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.
Building Energy Use Prediction and System Identification Using Recurrent Neural Networks
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Kreider, J. F., Claridge, D. E., Curtiss, P., Dodier, R., Haberl, J. S., and Krarti, M. (August 1, 1995). "Building Energy Use Prediction and System Identification Using Recurrent Neural Networks." ASME. J. Sol. Energy Eng. August 1995; 117(3): 161–166. https://doi.org/10.1115/1.2847757
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