ANN based optimized AHU discharge air temperature control of conventional VAV system for minimized cooling energy in an office building

Jong Man Lee, Won Hee Kang, Kwang Ho Lee

Research output: Contribution to journalConference articlepeer-review

Abstract

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℃.

Original languageEnglish
Article number05014
JournalE3S Web of Conferences
Volume111
DOIs
Publication statusPublished - 2019 Aug 13
Externally publishedYes
Event13th REHVA World Congress, CLIMA 2019 - Bucharest, Romania
Duration: 2019 May 262019 May 29

ASJC Scopus subject areas

  • Environmental Science(all)
  • Energy(all)
  • Earth and Planetary Sciences(all)

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