Abstract
This study presents six data mining techniques for prediction of coefficient of performance (COP) for refrigeration equipment. These techniques include 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). Based on COP values, abnormal situation of equipment can be evaluated for refrigerant leakage. Experimental results from cross-fold validation are compared to determine the best models. The study shows that data mining 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. The models built in this study are effective for evaluating refrigeration equipment performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liao, S.-H., Chu, P.-H., Hsiao, P.-Y.: Data mining techniques applications. A decade review from 2000 to 2011. Expert Systems with Applications 39(12), 11303–11311 (2012)
Lee, H.I.: Energy report. lower the price of electricity. Bureau of Energy Ministry of Economics Affairs (2011)
Ozgoren, M., Bilgili, M., Babayigit, O.: Hourly performance prediction of ammoniaewater solar absorption refrigeration. Applied Thermal Engineering 40, 80–90 (2012)
Şahin, A.T.: Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy. Renewable Energy 36(10), 2747–2752 (2011)
Ahmed, A., Korres, N.E., Ploennigs, J., Elhadi, H., Menzel, K.: Mining building performance data for energy-efficient operation. Advanced Engineering Informatics 25(2), 341–354 (2011)
Chou, J., Chiu, C., Farfoura, M., Al-Taharwa, I.: Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques. Journal of Computing in Civil Engineering 25(3), 242–253 (2011)
Taylor, J.W., Buizza, R.: Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting 19(1), 57–70 (2003)
Saab, S., Badr, E., Nasr, G.: Univariate modeling and forecasting of energy consumption. The case of electricity in Lebanon. Energy 26(1), 1–14 (2001)
Chen, C.S., Tzeng, Y.M., Hwang, J.C.: The application of artificial neural networks to substation load forecasting. Electric Power Systems Research 38(2), 153–160 (1996)
Ye, B., Guo, C., Cao, Y.: Short-term load forecasting using a new fuzzy modeling strategy, Hangzhou, P.R. China, pp. 5045–5049 (2004)
Pao, H.T.: Comparing linear and nonlinear forecasts for Taiwan’s electricity consumption. Energy 31(12), 1793–1805 (2006)
Şencan, A., Köse, S.L., Selbaş, R.: Prediction of thermophysical properties of mixed refrigerants using artificial neural network. Energy Conversion and Management 52(2), 958–974 (2011)
Orenstein, T., Kohavi, Z., Pomeranz, I.: An optimal algorithm for cycle breaking in directed graphs. Journal of Electronic Testing 7(1-2), 71–81 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chou, JS., Pham, AD. (2013). Applying Smart Meter and Data Mining Techniques to Predict Refrigeration System Performance. In: Meesad, P., Unger, H., Boonkrong, S. (eds) The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Advances in Intelligent Systems and Computing, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37371-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-37371-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37370-1
Online ISBN: 978-3-642-37371-8
eBook Packages: EngineeringEngineering (R0)