Artificial neural network based optimized control of condenser water temperature set-point

Tae Young Kim, Jong Man Lee, Sung Hyup Hong, Jong Min Choi, Kwang Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this study, we developed an artificial neural network based real-time predictive control and optimization model to compare and analyze the difference in total energy consumption when the condenser water outlet temperature coming out of the cooling tower is fixed and when real-time control of the condenser water outlet temperature through the optimal ANN model is applied. An ANN model was developed through MATLAB's built-in neural network toolbox functionality to predict total energy consumption. The model accuracy of the ANN was examined by applying Cv(RMSE), a statistical concept that shows the overall accuracy of the predicted values, and as a result, it was found to have a Cv(RMSE) value of approximately 25%. In addition, the predictive control algorithm was able to reduce cooling energy consumption by about 5.6% compared to the conventional control strategy that fix condenser water temperature set-point to constantly 30°C.

Original languageEnglish
Title of host publicationProceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884881
DOIs
Publication statusPublished - 2021
EventASME 2021 15th International Conference on Energy Sustainability, ES 2021 - Virtual, Online
Duration: 2021 Jun 162021 Jun 18

Publication series

NameProceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021

Conference

ConferenceASME 2021 15th International Conference on Energy Sustainability, ES 2021
CityVirtual, Online
Period21/6/1621/6/18

Keywords

  • ANN (artificial neural network)
  • BCVTB (building controls virtual test bed)
  • Chiller
  • Condenser water temperature
  • Cooling tower
  • EnergyPlus
  • HVAC (heating, ventilation, and air conditioning) system
  • MATLAB

ASJC Scopus subject areas

  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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