Research Papers

How Baseline Model Implementation Choices Affect Demand Response Assessments

[+] Author and Article Information
Nathan J. Addy

2557 Glen Ave.,
Berkeley, CA 94709
e-mail: nathan.addy@gmail.com

Sila Kiliccote

Environmental Energy Technologies Division,
Lawrence Berkeley National Laboratory,
1 Cyclotron Rd., MS 90R1121,
Berkeley, CA 94720
e-mail: skiliccote@lbl.gov

Duncan S. Callaway

Assistant Professor
Energy and Resources Group,
University of California, Berkeley,
310 Barrows Hall,
Berkeley, CA 94720-3050
e-mail: dcal@berkeley.edu

Johanna L. Mathieu

Assistant Professor
Department of Electrical Engineering
and Computer Science,
1301 Beal Ave.,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jlmath@umich.edu

1N. J. Addy was formally with the Environmental Energy Technologies Division at Lawrence Berkeley National Laboratory, which is where the majority of this work was conducted.

2Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received June 6, 2014; final manuscript received August 22, 2014; published online September 30, 2014. Assoc. Editor: Gregor P. Henze. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Sol. Energy Eng 137(2), 021008 (Sep 30, 2014) (6 pages) Paper No: SOL-14-1171; doi: 10.1115/1.4028478 History: Received June 06, 2014; Revised August 22, 2014

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.

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Grahic Jump Location
Fig. 1

Effect of outdoor air temperature data source. Base: NCDC data, variant: weather underground data. Std. dev.: 23.36 kW.

Grahic Jump Location
Fig. 2

Effect of data resolution. Base: 15-min interval data, variants: 30 - and 60-min data. Std dev.: 2.22 and 4.59 kW.

Grahic Jump Location
Fig. 3

Effect of method to detect occupied/unoccupied mode transitions. Base: manual selection, variant: automated algorithm. Std dev.: 2.39 kW.

Grahic Jump Location
Fig. 4

Effect of data alignment. Base: 15-min offset, variant: no offset. Std dev.: 4.59 kW.

Grahic Jump Location
Fig. 5

Effect of power outage filter. Base: original filter, variants: no filter and sensitive filter. Std dev.: 30.08 and 18.55 kW

Grahic Jump Location
Fig. 6

Box plot of the mismatch in each variant case



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