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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Grahic Jump Location
A χ2-distribution with the mean and critical values of W indicated. The tail mass shown here is α=0.001.
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Short-term normalized autocovariance functions, at lags from 1 to 24 hours. At left, from the top downwards, there are temperature, insolation, and wind.
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Long-term normalized autocovariance function of ambient temperature, at lags from 1 hour to 50 days
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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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