Title :
Regression models for demand reduction based on cluster analysis of load profiles
Author :
Yamaguchi, Nobuyuki ; Han, Junqiao ; Ghatikar, Girish ; Kiliccote, Sila ; Piette, Mary Ann ; Asano, Hiroshi
Author_Institution :
Central Res. Inst. of Electr. Power Ind., Komae, Japan
Abstract :
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.
Keywords :
pattern clustering; pricing; regression analysis; cluster analysis; critical peak pricing program; customer baseline load; customer enrollment; demand reduction; demand response programs; electric company commercial-industrial customers; ex ante evaluation; load profiles; load sensitivity; pacific gas; regression models; Electrical equipment industry; Gas industry; Job shop scheduling; Laboratories; Load management; Load modeling; Pattern analysis; Pricing; Recruitment; Temperature sensors; Automated Demand Response; Cluster Analysis; Critical Peak Pricing; Demand Reduction; K-means; Regression Model; Sensitivity to Outside Air Temperature;
Conference_Titel :
Sustainable Alternative Energy (SAE), 2009 IEEE PES/IAS Conference on
Conference_Location :
Valencia
Print_ISBN :
978-1-4244-4430-4
Electronic_ISBN :
978-1-4244-4431-1
DOI :
10.1109/SAE.2009.5534840
