<p>With the development of civilization, the energy consumption of mankind is rapidly increasing, and thus mankind needs more energy. According to the IEA(International Energy Agency)'s “The Future of Cooling” report, global energy demand has increased at the fastest rate in the past decade as economic development and regional cooling and heating demand have increased. In this study, an artificial neural network (ANN) based real-time predictive control and optimization algorithm for a chiller-based cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. To evaluate the algorithm performance, we compared and analyzed the electric consumption and the COP when the chilled and condenser water temperatures were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data were observed&nbsp;due to&nbsp;insufficient training data and a limited consideration of the outdoor air wet-bulb temperature when determining the condenser water set-point. Therefore, it is necessary to build training data under a wide range of conditions and to set the condenser water and chilled water temperature lower limit to be outdoor air wet-bulb temperature +3.6℃ in the outdoor wet-bulb temperature region above 23℃, so that further energy savings can be achieved.</p>

Upper and Lower Threshold Limit of Chilled and Condenser Water Temperature Set-Points During Ann Based Optimized Control

Paper Proceedings: https://asmedigitalcollection.asme.org/ES/ES2021/volume/84881

ASME 2021 Energy Sustainability (ES2021)

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