POWER February 2020 - 22

CONNECTED PLANT
Hype and Hope: Artificial Intelligence's
Role in the Power Sector
Artificial intelligence (AI) is a term power professionals are seeing increasingly
to describe sophisticated digital technologies. Could it be the breakthrough
that is so highly hyped?
Sonal Patel
T
here is no argument about how
profoundly electric power has
changed our world. The advent of
steam power to mechanize production
propelled our world into the Industrial
Age, triggering the first of several industrial
revolutions. In the Second Industrial
Revolution, the use of power
enabled the era of mass production,
spurring groundbreaking advancements
in transport, telecommunications,
and manufacturing. The Third
Industrial Revolution ushered in the
digital age, marked most distinctively
perhaps by the proliferation of internet
(and later, the internet of things),
and the widespread use of information
technology to automate production.
And, now, according to the World
Economic Forum (WEF), we are at the
cusp of another profound change: a
fourth revolution, a transformation
that is distinct in its velocity, scope,
and systems impact, characterized
by scientific and technological breakthroughs
that are disrupting industries
and blurring geographical boundaries.
According to the Geneva-based
public-private international cooperation
that serves as a common platform
for adaptability, driving the Fourth Industrial
Revolution-its " software engine " -is
artificial intelligence (AI). The
concept has no uniformly agreed-upon
definition, but its applicability could
have a widespread potential across all
industries, generally to enable people
to rethink how to integrate information,
analyze data, and use insights to
improve decision making. Like other industries,
the power sector has been inundated
by AI-enabled digital solutions
of late. Though many seasoned experts
caution much of it is hype, pointing
to existing limitations, others laud
AI's potential in a sector already being
dramatically reshaped by a number of
trends, among them, digitalization, decarbonization,
and decentralization.
22
A Sticky Definition
As Harshal Upadhye, program manager
for the Artificial Intelligence
initiative
at the Electric Power Research Institute
(EPRI), explained to POWER, key
reasons that there's no " single-tested
universally accepted definition " of AI
is because it's broad and complex,
and because its applications are fairly
new-and constantly evolving. The concept
was first introduced by John McCarthy,
the founding father of AI, in a
1956 conference paper as " the science
and engineering of making intelligent
machines, especially intelligent computer
programs. " But today, AI is based
on a variety of disciplines, including
mathematics, statistics, cognitive science,
philosophy, and linguistics. EPRI
loosely defines AI as " how machines
can imitate human intelligence " -being
human-like rather than becoming human-such
as learning from experience,
said Upadhye. Because it enables a vast
set of capabilities, AI can be viewed as
a " tool, " he explained.
The U.S. Department of Energy (DOE),
which last September established a
new Artificial Intelligence and Technology
Office (AITO) to serve as a hub to
coordinate the agency's AI development,
delivery, and adoption, also emphasizes
AI's capability to solve problems. Its AI
definition: " the capability of machines to
rapidly learn from large data sets, solve
problems, and continuously adapt
to
new data without human intervention. "
The DOE's definition was echoed by Bryan
Friehauf, executive vice president and
general manager of Enterprise Software
at Zurich-based technology conglomerate
ABB, who stressed AI's capability is
enhanced " by identifying patterns within
data to make decisions. " Notably, the
term AI is still evolving, along with the
growing opportunities it affords. " There
are three key reasons why: an increase
in computing power, access to data, and
improved algorithms, " he said.
Marco Sanchez, vice president of Serwww.powermag.com
Understanding
AI's Different
Facets
To understand the crucial role data plays
in AI's vast complex, it is useful to examine
the technology's varied encapsulating
and overlapping approaches (Figure 1).
Machine Learning. " Sometimes used
interchangeably with AI, machine learning
(ML) is the study of data-driven computer
algorithms that improve automatically
through experience, " EPRI explains. ML,
currently the most widely used subset
of AI, encapsulates natural language processing
(NLP), deep learning, and neural
POWER | February 2020
1. Artificial intelligence encapsulates several
concepts including natural language processing
(NLP), deep learning (DL), and neural networks
(NN). Courtesy: EPRI
vice Sales at Mitsubishi Hitachi Power
Systems's (MHPS's) newly launched
Intelligent Solutions, a division that is
largely invested in MHPS's TOMONI solutions,
further illustrated how the definition
can depend of its end-uses: " In our
view, AI encapsulates automating processes
and actions that are performed
by humans today, which can have a
positive impact on performance, productivity,
and efficiency, " he explained.
However, he agreed AI uses heavily depend
on data. " AI leverages structured
data, for instance, machine sensor data,
and unstructured data, such as [original
equipment manufacturer] manuals,
maintenance records, weather data, and
market/business data, to perform advanced
decision making, " he explained.
http://www.powermag.com

POWER February 2020

Table of Contents for the Digital Edition of POWER February 2020

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POWER February 2020 - Cover1
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