應用遞歸神經網路於空調性能分析系統

Abstract

本研究旨在提供一套診斷系統，利用冷媒側四點溫度、水側四點溫度及主機耗電量測之數據，透過物理理論進一步推算出運轉效率等指標，使診斷能順利進行並提供業者改善方向。另一部分希望發展出一預測系統，透過大量數據建立準確預測壓縮機運轉功率、運轉效率等模型之方法，以應用在各企業中準確預測耗能，達成長期監控的目標。 本論文主要透過中龍鋼鐵的冰水主機與箱型主機兩案例，驗證空調分析系統推算之運轉效率、水泵效率與不可逆性是否符合實際趨勢，比較不同的多元複迴歸模型與遞歸神經網路之預測誤差，實驗長短期記憶(Long Short-Term Memory, LSTM)架構應用於主機性能等參數預測的可行性。 研究結果顯示，空調性能分析系統應用在即時的空調診斷是可行的，推算之性能係數、水泵效率與不可逆性皆符合理論。LSTM應用於空調主機性能的預測模型是準確的，在預測冷卻水出水溫、冰水出水溫、壓縮機功率和性能係數等表現，因為LSTM擁有對時間序列的記憶性，相較於多元複迴歸穩定且準確。本研究驗證了使用八點溫度來診斷空調的可能性，去除壓力與流量的量測，使空調的性能分析能夠以更簡易且快速的實行，提供在實務上空調診斷的替代方案。

The purpose of this study is to set up an analysis system for air conditioning systems. The first part of the system was construct based on physics. For this purpose; the temperature of refrigerant, temperature and flow rate of water, and input power of compressor have been measured for calculating the coefficient of efficiency(COP), pump efficiency, and irreversibility. Another aim was to develop a method for predicting the performance of chiller. The models for predicting the outlet temperature, input power of compressor, and COP were constructed based on a large dataset obtained from the experiments. The multiple regressions were compared with long short-term memory(LSTM) based recurrent neural network(RNN) for the prediction error. The objective for the analysis system is to make diagnosis and long term monitoring for air conditioning systems simpler and feasible in the industry. The experiments were conducted including water cooled chiller and packaged air conditioner which are located at the industrial area of the collaborate company Dragon Steel Co.,Ltd., which is subsidiary of China Steel Co.,Ltd.. To verify the reliability and validity of the two main idea of the study, the examination processes were carried out with these two cases. The results indicate that the analysis system for detecting the performance of chiller and heat pump is practicable. The trend of the COP, pump efficiency, and irreversibility simulated from the analysis system are fit to the theory and the references. On the other hand, the study shows that the LSTM neural network provides the best results due to the strong ability to model the temporal relationship between time series. For each output parameters, LSTM structure performs more accurate and stable than multiple regression. The analysis system only needs to measure the temperature of refrigerant and water which is more easily than pressure drop and flow rate to obtain. The analysis system could be proposed as an alternative method for engineers to diagnosis or monitor air conditioning systems.