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Type: Conference paper
Title: Multi-zone temperature prediction in a commercial building using artificial neural network model
Author: Huang, H.
Chen, L.
Mohammadzaheri, M.
Hu, E.
Chen, M.
Citation: Proceedings of the 2013 10th IEEE International Conference on Control and Automation, ICCA 2013: pp.1896-1901
Publisher: IEEE
Publisher Place: USA
Issue Date: 2013
Series/Report no.: IEEE International Conference on Control and Automation ICCA
ISBN: 9781467347082
ISSN: 1948-3449
Conference Name: IEEE International Conference on Control and Automation (10th : 2013 : Hangzhou, China)
Statement of
Hao Huang, Lei Chen, Eric Hu, and Minlei Chen
Abstract: Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies. © 2013 IEEE.
Keywords: HVAC
Model predictive control
Artificial neural network
Rights: ©2013 IEEE
DOI: 10.1109/ICCA.2013.6565010
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Appears in Collections:Aurora harvest 4
Mechanical Engineering conference papers

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