Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms

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Publication Type journal article
School or College College of Engineering
Department Mechanical Engineering
Creator Rahman, Aowabin
Other Author Smith, Amanda D.
Title Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms
Date 2018
Description This paper evaluates the performance of deep recurrent neural networks in predicting heating demand for a commercial building over a medium-to-long term time horizon (≥ 1 week), and proposes a modeling framework to demonstrate how these longer-term predictions can be used to aid design of a stratified thermal storage tank. The building sector contributes significantly to primary energy consumption in the U.S, and as such, there is a need to predict heating demand in buildings over longer time horizons, and to develop methods that can facilitate installation, planning and management of distributed generation and thermal storage to meet these heating demands. Key objectives of this paper are: (a) Investigate how a deep recurrent neural network model performs in predicting heating demand in campus buildings at University of Utah over multiple weeks, and (b) Develop an optimization framework that which can provide definitive guidelines on sizing a stratified thermal storage tank without requiring high performance computing resources. The results showed that the predictions by the deep RNN are comparatively more accurate than those by a 3-layer MLP, and that these deep RNN predictions can adequately serve as proxy for future demand while considering sizing in the design of a complementary stratified thermal storage tank.
Type Text
Publisher Elsevier
Journal Title Applied Energy
Volume 228
First Page 108
Last Page 121
DOI https://doi.org/10.1016/j.apenergy.2018.06.064
Subject Building Energy Modeling; Machine Learning; Recurrent Neural Networks; Deep Learning; Heating Load Prediction; Thermal Energy Storage
Language eng
Bibliographic Citation A. Rahman and A.D. Smith, 2018. Predicting Heating Demand and Sizing a Stratified Thermal Storage Tank using Machine Learning Algorithms, Applied Energy, Volume 228, pp. 108-121. https://doi.org/10.1016/j.apenergy.2018.06.064
Rights Management (c) Aowabin Rahman, Amanda D. Smith
Format Medium application/pdf
ARK ark:/87278/s6061mgp
Setname ir_uspace
Date Created 2018-07-12
Date Modified 2021-05-06
ID 1348676
Reference URL https://collections.lib.utah.edu/ark:/87278/s6061mgp