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Results for “CndWT (Condenser water temperature)”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system

In this study, an ANN (artificial neural network) based real-time optimized control algorithm for a chilled water cooling system was developed and applied in an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, the cooling tower’s CndWT (condenser water temperature) and the chiller’s ChWT (chilled water temperature) were set as system control variables. To evaluate algorithm performance, the electric consumption and the COP (coefficient of performance) were compared and analyzed when ChWT and CndWTs were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data was observed due to insufficient training data and limited consideration of OWBT (outdoor air wet-bulb temperature) when determining the CndWT set-point. Therefore, it is necessary to further build training data from a wider range of conditions and to set the lower limit of CndWT set-point to at least +3.6 °C above OWBT when the OWBT is higher than 23 °C, so that further energy savings can be achieved.

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DOE OSTI2021

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

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.

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