Title :
Prediction of electric power consumption for commercial buildings
Author :
Cherkassky, Vladimir ; Chowdhury, Sohini Roy ; Landenberger, Volker ; Tewari, Saurabh ; Bursch, Paul
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
fDate :
July 31 2011-Aug. 5 2011
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.
Keywords :
artificial intelligence; building management systems; power consumption; power engineering computing; regression analysis; bin-based method; clustering method; commercial buildings; computational intelligence techniques; demand response; electric meters; electric power consumption prediction; energy consumption monitoring; energy efficiency; energy wasting problems; government buildings; power consumption modeling; real-life data; real-time pricing; regression method; smart grid programs; Buildings; Data models; Energy consumption; Government; Load modeling; Predictive models; Training data;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033285