Building Energy Use Prediction and System Identification Using Recurrent Neural Networks

[+] Author and Article Information
J. F. Kreider, P. Curtiss, R. Dodier, M. Krarti

JCEM, CEAE Department, University of Colorado, Boulder, CO 80309

D. E. Claridge, J. S. Haberl

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123

J. Sol. Energy Eng 117(3), 161-166 (Aug 01, 1995) (6 pages) doi:10.1115/1.2847757 History: Received December 01, 1994; Revised April 01, 1995; Online February 14, 2008


Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem (Wang and Kreider, 1992; JCEM, 1992, 1993; Curtiss et al., 1993, 1994; Anstett and Kreider, 1993; Kreider and Haberl, 1994) a more difficult problem has been addressed recently: namely, the prediction of building energy consumption well into the future without knowledge of immediately past energy consumption. This paper will report results on a recent study of six months of hourly data recorded at the Zachry Engineering Center (ZEC) in College Station, TX. Also reported are results on finding the R and C values for buildings from networks trained on building data.

Copyright © 1995 by The American Society of Mechanical Engineers
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