Prediction of electric power consumption for commercial buildings

Vladimir Cherkassky, Sohini Roy Chowdhury, Volker Landenberger, Saurabh Tewari, Paul Bursch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Currently many commercial buildings are not continuously monitored for energy consumption, especially small buildings which constitute 90% of all such buildings. However, readily available data from the electric meters can be used for monitoring and analyzing energy consumption. Efficient utilization of available historical data (from these meters) can potentially improve energy efficiency, help to identify common energy wasting problems, and, in the future, enable various Smart Grid programs, such as demand response, real-time pricing etc. This paper describes application of computational intelligence techniques for prediction of electric power consumption. The proposed approach combines regression and clustering methods, in order to improve the prediction accuracy of power consumption, as a function of time (of the day) and temperature, using real-life data from several commercial and government buildings. Empirical comparisons show that the proposed approach provides an improvement over the currently used bin-based method for modeling power consumption.

Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages666-672
Number of pages7
DOIs
StatePublished - Oct 24 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: Jul 31 2011Aug 5 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CountryUnited States
CitySan Jose, CA
Period7/31/118/5/11

Fingerprint Dive into the research topics of 'Prediction of electric power consumption for commercial buildings'. Together they form a unique fingerprint.

Cite this