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Discussion of PJM Forecasting Model
Tim McClive
OPSI Annual Meeting
October 12, 2015
Navigant Overview
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Navigant’s core business areas
› Management Consulting, Economics, Financial Advisory, Disputes
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& Investigations
Publicly traded since 1996 (NYSE: NCI), 35 offices in N.A., Europe,
Asia
Navigant’s global energy practice
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Clients: 50 largest electric and gas utilities, 20 largest independent
power generators, 20 largest gas distribution and pipeline
companies, Federal/State governments, new entrants, investors
Personnel: 450+ consultants, average 15 years experience, 60%
with advanced degree, 51% with engineering degree
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Overview of Navigant’s Engagement with P3
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Provide an independent review of
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Work with PJM LAS to investigate and recommend steps to
improve accuracy and stability of the forecasts
Preliminary findings
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› PJM’s proposed changes of its load forecasting models
› the structure, data, and estimation techniques of the models
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Near term – improvements to a key energy efficiency variable
Longer term – potential theoretical and empirical modifications to
the models
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Econometrics – not as hard as rocket science, but close
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“Econometrics is a special type of economic analysis in
which the general theoretical approach is combined with
empirical measurement of economic phenomena.” Leontief, 1948
Econometrics has two elements, and both are important:
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Economics – e.g., demand is higher with a stronger economy, lower
prices, hotter summers, or colder winters (or lower when the
opposite happens), and demand is lower when more efficient
technology is used to meet the same service needs
Mathematics – the estimated models for demand should meet
specified statistical conditions with an objective of producing
unbiased estimates with minimum variance and error
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Issue: SEER Forecasts Underestimate Peak Demand
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PJM used available SEER data, and Navigant offered a way to
augment that with publicly available data to improve accuracy
Cooling demand forecast – conditioned by projected efficiency
improvements for different equipment classes.
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For residential AC and heat pumps, PJM uses efficiency indexes based
on seasonal energy efficiency ratio (SEER)
But SEER is calculated using full-load and part-load test conditions, while
the energy efficiency ratio (EER) measures equipment running at full load
Correlations show that large improvements in SEER correlate with only
moderate improvements in EER
Forecasts based on SEER may overestimate efficiency gains
over time, and underestimate peak electrical demand
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Illustration of SEER-EER Relationship
Points represent SEER
and EER ratings of
individual AC systems in
the AHRI database of
certified products.
Note metrics’ ranges:
SEER: 13.0 to 26.0,
100% above lowest
EER: 9.0 to 16.5,
83% above lowest
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There is no direct relationship between SEER and EER
AC, HP manufacturers can increase SEER in ways that do
not increase EER (advanced motors, compressors, controls)
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The Effect on PJM’s Total Cooling Index
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Graphical comparison of load-weighted average Total
Cooling Index (SEER-based vs. EER-based).
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Sensitivity of Forecast to Projected Index Values
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PJM’s May 2015 forecast update demonstrated that the
forecast is highly sensitive to the efficiency index.
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Efficiency growth that is one % point faster than projected results in
peak demand forecast falling by 5,000 – 7,000 MW by 2020
Navigant tested the sensitivity of the forecast to a change
from a SEER-based to an EER-based projected index and
calculated a material impact on the forecast.
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Efficiency-related parameters in the forecast equations will likely
change when the historical SEER-based index is replaced
Nevertheless, the transition from SEER to EER-based index would
still materially affect the peak demand forecast.
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On Forecast Accuracy
» From Nov ‘14 LAS
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presentation
The over-forecast
in 2010-14
prompted the
current reviews
Forecasts are not
consistently high
nor low over the
long-term
PJM’s work in 2015
is a good start to
resolve issues
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Future Work and Recommendations
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PJM Staff and LAS members work continuously and proactively
to monitor forecast model performance and improve the
accuracy of the models
Navigant’s review has identified areas for consideration and
future study and will raise these with PJM and the LAS, e.g.
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Simplify interactive structure of the explanatory variables to separate
the discernible impacts of economic, technology and weather variables
Consider a two-part process – use current models to control for the
short-term variances (“normalization”) and develop new model for the
long term effects of economic and technology adoption variables
Various “in the weeds” ideas around technical methods to address
data pooling, multicollinearity, and missing-variables issues
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Navigant Contacts
Tim McClive, Director, Washington DC
[email protected]
(202) 973-4555
Ken Seiden, Director, Boulder, CO
[email protected]
(303) 728-2479
Peter Steele-Mosey, Managing Consultant, Toronto, ON
[email protected]
(416) 956-5050
J. Decker Ringo, Managing Consultant, Burlington MA
[email protected]
(781) 270-8410
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