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TECHNICAL PAPERS

Statistical Analysis of Neural Networks as Applied to Building Energy Prediction

[+] Author and Article Information
Robert H. Dodier

60 South Boulder Circle #6301, Boulder, CO 80303 USA

Gregor P. Henze

Architectural Engineering, University of Nebraska–Lincoln, Peter Kiewit Institute, 1110 South 67th Street, Omaha, Nebraska 68182-0681 USA

J. Sol. Energy Eng 126(1), 592-600 (Feb 12, 2004) (9 pages) doi:10.1115/1.1637640 History: Received April 01, 2002; Revised May 01, 2003; Online February 12, 2004
Copyright © 2004 by ASME
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References

Haberl,  J., and Thamilseran,  S., 1996, “Predicting Hourly Building Energy Use: The Great Energy Predictor Shootout II: Measuring Retrofit Savings,” ASHRAE Trans., 102, Part 2.
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Ohlsson,  M., Peterson,  C., Pi,  H., Rognvaldsson,  T., and Soderberg,  B., 1994, “Predicting System Loads with Artificial Neural Networks,” ASHRAE Trans., 100(2), pp. 1063–1074.
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Raftery,  A., Madigan,  D., and Hoeting,  J. A., 1997, “Bayesian Model Averaging for Linear Regression Models,” J American Statistical Assoc, 437, pp. 179–191.

Figures

Grahic Jump Location
A χ2-distribution with the mean and critical values of W indicated. The tail mass shown here is α=0.001.
Grahic Jump Location
Short-term normalized autocovariance functions, at lags from 1 to 24 hours. At left, from the top downwards, there are temperature, insolation, and wind.
Grahic Jump Location
Long-term normalized autocovariance function of ambient temperature, at lags from 1 hour to 50 days
Grahic Jump Location
Target values (solid line) of CHW, Business Building, compared to predictions (dotted line) made by a network. When a prediction is too high or too low, the next prediction tends to be too high or too low also.

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