IEEE Power & Energy Magazine - May/June 2018 - 72

the courses from various departments in degree requirements. Meanwhile, the power industry has too many analytical problems to squeeze into one single course. a holistic
approach would be to offer a series of courses on energy data
analytics, which could be bundled into a graduate certificate
program or a concentration within a master's program.
Many power programs today may not have enough tenuretrack faculty to cover energy data analytics courses. a workaround is to include analytics topics in existing power engineering courses in the form of lectures and course projects.
figure 4 maps selected analytics topics to some of the existing power engineering courses. a drawback of this approach
is the lack of coverage on analytics tools. for instance, in an
electricity market course with load forecasting as a course
project, the instructor may not have enough contact hours to
cover regression analysis in sufficient detail to prepare the
students for the project.

rebuild and renovate those programs, when many other business sectors were embarking on advanced analytics in the big
data era. with the development of advanced sensors and communications and information technologies, more precise and
detailed operation, management, and monitoring techniques
for power systems became possible. in this decade, numerous phasor measurement units and smart meters have been
deployed to help utilities better evaluate system states and also
better operate the grid. as a result, a large amount of data is
generated every second in power systems. how to turn the big
energy data into actionable insights is a challenge for power
engineers today and tomorrow. while utilities are in chronic
need of energy data scientists, most power engineering programs are not producing them. Moreover, it is quite difficult
for the power industry to compete with other analytics-savvy
industries in recruiting data scientists.
a transformation will not happen overnight. it will take a
village many years to establish a healthy pipeline of energy
data scientists. Universities and utilities have to work together
Looking Forward
in the late 20th century, many power engineering programs to make this happen (figure 5). The utilities feed the universiwere vanishing, with a downhill trend in faculty positions and ties with funded projects, and the universities use the funding
student enrollments. it took the power industry a decade to and research topics to help utilities gain insights from their data
while turning students into energy
data scientists. Most of these energy data scientists can then join
Energy Analytics Topics
the utilities after graduation, while
some of them may become educaLong-Term Load Forecasting
Power Engineering Courses
tors to train the future students.
Short-Term Load Forecasting
Power System Planning
To create this new "product line"
Electricity Price Forecasting
of
energy
data scientists, universiPower System Operations
Wind and Solar Power Forecasting
ties have to enrich their power enPower System Economics
gineering programs with relevant
Outage Prediction
Fault Analysis and Protection
course content, which typically
Asset Management
Electricity Market
requires additional tenure-track poCustomer Segmentation
sitions. given today's higher educaRenewable Energy
Anomaly Detection
tion environment, it is unlikely that
an academic department would deDemand Response
vote several tenure-track professors
to this area. on the other hand, the
figure 4. The links between energy analytics topics and traditional power engineerinterdisciplinary nature of energy
ing courses.
data science is calling for professors from different fields, such as
engineering, computer science,
business, and statistics. having sevFunded Analytics Projects
eral departments form a research
cluster and jointly sponsor one or
more tenure-track positions would
Energy
Actionable Insights
be a sensible approach. while joint
Power
University
Analytics
Industry
Programs
appointments between electrical
Faculty
engineering and other engineering
departments are not uncommon in
today's universities, it does take sigEnergy Data Scientists
nificant entrepreneurship to break
the silos among colleges in terms of
faculty recruiting.
figure 5. The ecosystem for producing energy data scientists.

72

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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
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