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Understanding the Effect of Baseline Modeling Implementation Choices on Analysis of Demand Response Performance

[+] Author Affiliations
Nathan Addy, Sila Kiliccote

Lawrence Berkeley National Lab, Berkeley, CA

Johanna L. Mathieu

ETH Zürich, Zurich, Switzerland

Duncan S. Callaway

University of California, Berkeley, Berkeley, CA

Paper No. IMECE2013-63717, pp. V011T06A024; 8 pages
  • ASME 2013 International Mechanical Engineering Congress and Exposition
  • Volume 11: Emerging Technologies
  • San Diego, California, USA, November 15–21, 2013
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5640-6
  • Copyright © 2013 by ASME


Accurate evaluation of the performance of buildings participating in Demand Response (DR) programs is critical to the adoption and improvement of these programs. Typically, we calculate load sheds during DR events by comparing observed electric load against counterfactual predictions made using statistical baseline models. Many baseline models exist and these models can produce different shed estimates. Moreover, modelers implementing the same baseline model can make different modeling implementation choices, which may affect shed estimates. In this work, using real data, we analyze the effect of different modeling implementation choices on shed estimates. We focus on five issues: weather data source, resolution of data, methods for determining when buildings are occupied, methods for aligning building data with temperature data, and methods for power outage filtering. Results indicate sensitivity to the weather data source and data filtration methods as well as an immediate potential for automation of methods to choose building occupied modes.

Copyright © 2013 by ASME
Topics: Modeling



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