BUILDING ENERGY USE PREDICTION AND SYSTEM-IDENTIFICATION USING RECURRENT NEURAL NETWORKS Academic Article uri icon

abstract

  • 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.

published proceedings

  • JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME

author list (cited authors)

  • KREIDER, J. F., CLARIDGE, D. E., CURTISS, P., DODIER, R., HABERT, J. S., & KRARTI, M.

citation count

  • 54

complete list of authors

  • KREIDER, JF||CLARIDGE, DE||CURTISS, P||DODIER, R||HABERT, JS||KRARTI, M

publication date

  • August 1995