Building Energy Use Prediction and System Identification Using Recurrent Neural Networks
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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. © 1995 by ASME.
author list (cited authors)
Kreider, J. F., Claridge, D. E., Curtiss, P., Dodier, R., Haberl, J. S., & Krarti, M.