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http://dx.doi.org/10.3795/KSME-C.2016.4.1.027

Data Mining-Based Performance Prediction Technology of Geothermal Heat Pump System  

Hwang, Min Hye (VP Korea, Inc.)
Park, Myung Kyu (VP Korea, Inc.)
Jun, In Ki (VP Korea, Inc.)
Sohn, Byonghu (Building and Urban Research Institute, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Transactions of the KSME C: Technology and Education / v.4, no.1, 2016 , pp. 27-34 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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