Deep recurrent neural networks for building energy prediction

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Publication Type poster
School or College College of Engineering
Department Mechanical Engineering
Creator Rahman, Aowabin
Other Author Smith, Amanda D.
Title Deep recurrent neural networks for building energy prediction
Date 2017-01-13
Description This poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week).
Type Text
Publisher University of Utah
Subject Machine learning; Energy; Building energy modeling; Deep learning; Recurrent neural networks; Prediction
Language eng
Conference Title Utah Science Day, University of Utah, Salt Lake City, UT
Rights Management (c) Aowabin Rahman, Amanda D. Smith
Format Medium application/pdf
ARK ark:/87278/s68h2r92
Setname ir_uspace
Date Created 2018-07-13
Date Modified 2018-07-13
ID 1349230
Reference URL https://collections.lib.utah.edu/ark:/87278/s68h2r92
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