Evaluation of the causes and impact of outliers on residential building energy use prediction using inverse modeling

2018 ◽  
Vol 138 ◽  
pp. 194-206 ◽  
Author(s):  
Huyen Do ◽  
Kristen S. Cetin
Keyword(s):  
Inverse Modeling ◽  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Use ◽  
Energy Use Prediction

Detection and interpretation of anomalies in building energy use through inverse modeling

2019 ◽  
Vol 25 (4) ◽  
pp. 488-503 ◽  
Author(s):  
H. Burak Gunay ◽  
Weiming Shen ◽  
Guy Newsham ◽  
Araz Ashouri
Keyword(s):  
Inverse Modeling ◽  
Energy Use ◽  
Building Energy ◽  
Building Energy Use

Building Energy Use Prediction and System Identification Using Recurrent Neural Networks

1995 ◽  
Vol 117 (3) ◽  
pp. 161-166 ◽  
Author(s):  
J. F. Kreider ◽  
D. E. Claridge ◽  
P. Curtiss ◽  
R. Dodier ◽  
J. S. Haberl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Feedforward Neural Networks ◽  
Building Energy ◽  
Difficult Problem ◽  
Energy Prediction ◽  
Building Energy Use ◽  
Hourly Data ◽  
Energy Use Prediction

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.

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