@inproceedings{84d8d5b8217b412ca0d6e7b393cace27,
title = "ANN based optimized AHU discharge air temperature control of conventional VAV system for minimized cooling energy in an office building",
abstract = "This paper assesses the energy performance of applying optimal control of air handling unit (AHU) discharge air temperature (DAT). An artificial neural network (ANN) model was used through the link between Matlab and EnergyPlus via BCVTB to realize automatic and optimal control of the AHUDAT. As a result of this study, the predictive control algorithm was able to significantly reduce cooling energy by approximately 10%, compared to a conventional control strategy of fixing AHUDAT to 14°C. These findings suggest that the ANN model and the control algorithm showed energy saving potential for various types of forced air systems by taking dynamic operating conditions into account.",
author = "Lee, {Jong Man} and Kang, {Won Hee} and Lee, {Kwang Ho}",
note = "Publisher Copyright: {\textcopyright} 2019 Building Simulation Conference Proceedings. All rights reserved.; 16th International Conference of the International Building Performance Simulation Association, Building Simulation 2019 ; Conference date: 02-09-2019 Through 04-09-2019",
year = "2019",
language = "English",
series = "Building Simulation Conference Proceedings",
publisher = "International Building Performance Simulation Association",
pages = "1802--1808",
editor = "Vincenzo Corrado and Enrico Fabrizio and Andrea Gasparella and Francesco Patuzzi",
booktitle = "16th International Conference of the International Building Performance Simulation Association, Building Simulation 2019",
}