The prediction model for electrical power system using an improved hybrid optimization model

Abstract In this paper, an improved hybrid optimization model based on grey GM (1, 1) model is proposed to develop the prediction model in power systems. To realize more accurate prediction, the regression model is firstly integrated into GM (1, 1) through compensation for the residual error series. The improved model is defined as RGM (1, 1). Furthermore, Markov chain model is applied to RGM (1, 1) to enhance the prediction performance. We call the proposed model as MC-RGM (1, 1). Finally, Taylor approximation method is presented MC-RGM (1, 1) for achieving the high prediction accuracy. The improved model is defined as T-MC-RGM (1, 1). A real case of thermal electric power generation in Japan is used to validate the effectiveness of proposed model.

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

[2]  Shiro Masuda,et al.  An improved grey dynamic GM(2, 1) model , 2010, Int. J. Comput. Math..

[3]  Y. Tamura,et al.  Interval prediction of annual maximum demand using grey dynamic model , 1996 .

[4]  Renkuan Guo,et al.  Fuzzy set‐valued and grey filtering statistical inferences on a system operating data , 2005 .

[5]  Pi-Fang Hsu,et al.  Forecast of non‐alcoholic beverage sales in Taiwan using the Grey theory , 2002 .

[6]  Daisuke Yamaguchi,et al.  Application hybrid grey dynamic model to forecasting compensatory control , 2007 .

[7]  Shiro Masuda,et al.  An Optimal Grey PID Control System , 2009 .

[8]  George G. Karady,et al.  Fuzzy logic for short term load forecasting , 1996 .

[9]  Shiro Masuda,et al.  The hybrid grey-based model for cumulative curve prediction in manufacturing system , 2010 .

[10]  Cheng-Hsiung Hsieh.  Grey Neural Network and Its Application to Short Term Load Forecasting Problem , 2002 .

[11]  Daisuke Yamaguchi,et al.  Application of GM(1,1)-Markov chain combined model to China's automobile industry , 2007 .

[12]  Shiro Masuda,et al.  A research on short term load forecasting problem applying improved grey dynamic model , 2011 .

[13]  Albert W. L. Yao,et al.  An improved Grey-based approach for electricity demand forecasting , 2003 .

[14]  Masatoshi Kitaoka,et al.  Evaluation of Production Process and Prediction with Cumulative Curve. , 2001 .

[15]  M. Aaron,et al.  The Use of Least Squares in System Design , 1956 .

[16]  Chan-Ben Lin,et al.  High-precision forecast using grey models , 2001 .

[17]  Maarouf Saad,et al.  Non-parametric short-term load forecasting , 2007 .

[18]  GuoDong Li,et al.  New Proposal and Accuracy Evaluation of Grey Prediction GM , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[19]  Yo-Ping Huang,et al.  The hybrid grey-based models for temperature prediction , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Chia-Yon Chen,et al.  Applications of improved grey prediction model for power demand forecasting , 2003 .

[21]  P. Vähäkyla,et al.  Short-term forecasting of grid load using Box-Jenkins techniques , 1980 .

[22]  Ruey-Hsun Liang.  Application of grey relation analysis to hydroelectric generation scheduling , 1999 .

[23]  Shiro Masuda,et al.  A New Reliability Prediction Model in Manufacturing Systems , 2010, IEEE Transactions on Reliability.

[24]  Masatake Nagai,et al.  A GM(1,1)–Markov chain combined model with an application to predict the number of Chinese international airlines , 2007 .

[25]  B. W. Ang,et al.  A trigonometric grey prediction approach to forecasting electricity demand , 2006 .

[26]  Maarouf Saad,et al.  An efficient approach for short term load forecasting using artificial neural networks , 2006 .