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Energies 2018, 11(2), 407; https://doi.org/10.3390/en11020407

Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy

1
SH Urban Research Center, Seoul Housing & Communities Corporation, 621, Gaepo-ro, Gangnam-gu, Seoul 06336, Korea
2
Department of Architectural Engineering, Yonsei University, 50 Yonsei Street, Seodaemun-gu, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
Received: 29 December 2017 / Revised: 24 January 2018 / Accepted: 29 January 2018 / Published: 9 February 2018
(This article belongs to the Special Issue Control and Nonlinear Dynamics on Energy Conversion Systems)
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Abstract

The purpose of this study was to develop a data-driven predictive model that can predict the supply air temperature (SAT) in an air-handling unit (AHU) by using a neural network. A case study was selected, and AHU operational data from December 2015 to November 2016 was collected. A data-driven predictive model was generated through an evolving process that consisted of an initial model, an optimal model, and an adaptive model. In order to develop the optimal model, input variables, the number of neurons and hidden layers, and the period of the training data set were considered. Since AHU data changes over time, an adaptive model, which has the ability to actively cope with constantly changing data, was developed. This adaptive model determined the model with the lowest mean square error (MSE) of the 91 models, which had two hidden layers and sets up a 12-hour test set at every prediction. The adaptive model used recently collected data as training data and utilized the sliding window technique rather than the accumulative data method. Furthermore, additional testing was performed to validate the adaptive model using AHU data from another building. The final adaptive model predicts SAT to a root mean square error (RMSE) of less than 0.6 °C. View Full-Text
Keywords: data-driven; prediction; neural network; air-handling unit (AHU); supply air temperature data-driven; prediction; neural network; air-handling unit (AHU); supply air temperature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hong, G.; Kim, B.S. Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy. Energies 2018, 11, 407.

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