IEEE Power & Energy Magazine - May/June 2020 - 70

actions to automatically correct issues quickly. Forwardlooking utilities with high expectations for load research
and analytics will make significant investments in software
technology, hardware capabilities, and staffing to bolster
data quality so that the future advanced applications do not
fail due to a lack of reliable input data.

High-Value Research Questions
Leadership is the biggest determining factor among utilities
that make significant returns on their investment in analytics and those that fail to achieve tangible benefits. Utilities
should position managers who can pinpoint high-value goals
and challenge load research and other teams to meet those
goals using the appropriate technology and methodologies.
Importantly, these leaders do not always have to understand
the minutia of the solution, but they must lead the formulation
of the value proposition.

Do Not Forget Where We Came From
It is a cliché, but as we move toward increasingly available
smart meter data and the new technologies used to harness
it, we should not forget the lessons learned from the past. In
many applications, it will make more sense to work with a
robust, statistically designed sample for a population rather
than a census, given the potential for bias from unknown
data quality issues. An agile load research group of the future
will consider the efficient solutions used in the past when
determining the best approach to a problem. Using fully
deployed and perfectly functioning AMI, various class and
domain load profiles can be easily calculated directly from
the interval data collected for each account in the population. In practice, however, the AMI data are incomplete and
contain errors. There will always be some accounts missing,
and many others may have only partial data. As a result, a
simple tally of all the available data will usually fall short
of the total hourly load of the entire class or domain. If the
accounts with usable AMI data are materially different than
those with missing AMI data, the estimation calculation will
not accurately result in the true total load of all accounts for
the class or domain of interest, which could introduce bias or
create misleading estimation results.

Take Advantage of Powerful Analytics Tools
Although we use the lessons of the past, moving forward,
we must also take full advantage of newer and more innovative methods. The list of these, such as artificial intelligence,
machine learning, and advanced statistical techniques, is
familiar. Load researchers must keep abreast of developments in the data science realms in addition to the happenings in the utility industry.

Attract Top Talent for Both Utility and
Supporting Vendor Communities
For the utility industry to be among the leaders for getting
high value from data-driven applications, we must attract and
70	

ieee power & energy magazine	

retain top talent. Data scientists, statisticians, electrical engineers, computer scientists, and behavioral economists are
among the disciplines needed by utilities and vendors, and
the competition for this talent continues to be fierce. Banking, finance, and high-tech sectors are also vying for professionals with the experience and expertise to mine value from
big data.

For Further Reading
"Global best practices and trends in load research," DNV
GL, Høvik, Norway, 2020. [Online]. Available: https://www
.dnvgl.com/publications/global-best-practices-and-trends
-in-load-research-166325
W. G. Cochran, Sampling Techniques, 3rd ed. New York:
Wiley, 1977.
Y. Wang, Q. Chen, T. Hong, and C. Kang, "Review of
smart meter data analytics: Applications, methodologies,
and challenges," IEEE Trans. Smart Grid, vol. 10, no. 3, pp.
3125-3148, May 2019. doi: 10.1109/TSG.2018.2818167.
T. Hong, D. W. Gao, T. Laing, D. Kruchten, and J. Calzada, "Training energy data scientists: Universities and industry need to work together to bridge the talent gap," IEEE
Power Energy Mag., vol. 16, no. 3, pp. 66-73, May-June
2018. doi: 10.1109/MPE.2018.2798759.
C. Puckett, "Smart energy technologies: An application
using residential battery storage," presented at the 2018 Int.
Energy Policy & Programme Evaluation Conf., Vienna,
Austria, 2018. [Online]. Available: https://energy-evaluation
.org/wp-content/uploads/2019/06/2018-puckett-paper
-vienna.pdf
"Report: Evaluation of the establishment of uniform protocols for measuring, verifying, validating, and reporting the
impacts of energy efficiency measures; a methodology for
estimating annual kilowatt savings; and a formula to calculate the levelized cost of saved energy for energy efficiency
measures implemented by investor-owned electric utilities
in the Commonwealth," Commonwealth of Virginia State
Corporation Commission, Richmond, Dec. 1, 2016. [Online].
Available: https://www.scc.virginia.gov/comm/reports/2016_
EM&V_Report.pdf

Biographies
Curt Puckett is with DNV GL, Høvik, Norway.
Craig Williamson is with DNV GL, Høvik, Norway.
Claude Godin is with DNV GL, Høvik, Norway.
Will Gifford is with TROVE Predictive Data Science,
Buffalo, New York.
Jonathan Farland is with TROVE Predictive Data Science, Buffalo, New York.
Tom Laing is with North Carolina Association of Electric Cooperatives, Raleigh.
Tao Hong is with the University of North Carolina at
Charlotte.


p&e
may/june 2020


https://www.dnvgl.com/publications/global-best-practices-and-trends-in-load-research-166325 https://www.dnvgl.com/publications/global-best-practices-and-trends-in-load-research-166325 https://www.dnvgl.com/publications/global-best-practices-and-trends-in-load-research-166325 https://energy-evaluation.org/wp-content/uploads/2019/06/2018-puckett-paper-vienna.pdf https://energy-evaluation.org/wp-content/uploads/2019/06/2018-puckett-paper-vienna.pdf https://energy-evaluation.org/wp-content/uploads/2019/06/2018-puckett-paper-vienna.pdf https://rga.lis.virginia.gov/Published/2016/HD12 https://rga.lis.virginia.gov/Published/2016/HD12

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