Geometrical Variable Weights Buffer GM(1, 1) Model and Its Application in Forecasting of China's Energy Consumption

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

[1]  Kyoung Kwan Ahn,et al.  An accurate signal estimator using a novel smart adaptive grey model SAGM(1, 1) , 2012, Expert Syst. Appl..

[2]  Lee-Ing Tong,et al.  Forecasting energy consumption using a grey model improved by incorporating genetic programming , 2011 .

[3]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[4]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .

[5]  Shuo-Pei Chen,et al.  Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1, 1) , 2008 .

[6]  Jeffrey Yi-Lin Forrest,et al.  The Current Developing Status on Grey System Theory , 2007 .

[7]  Chi-Sheng Shih,et al.  Grey number prediction using the grey modification model with progression technique , 2011 .

[8]  Der-Chiang Li,et al.  Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case , 2012 .

[9]  Jing Zhao,et al.  Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China , 2012 .

[10]  Der-Chiang Li,et al.  A grey-based fitting coefficient to build a hybrid forecasting model for small data sets , 2012 .

[11]  Der-Chiang Li,et al.  An extended grey forecasting model for omnidirectional forecasting considering data gap difference , 2011 .

[12]  Yi Lin,et al.  Grey Systems: Theory and Applications , 2010 .

[13]  Yan Wang,et al.  Fire detection model in Tibet based on grey-fuzzy neural network algorithm , 2011, Expert Syst. Appl..

[14]  Wei Zhou,et al.  Generalized GM (1, 1) model and its application in forecasting of fuel production , 2013 .

[15]  Yichun Xie,et al.  Asymmetric adjustment of the dynamic relationship between energy intensity and urbanization in China , 2013 .

[16]  Yi-Fan Wang,et al.  Predicting stock price using fuzzy grey prediction system , 2002, Expert Syst. Appl..

[17]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[18]  Bo-Suk Yang,et al.  Application of grey model for machine degradation prognostics , 2011 .

[19]  Li-Hui Chen,et al.  Forecasting financial crises for an enterprise by using the Grey Markov forecasting model , 2011 .

[20]  Liu Si-feng.  Buffer Operators with Variable Weights and the Complement of Axioms , 2009 .