Commercial buildings have a significant impact on energy and the environment, being responsible for more than 18% of the annual primary energy consumption in the United States. Analyzing their electrical demand profiles is necessary for the assessment of supply-demand interactions and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the smart grid context. This research developed and applied unsupervised classification of commercial buildings according to their electrical demand profile. A Department of Energy (DOE) database was employed, containing electrical demand profiles representing the United States commercial building stock as detailed in the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the EnergyPlus building energy simulation tool. The essence of the approach was: (1) discrete wavelet transformation of the electrical demand profiles, (2) energy and entropy feature extraction (absolute and relative) from the wavelet levels at definitive time frames, and (3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yielded a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.

References

References
1.
Lee
,
K.
, and
Braun
,
J.
,
2006
, “
An Experimental Evaluation of Demand Limiting Using Building Thermal Mass in a Small Commercial Building
,” Winter Meeting of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), Chicago, IL, January 21–25,
ASHRAE Trans.
,
112
, pp.
559
571
.
2.
Henze
,
G.
,
Le
,
T.
,
Florita
,
A.
, and
Felsmann
,
C.
,
2007
, “
Sensitivity Analysis of Optimal Building Thermal Mass Control
,”
ASME J. Solar Energ. Eng.
,
129
(
4
), pp.
473
485
.10.1115/1.2770755
3.
Bahnfleth
,
W.
, and
Joyce
,
W.
,
1994
, “
Energy Use in a District Cooling System With Stratified Chilled-Water Storage
,” Proceedings of the ASHRAE Winter Meeting, New Orleans, LA, January 23–26,
ASHRAE Trans.
,
100
(
1
), pp.
1767
1778
.
4.
Sohn
,
C.
,
Fuchs
,
J.
, and
Gruber
,
M.
,
1999
, “
Chilled Water Storage Cooling System for an Army Installation
,” ASHRAE Annual Meeting, Seattle, WA, June 18–23,
ASHRAE Trans.
,
105
(
2
), pp.
1126
1133
.
5.
Henze
,
G.
,
2003
, “
An Overview of Optimal Control for Central Cooling Plants With Ice Thermal Energy Storage
,”
ASME J. Solar Energ. Eng.
,
125
(
3
), pp.
302
309
.10.1115/1.1591801
6.
Braun
,
J.
,
2007
, “
A Near-Optimal Control Strategy for Cool Storage Systems With Dynamic Electric Rates (RP-1252)
,”
HVACR Res.
,
13
(
4
), pp.
557
580
.10.1080/10789669.2007.10390972
7.
Cavallo
,
A.
,
2001
, “
Energy Storage Technologies for Utility Scale Intermittent Renewable Energy Systems
,”
ASME J. Solar Energ. Eng.
,
123
(
4
), pp.
387
389
.10.1115/1.1409556
8.
Kempton
,
W.
, and
Tomic
,
J.
,
2005
,“
Vehicle-to-Grid Power Implementation: From Stabilizing the Grid to Supporting Large-Scale Renewable Energy
,”
J. Power Source.
,
144
(
1
), pp.
280
294
.10.1016/j.jpowsour.2004.12.022
9.
DOE-OEI
,
2010
, “
Open Energy Information: Commercial Building Profiles. U.S. Department of Energy
,” accessed January 2012, //en.openei.org/datasets/node/41
10.
DOE-EIA
,
2006
, “
2003 Commercial Buildings Energy Consumption Survey. U.S. Department of Energy: Energy Information Administration
,” accessed January 2012, www.eia.doe.gov/emeu/cbecs/cbecs2003
11.
DOE-EP
,
2007
, “
EnergyPlus Version 2.0. U.S. Department of Energy
,” accessed January 2012, www.energyplus.gov
12.
Griffith
,
B.
,
Long
,
N.
,
Torcellini
,
P.
,
Judkoff
,
R.
,
Crawley
,
D.
, and
Ryan
,
J.
,
2008
, “
Methodology for Modeling Building Energy Performance Across the Commercial Sector
,” National Renewable Energy Laboratory, Golden, CO, Technical Report NREL/TP-550-41956, March, www.nrel.gov/docs/fy08osti/41956.pdf
13.
Percival
,
D.
, and
Walden
,
A.
,
2000
,
Wavelet Methods for Time Series Analysis
,
Cambridge University Press
, New York.
14.
Mallat
,
S.
,
1989
, “
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
,”
IEEE T. Pattern Anal.
,
11
(
7
), pp.
674
693
.10.1109/34.192463
15.
Heller
,
K.
, and
Ghahramani
,
Z.
,
2005
, “
Bayesian Hierarchical Clustering
,”
Proceedings of the 22nd International Conference on Machine Learning
, pp.
297
304
.
16.
R Development Core Team
,
2010
,
R: A Language and Environment for Statistical Computing
,
R Foundation for Statistical Computing
,
Vienna, Austria
.
You do not currently have access to this content.