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Data Mining-Based Performance Prediction Technology of Geothermal Heat Pump System
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 Title & Authors
Data Mining-Based Performance Prediction Technology of Geothermal Heat Pump System
Hwang, Min Hye; Park, Myung Kyu; Jun, In Ki; Sohn, Byonghu;
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 Abstract
This preliminary study investigated data mining-based methods to assess and predict the performance of geothermal heat pump(GHP) system. Data mining is a key process of the knowledge discovery in database (KDD), which includes five steps: 1) Selection; 2) Pre-processing; 3) Transformation; 4) Analysis(data mining); and 5) Interpretation/Evaluation. We used two analysis models, categorical and numerical decision tree models to ascertain the patterns of performance(COP) and electrical consumption of the GHP system. Prior to applying the decision tree models, we statistically analyzed measurement database to determine the effect of sampling intervals on the system performance. Analysis results showed that 10-min sampling data for the performance analysis had highest accuracy of 97.7% over the actual dataset of the GHP system.
 Keywords
Geothermal Heat Pump System;Data Mining;Decision Tree Model;Performance Prediction;KDD;
 Language
Korean
 Cited by
 References
1.
Lund, J. W. and Boyd, T. L., 2015, "Direct Utilization of Geothermal Energy 2015 Worldwide Review," Proceedings World Geothermal Congress 2015, Melbourne, Australia, pp. pp. 1-31.

2.
Cervera-Vazquez, J., Montagud, C. and Corberan J. M., 2015, "In Situ Optimization Methodology for Ground Source Heat Pump Systems: Upgrade to Ensure User Comfort," Energy and Buildings, Vol. 109, pp. 195-208. crossref(new window)

3.
Jung, Y., Jo, J., Kim, Y. and Cho, Y., 2014, "A Study on the Geothermal Heat Pump System Performance Analysis According to Water Flow Rate of the Geothermal Water Circulation Pump," Journal of the Korean Solar Energy Society, Vol. 34, No. 6, pp. 103-109. crossref(new window)

4.
He, X., Z hang, Z. and Kusiak, A., 2014, "Performance Optimization of HVAC Systems with Computational Intelligence Algorithms," Energy and Buildings, Vol. 81, pp. 371-380. crossref(new window)

5.
Ren, X., Yan, D. and Hong, T., 2015, "Data Mining of Space Heating System Performance in Affordable Housing," Building and Environment, Vol. 89, pp. 1-13.

6.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., 1996, "From Data Mining to Knowledge Discovery in Databases," AI Magazine, Vol. 17, No. 3, pp. 37-54.

7.
Han, J., Kamber, M. and Pei, J., 2011, Data Mining: Concepts and Techniques(Third Edition), Morgan Kaufmann, Burlington, MA.

8.
Kong, H. J., Kang, S. J., Yun, K. S. and Lim, H. J., 2013, "An Experimental Study on the Cooling and Heating Performance of a Residential Ground Source Heat Pump System," Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 25, No. 3, pp. 156-163. crossref(new window)