The performance of buildings participating in demand response (DR) programs is usually evaluated with baseline models, which predict what electric demand would have been if a DR event had not been called. Different baseline models produce different results. Moreover, modelers implementing the same baseline model often make different model implementation choices producing different results. Using real data from a DR program in CA and a regression-based baseline model, which relates building demand to time of week, outdoor air temperature, and building operational mode, we analyze the effect of model implementation choices on DR shed estimates. Results indicate strong sensitivities to the outdoor air temperature data source and bad data filtration methods, with standard deviations of differences in shed estimates of ≈20–30 kW, and weaker sensitivities to demand/temperature data resolution, data alignment, and methods for determining when buildings are occupied, with standard deviations of differences in shed estimates of ≈2–5 kW.

References

References
1.
Callaway
,
D.
, and
Hiskens
,
I.
,
2011
, “
Achieving Controllability of Electric Loads
,”
Proc. IEEE
,
99
(
1
), pp.
184
199
.10.1109/JPROC.2010.2081652
2.
DOE
,
2006
, “
Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them
,” Department of Energy Report to the U.S. Congress.
3.
Borenstein
,
S.
,
Jaske
,
M.
, and
Rosenfeld
,
A.
,
2002
, “
Dynamic Pricing, Advanced Metering and Demand Response in Electricity Markets
,” Technical Report No. CSEMWP-105, University of California Energy Institute: Center for the Study of Energy Markets.
4.
Fels
,
M.
,
1986
, “
PRISM: An Introduction
,”
Energy Build.
,
9
(
1–2
), pp.
5
18
.10.1016/0378-7788(86)90003-4
5.
Katipamula
,
S.
,
Reddy
,
T.
, and
Claridge
,
D.
,
1998
, “
Multivariate Regression Modeling
,”
ASME J. Sol. Energy Eng.
,
120
(
3
), pp.
177
184
.10.1115/1.2888067
6.
Kissock
,
J.
,
Reddy
,
T.
, and
Claridge
,
D.
,
1998
, “
Ambient-Temperature Regression Analysis for Estimating Retrofit Savings in Commercial Buildings
,”
ASME J. Sol. Energy Eng.
,
120
(
3
), pp.
168
176
.10.1115/1.2888066
7.
Kissock
,
J.
, and
Eger
,
C.
,
2008
, “
Measuring Industrial Energy Savings
,”
Appl. Energy
,
85
(
5
), pp.
347
361
.10.1016/j.apenergy.2007.06.020
8.
Ruch
,
D.
,
Kissock
,
J.
, and
Reddy
,
T.
,
1999
, “
Prediction Uncertainty of Linear Building Energy Use Models With Autocorrelated Residuals
,”
ASME J. Sol. Energy Eng.
,
121
(
1
), pp.
63
68
.10.1115/1.2888144
9.
Yang
,
J.
,
Rivard
,
H.
, and
Zmeureanu
,
R.
,
2005
, “
On-Line Building Energy Prediction Using Adaptive Artificial Neural Networks
,”
Energy Build.
,
37
(
12
), pp.
1250
1259
.10.1016/j.enbuild.2005.02.005
10.
Coughlin
,
K.
,
Piette
,
M.
,
Goldman
,
C.
, and
Kiliccote
,
S.
,
2009
, “
Statistical Analysis of Baseline Load Models for Non-Residential Buildings
,”
Energy Build.
,
41
(
4
), pp.
374
381
.10.1016/j.enbuild.2008.11.002
11.
Goldberg
,
M.
, and
Agnew
,
G.
,
2003
, “
Protocol Development for Demand Response Calculation–Findings and Recommendations
,” Technical Report No. CEC 400-02-017F, California Energy Commission (KEMA-XENERGY).
12.
Kozikowski
,
D.
,
Breidenbaugh
,
A.
, and
Potter
,
M.
,
2006
, “
The Demand Response Baseline, v.1.75
,” EnerNOC OPS Publication.
13.
Wi
,
Y.-M.
,
Kim
,
J.-H.
,
Joo
,
S.-K.
,
Park
,
J.-B.
, and
Oh
,
J.-C.
,
2009
, “
Customer Baseline Load (CBL) Calculation Using Exponential Smoothing Model With Weather Adjustment
,”
Transmission Distribution Conference Exposition: Asia and Pacific
, Seoul, Korea, Oct. 26–30, pp.
1
4
.10.1109/TD-ASIA.2009.5356984
14.
Mathieu
,
J.
,
Price
,
P.
,
Kiliccote
,
S.
, and
Piette
,
M.
,
2011
, “
Quantifying Changes in Building Electricity Use, With Application to Demand Response
,”
IEEE Trans. Smart Grid
,
2
(
3
), pp.
507
518
.10.1109/TSG.2011.2145010
15.
Addy
,
N.
,
Mathieu
,
J.
,
Kiliccote
,
S.
, and
Callaway
,
D.
,
2013
, “
Understanding the Effect of Baseline Modeling Implementation Choices on Analysis of Demand Response Performance
,”
ASME
Paper No. IMECE2012-86973.10.1115/IMECE2012-86973
16.
Mathieu
,
J. L.
,
Callaway
,
D. S.
, and
Kiliccote
,
S.
,
2011
, “
Variability in Automated Responses of Commercial Buildings and Industrial Facilities to Dynamic Electricity Prices
,”
Energy Build.
,
43
(
12
), pp.
3322
3330
.10.1016/j.enbuild.2011.08.020
17.
National Oceanic and Atmospheric Administration
,
2009
, “
National Climatic Data Center
,” Available at: http://www7.ncdc.noaa.gov/CDO/dataproduct
18.
The Weather Channel LLC
,
2012
, “
Weather Underground
,” Available at: http://weatherunderground.com
19.
Price
,
P.
,
2010
, “
Methods for Quantifying Electric Load Shape and Its Variability
,” Technical Report No. LBNL-3713E, Lawrence Berkeley National Laboratory.
20.
Pacific Gas and Electric Company
,
2010
, “
General Schedule A-10
,” Available at: http://www.pge.com/tariffs/tm2/pdf/ELEC_SCHEDS_A-10.pdf
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