TY - JOUR
T1 - ANN based optimized AHU discharge air temperature control of conventional VAV system for minimized cooling energy in an office building
AU - Lee, Jong Man
AU - Kang, Won Hee
AU - Lee, Kwang Ho
N1 - Funding Information:
This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government Ministry of Trade, Industry & Energy (No. 20184030201900)
PY - 2019/8/13
Y1 - 2019/8/13
N2 - In most conventional forced-air systems, the guidelines for the air handling unit(AHU) discharge air temperature(DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set-point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set-points for the AHU DAT. Three-story office building was modeled using co-simulation technique between EnergyPlus and Matlab via BCVTB(Building Controls Virtual Test Bed). In addition, artificial neural network(ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time-step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set-points of the AHU DAT, the control algorithm can determine the most energy-effective AHU DAT set-point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24%. In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10%, compared to a conventional control strategy of fixing AHU DAT to 14℃.
AB - In most conventional forced-air systems, the guidelines for the air handling unit(AHU) discharge air temperature(DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set-point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set-points for the AHU DAT. Three-story office building was modeled using co-simulation technique between EnergyPlus and Matlab via BCVTB(Building Controls Virtual Test Bed). In addition, artificial neural network(ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time-step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set-points of the AHU DAT, the control algorithm can determine the most energy-effective AHU DAT set-point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24%. In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10%, compared to a conventional control strategy of fixing AHU DAT to 14℃.
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U2 - 10.1051/e3sconf/201911105014
DO - 10.1051/e3sconf/201911105014
M3 - Conference article
AN - SCOPUS:85071859354
VL - 111
JO - E3S Web of Conferences
JF - E3S Web of Conferences
SN - 2555-0403
M1 - 05014
T2 - 13th REHVA World Congress, CLIMA 2019
Y2 - 26 May 2019 through 29 May 2019
ER -
