Application of hybrid GMDH and Least Square Support Vector Machine in energy consumption forecasting

Forecasting is a tool to predict the future event with the uncertainty and depending on the historical data. It is important for an upcoming planning event because the forecasting result will deliver the initial view for the future. This paper reviews the Least Square Support Vector Machine (LSSVM) and Group Method of Data Handling (GMDH) used in different application of forecasting. Besides, this paper will highlight the possibility of implementing the hybrid GMDH and LSSVM to achieve better accuracy of building energy consumption forecasting.

[1]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[2]  Dongxiao Niu,et al.  Short-Term Power Load Forecasting Using Least Squares Support Vector Machines(LS-SVM) , 2009, 2009 Second International Workshop on Computer Science and Engineering.

[3]  Puteh Saad,et al.  Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand. , 2010 .

[4]  Kin Keung Lai,et al.  A new fuzzy support vector machine to evaluate credit risk , 2005, IEEE Transactions on Fuzzy Systems.

[5]  Ali Azadeh,et al.  Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors , 2008 .

[6]  Dimitrios X. Kokotos,et al.  An application of data mining tools for the study of shipping safety in restricted waters , 2011 .

[7]  Dipti Srinivasan,et al.  Energy demand prediction using GMDH networks , 2008, Neurocomputing.

[8]  Kenneth A. Kaufman,et al.  Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach , 1997 .

[9]  J. Contreras,et al.  Short-term demand and energy price forecasting , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[10]  Christie M. Fuller,et al.  Analysis of healthcare coverage: A data mining approach , 2009, Expert Syst. Appl..

[11]  H. Pomares,et al.  A heuristic method for parameter selection in LS-SVM: Application to time series prediction , 2011 .

[12]  Yongjin Kwon,et al.  Data mining approaches for modeling complex electronic circuit design activities , 2008, Comput. Ind. Eng..

[13]  L. Wang,et al.  Application of Data Mining Technology Based on Neural Network in the Engineering , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[14]  Puteh Saad,et al.  Combination of Forecasting Using Modified GMDH and Genetic Algorithm , 2009 .

[15]  D. Srinivasan,et al.  Composite Reliability Evaluation Using Monte Carlo Simulation and Least Squares Support Vector Classifier , 2011, IEEE Transactions on Power Systems.

[16]  M. A. Jaramillo,et al.  Electric energy demand forecasting with neural networks , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[17]  Q. Henry Wu,et al.  Generalized Locally Weighted GMDH for Short Term Load Forecasting , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  S. J. Farlow.  The GMDH Algorithm of Ivakhnenko , 1981 .

[19]  Jinlong An,et al.  An Improved LSSVM Regression Algorithm , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[20]  E. Gonzalez-Romera,et al.  Monthly Electric Energy Demand Forecasting Based on Trend Extraction , 2006, IEEE Transactions on Power Systems.

[21]  Jinhui Zhang,et al.  Short-Term Electricity Load Forecasting Based on ICA and LSSVM , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[22]  V. Ravi,et al.  Rule extraction using Support Vector Machine based hybrid classifier , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.