Using Significant Classification Rules to Analyze Korean Customers' Power Consumption Behavior: Incremental Tree Induction using Cascading-and-Sharing Method

Power load analysis is an important issue in electrical industry. Data mining techniques are widely studied methodology for power load analysis and it helps decision making on electrical industry. In this paper, we propose an incremental tree induction algorithm using Cascading-and-Sharing method, and use mined significant classification rules to analyze customers’ power consumption behavior in General, Education and Regular groups.

[1]  Gianfranco Chicco,et al.  Application of clustering algorithms and self organising maps to classify electricity customers , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[2]  Xiaodong Wang,et al.  Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine , 2006 .

[3]  H SyedRiazul,et al.  A Multi-agent Approach To Short Term Load Forecasting Problem , 2005 .

[4]  Keun Ho Ryu,et al.  Application of Classification Methods for Forecasting Mid-Term Power Load Patterns , 2008, ICIC.

[5]  Zuhaimy Ismail,et al.  Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach , 2009 .

[6]  Regina Lamedica,et al.  A neural network based technique for short-term forecasting of anomalous load periods , 1996 .

[7]  Enrico Carpaneto,et al.  Electricity customer classification using frequency–domain load pattern data , 2006 .

[8]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.

[9]  Douglas H. Fisher,et al.  A Case Study of Incremental Concept Induction , 1986, AAAI.

[10]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[11]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[12]  Chu-song Chen,et al.  Synthesis of system power profile and temperature sensitivity analysis , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[13]  Mehmet Kurban,et al.  A New Approach for the Short-Term Load Forecasting with Autoregressive and Artificial Neural Network Models , 2007 .

[14]  Ghassan Halasa,et al.  Short-Term and Medium-Term Load Forecasting for Jordan's Power System , 2008 .

[15]  OpitzDavid,et al.  Popular ensemble methods , 1999 .

[16]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[17]  D.J. King,et al.  Electricity load profile classification using Fuzzy C-Means method , 2008, 2008 43rd International Universities Power Engineering Conference.

[18]  Huiqing Liu,et al.  Discovery of significant rules for classifying cancer diagnosis data , 2003, ECCB.

[19]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[20]  Ziyan Liu,et al.  Fuzzy-Rule based Load Pattern Classifier for Short-Tern Electrical Load Forecasting , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[21]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[22]  Gheorghe Grigoras,et al.  Clustering Techniques in Load Profile Analysis for Distribution Stations , 2009 .

[23]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[24]  C. S. Chen,et al.  Temperature Effect to Distribution System Load Profiles and Feeder Losses , 2001, IEEE Power Engineering Review.

[25]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[26]  Paul E. Utgoff,et al.  Decision Tree Induction Based on Efficient Tree Restructuring , 1997, Machine Learning.

[27]  Huiqing Liu,et al.  Ensembles of cascading trees , 2003, Third IEEE International Conference on Data Mining.