Prediction of electric power consumption for commercial buildings

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 666-672

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

^a University of Minnesota   (United States)

^b NorthWrite   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING METHODS; COMMERCIAL BUILDING; COMPUTATIONAL INTELLIGENCE TECHNIQUES; DEMAND RESPONSE; ELECTRIC POWER CONSUMPTION; EMPIRICAL COMPARISON; FUNCTION OF TIME; GOVERNMENT BUILDINGS; HISTORICAL DATA; MODELING POWER; PREDICTION ACCURACY; REAL LIFE DATA; REAL TIME PRICING; SMART GRID;

ELECTRIC UTILITIES; ENERGY EFFICIENCY; ENERGY UTILIZATION; FORECASTING; NEURAL NETWORKS; OFFICE BUILDINGS;

SMART POWER GRIDS;

EID: 80054725333     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033285     Document Type: Conference Paper

References (6)

1. Granderson, Jessica, Piette, M.A., Ghatikar, G., Price P. Building Energy Information Systems: State of Technology and User Case Studies. Lawrence Berkeley National Laboratory. LBNL-2899E. 2009. [Online] http://eis.lbl.gov/ pubs/lbnl-2899e.pdf
2. NorthWrite, Inc. [Online] http://www.northwrite.com/
3. EnergyExpert [Online]:http://www.northwrite.com/pdf/ appovvwenergyexpertv1.1.pdf
4. Bramley, M.R., Katipamula, S. and O'Neill, P., Diagnostics for Monitoring-Based Commissioning, National Conf. on Building Commissioning, June 2009
5. nd ed., John Wiley & Sons, Inc., 2007
6. U.S. Office of Personal Management: 2011 Federal holidays. [Online]:http://www.opm.gov/fedhol/index.htm