Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

Kim, Tae Young; Lee, Jong Man; Yoon, Yeobeom; Lee, Kwang Ho

doi:10.1007/s10765-021-02922-w

Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

Journal Article · Tue Oct 05 00:00:00 EDT 2021 · International Journal of Thermophysics

DOI:https://doi.org/10.1007/s10765-021-02922-w· OSTI ID:1883792

Kim, Tae Young ^[1]; Lee, Jong Man ^[1]; Yoon, Yeobeom ^[2]; Lee, Kwang Ho ^[1]

Korea University, Seoul, (Korea, Republic of)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Here, in this study, real-time predictive control and optimization model based on an ANN (artificial neural network) was developed to evaluate the cooling energy saving performance of the optimized control of CndWT (condenser water temperature). For this purpose, the difference in TCEC (total cooling energy consumption) between the conventional control strategy when the CndWT produced by the cooling tower is fixed and the optimized control strategy when real-time control of the CndWT through the optimal ANN model is applied was compared and analyzed. For the modeling of the building to be simulated, the co-simulation of EnergyPlus and MATLAB was built through the middleware Building Controls Virtual Test Bed. For the prediction of TCEC, an ANN model was developed through MATLAB's neural network toolbox. The model accuracy of the ANN was examined through Cv(RMSE) index and as a result, Cv(RMSE) of the optimized ANN model turned out to be approximately 25 %. More importantly, the predictive control technique was able to save TCEC by 5.6 % compared to the conventional control method constantly fixing CndWT set-point to 30 °C. These results showed that the CndWT needs to be dynamically controlled using artificial intelligence technique such as ANN model and that significant energy savings were achievable compared to the conventional fixed control.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Ministry of Trade, Industry & Energy (MOTIE, Korea); National Research Foundation of Korea (NRF); USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1883792

Journal Information:: International Journal of Thermophysics, Journal Name: International Journal of Thermophysics Journal Issue: 12 Vol. 42; ISSN 0195-928X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (12)

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42 ENGINEERING
ANN
CndWT
EnergyPlus
artificial neural network
condenser water temperature
cooling energy cooling tower

Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

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