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Research Papers

Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

[+] Author and Article Information
Anthony R. Florita

e-mail: anthony.florita@nrel.gov

Larry J. Brackney

Electricity, Resources, and Building
Systems Integration Center,
National Renewable Energy Laboratory,
Golden, CO 80401

Todd P. Otanicar

Department of Mechanical Engineering,
The University of Tulsa,
Tulsa, OK 74104

Jeffrey Robertson

Department of Mechanical Engineering,
Loyola Marymount University,
Los Angeles, CA 90045

1Corresponding author.

Contributed by Solar Energy Division of ASME for publication in the Journal of Solar Energy Engineering. Manuscript received July 27, 2012; final manuscript received January 31, 2013; published online June 11, 2013. Assoc. Editor: Gregor P. Henze.

J. Sol. Energy Eng 135(3), 031020 (Jun 11, 2013) (10 pages) Paper No: SOL-12-1187; doi: 10.1115/1.4024029 History: Received July 27, 2012; Revised January 31, 2013

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.

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Figures

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Fig. 1

The discrete wavelet transformation (DWT) performed using a filter bank for multiresolution analysis of the electrical demand profiles

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Fig. 2

The first three levels of a DWT using the Daubechies-4 wavelet: the magnitude at a given time indicates the correlation strength at that level; units are not included for visual clarity

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Fig. 3

Two typical building electrical demand profiles and their relative wavelet energy comparison

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Fig. 4

Frequency plot of total wavelet entropy for each of the 4820 commercial buildings

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Fig. 5

Dendrogram of the clustering process for the United States commercial building stock: visualization of the 4820 buildings merging into the final 114 clusters (building classes) according to the 25 features extracted from the electrical demand profiles; for visual clarity building labels not included.

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Fig. 6

Limited portion of dendrogram (with focus on clusters 8 and 9) demonstrating merging hierarchy; dashed lines are merges not made, while the number displayed indicated log odds for merging

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Fig. 7

Clustering heat map based on United States climate zones: CBECS uses five climate regions based on cooling and heating degree days. Darkest rectangles represent a cluster with a small number of buildings (or zero), while white rectangles represent a cluster with a large number.

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Fig. 8

Clustering heat map based on principal building activity: CBECS classifies buildings according to their major usage, e.g., office, retail, etc. Darkest rectangles represent a cluster with a small number of buildings (or zero), while white rectangles represent a cluster with a large number.

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Fig. 9

Clustering heat map based on square footage. Darkest rectangles represent a cluster with a small number of buildings (or zero), while white rectangles represent a cluster with a large number.

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Fig. 10

The building demand profile, on-site PVE generation and difference for the month of July

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Fig. 11

Energy spillage and peak demand savings as a function of energy storage parameters

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