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In-situ application of an ANN algorithm for optimized chilled and condenser water temperatures set-point during cooling operation

机译：一种ANN算法的原位应用在冷却操作期间优化冷凝和冷凝器水温设定点

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© 2020 Elsevier B.V.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. As a result, the ANN model's accuracy was high, with a Cv(RMSE) of 4.9%. In addition, the ANN based control algorithm's energy analysis showed that the average energy saving rate for the chiller was 24.7% and that the total average energy saving effect for the chiller and cooling towers was 7.4%. The results confirmed that the proposed MPC algorithm could contribute to improved HVAC energy efficiency in commercial buildings.
机译：©2020 ElseVier Bvin本研究，开发了一种基于冷却器的冷却系统的实时预测控制和优化算法，并应用于实际建筑，通过原位应用分析其冷却节能效应和实际测量。为此目的，我们将冷却塔的冷凝器水出口温度和冷却器的冷却水出口温度设置为系统控制变量。为了评估算法性能，我们进行比较并分析了常规和冷凝水温，并基于ANN控制冷凝和冷凝器水温时的电力消耗和COP。结果，ANN模型的准确性高，CV（RMSE）为4.9％。此外，基于ANN的控制算法的能量分析表明，冷却器的平均节能率为24.7％，冷却器和冷却塔的总平均节能效果为7.4％。结果证实，所提出的MPC算法可以有助于改善商业建筑中的HVAC能效。

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