data issue, arpa-e launched its grid
daTa program, which stands for generate realistic information for the development of distribution and Transmission
algorithms. The program funded seven teams of universities, national laboratories, utilities, and vendors across United
states to address various aspects of power system models and
historically, the electric power research institute and the
national science foundation (nsf) have provided invaluable
support for university education and research in power engineering. however, energy data analytics has not yet been a
strong focus in the funded programs. with power and energy
industries as big stakeholders of data analytics, it makes
sense that public-private partnership programs such as the
nsf industry University cooperative research center be
looked at as a potential mechanism to kick-start change in
university curricula with the goal of meeting the emerging
industry need for energy data scientists.
after a decade-long grid modernization effort, the power
industry is now sitting on a gold mine of data. we now have
the opportunity to dig into the data, gain valuable insights,
and make the decisions needed to run the most complex manmade system on earth. Let's start with training the next generation of energy data scientists!
For Further Reading
b. h. chowdhury, "power engineering education at the crossroads," IEEE Spectr., vol. 37, no. 10, pp. 64-68, oct. 2000.
g. T. heydt and V. Vittal, "feeding our profession," IEEE
Power Energy Mag., vol. 1, no. 1, pp. 38-45, Jan.-feb. 2003.
p. w. sauer, g. T. heydt, and V. Vittal, "guest editorial special section on power engineering education," IEEE
Trans. Power Syst., vol. 19, no. 1, p. 4, feb. 2004.
U.s. department of energy, "workforce trend in the electric utility industry: a report to the United states congress
pursuant to section 1101 of the energy policy act of 2005,"
a. pahwa, k. L. butler-purry, and h. Zareipour, "foreword
for the special section on power and energy education," IEEE
Trans. Power Syst., vol. 29, no. 4, pp. 1871-1873, July 2014.
Quadrennial energy review Task force, "Transforming
the nation's electricity sector: The second installment of the
Qer," Jan. 2017.
T. h. davenport and d. J. patil, "data scientist: The sexiest job of the 21st century," Harvard Bus. Rev., pp. 70-76,
kaggle (2017). kaggle ML and data science survey. [online]. available: https://www.kaggle.com/surveys/2017
f. van den driest, s. sthanunathan, and k. weed, "building an insights engine," Harvard Bus. Rev., pp. 1600-1610,
T. hong, p. pinson, and s. fan, "global energy forecasting competition 2012," Int. J. Forecasting, vol. 30, no. 2, pp.
357-363, apr.-June 2014.
T. hong, p. pinson, s. fan, h. Zareipour, a. Troccoli, and r. J. hyndman, "probabilistic energy forecasting:
global energy forecasting competition 2014 and beyond,"
Int. J. Forecasting, vol. 32, no. 3, pp 896-913, July-sept.
sas institute, "Utility analytics in 2017: aligning data
and analytics with business strategy," cary, north carolina, 2017.
newVantage partners, "big data executive survey 2012,"
Tao Hong is with the University of north carolina at charlotte.
David Wenzhong Gao is with the University of denver,
Tom Laing is with the north carolina association of
electric cooperatives, raleigh.
Dale Kruchten is with national grid, hicksville, new york.
Jorge Calzada is with national grid, waltham, Massachusetts.
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