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

• DOI: 10.1016/j.egyr.2023.05.263
• Publication Date: 2023-06-10T18:36:43Z
• Authors: Sang Hun Yeon; Yeobeom Yoon; Won Hee Kang; Je Hyeon Lee; Kwan Woo Song; Young Tae Chae; Jong Min Choi; Kwang Ho Lee
• Keywords: CndWT (Condenser water temperature); ChWT (Chilled water temperature); ANN (Artificial neural network); In-situ application; OWBT (outdoor air wet-bulb temperature); 0202 electrical engineering, electronic engineering, information engineering; Electrical engineering. Electronics. Nuclear engineering; 02 engineering and technology; TK1-9971

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.