Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles

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This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program ... continued below

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Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi & Kiliccote, Sila June 28, 2009.

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This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.

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  • IEEE-PES/IAS Conference on Sustainable Alternative Energy , Valencia, Spain, September 28-30, 2009

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  • Report No.: LBNL-2259E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 970816
  • Archival Resource Key: ark:/67531/metadc926912

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  • June 28, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Jan. 4, 2017, 5:59 p.m.

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Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi & Kiliccote, Sila. Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles, article, June 28, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc926912/: accessed April 26, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.