@inproceedings{25772,
  keywords = {demand response and distributed energy resources center, demand response research center, automated demand response, cluster analysis, critical peak pricing, demand reduction, k-means, regression model, sensitivity to outside air temperature},
  author = {Nobuyuki Yamaguchi and Junqiao Han Dudley and Girish Ghatikar and Sila Kiliccote and Mary Ann Piette and Hiroshi Asano and Junqiao Han},
  title = {Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles},
  abstract = {<p>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 explanatoryvariables.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.</p>},
  year = {2009},
  journal = {IEEE-PES/IAS Conference on Sustainable Alternative Energy},
  address = {Valencia, Spain},
  language = {eng},
}