A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes

The well-known fuzzy rule-based Takagi-Sugeno-Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error.

[1]  Christian Döring,et al.  Data analysis with fuzzy clustering methods , 2006, Comput. Stat. Data Anal..

[2]  V. Z. Manusov,et al.  The fuzzy regression analysis as a means of electric power losses evaluation in electrical networks , 2002 .

[3]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  Ruoning Xu.  S-curve regression model in fuzzy environment , 1997, Fuzzy Sets Syst..

[5]  Fernando Olsina,et al.  Modeling long-term dynamics of electricity markets , 2006 .

[6]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[7]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[8]  J. Nazarzadeh,et al.  A hybrid nonlinear model for the annual maximum simultaneous electric power demand , 2006, IEEE Transactions on Power Systems.

[9]  Joanicjusz Nazarko,et al.  The fuzzy regression approach to peak load estimation in power distribution systems , 1999 .

[10]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[11]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[12]  Phil Diamond,et al.  Fuzzy least squares , 1988, Inf. Sci..

[13]  Michael Beenstock,et al.  The demand for electricity in Israel , 1999 .

[14]  M. E. El-Hawary,et al.  Fuzzy short-term electric load forecasting , 2004 .

[15]  Rakesh Nagi,et al.  Integration of just-in-time production and material handling for an assembly environment , 1996 .

[16]  Kamil Kaygusuz,et al.  Renewable and sustainable energy use in Turkey: a review , 2002 .

[17]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[18]  Eva González Romera,et al.  Forecasting of the electric energy demand trend and monthly fluctuation with neural networks , 2007, Comput. Ind. Eng..

[19]  H. Shakouri,et al.  Fuzzy linear regression models with absolute errors and optimum uncertainty , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[20]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Julián Moral-Carcedo,et al.  Modelling the non-linear response of Spanish electricity demand to temperature variations , 2005 .

[22]  Sedat Keleş,et al.  Global warming and renewable energy sources for sustainable development: A case study in Turkey , 2008 .

[23]  S.K.Y. Nikravesh,et al.  Exogeneity investigation and modeling energy demand via parallel dynamic linear models for maximum simultaneous power demand , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[24]  Junzo Watada,et al.  Possibilistic linear regression analysis for fuzzy data , 1989 .

[25]  Other.  Renewable energy: market and policy trends in IEA countries , 2004 .

[26]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[27]  V.Z. Manusov,et al.  The estimate of loss of electric power under conditions of uncertainty by using fuzzy regression analysis with asymmetrical triangular coefficients , 2004, Proceedings. The 8th Russian-Korean International Symposium on Science and Technology, 2004. KORUS 2004..

[28]  Abraham Kandel,et al.  General fuzzy least squares , 1997, Fuzzy Sets Syst..