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: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication16th International Conference of the International Building Performance Simulation Association, Building Simulation 2019
EditorsVincenzo Corrado, Enrico Fabrizio, Andrea Gasparella, Francesco Patuzzi
PublisherInternational Building Performance Simulation Association
Pages1802-1808
Number of pages7
ISBN (Electronic)9781713809418
Publication statusPublished - 2019
Event16th International Conference of the International Building Performance Simulation Association, Building Simulation 2019 - Rome, Italy
Duration: 2019 Sep 22019 Sep 4

Publication series

NameBuilding Simulation Conference Proceedings
Volume3
ISSN (Print)2522-2708

Conference

Conference16th International Conference of the International Building Performance Simulation Association, Building Simulation 2019
Country/TerritoryItaly
CityRome
Period19/9/219/9/4

ASJC Scopus subject areas

  • Building and Construction
  • Architecture
  • Modelling and Simulation
  • Computer Science Applications

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