Smart meter monitoring and data mining techniques for predicting refrigeration system performance

• Refrigeration system performance is predicted using data mining techniques and smart meter.
• Laboratory experiments are performed with different refrigerant leakage scenarios.
• Analytical results are compared based on synthetic index to determine the best models.
• The approach is effective for evaluating and optimizing refrigeration equipment performance.

A major challenge in many countries is providing sufficient energy for human beings and for supporting economic activities while minimizing social and environmental harm. This study predicted coefficient of performance (COP) for refrigeration equipment under varying amounts of refrigerant (R404A) with the aids of data mining (DM) techniques. The performance of artificial neural networks (ANNs), support vector machines (SVMs), classification and regression tree (CART), multiple regression (MR), generalized linear regression (GLR), and chi-squared automatic interaction detector (CHAID) were applied within DM process. After obtaining the COP value, abnormal equipment conditions can be evaluated for refrigerant leakage. Analytical results from cross-fold validation method are compared to determine the best models. The study shows that DM techniques can be used for accurately and efficiently predicting COP. In the liquid leakage phase, ANNs provide the best performance. In the vapor leakage phase, the best model is the GLR model. Experimental results confirm that systematic analyses of model construction processes are effective for evaluating and optimizing refrigeration equipment performance.