A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes
作者： H. Shakouri G. , R. Nadimi , F. Ghaderi
DOI: 10.1016/J.ESWA.2007.12.058
关键词:
摘要: The well-known fuzzy rule-based Takagi-Sugeno-Kang (TSK) model is combined with a set of regressions (FR) to investigate the impact climate change on electricity consumption duration. demand forecasts in short-terms have vital application markets. Knowing that energy product relation between and average duration peak load. paper introduces type III TSK inference machine linear nonlinear regressors consequent part effects demand. However, simplified version applied daily data temperature Tehran, 2004. First, based an initially fitted curve, optimization employed cluster into three groups cold, temperate hot. been expanded reduce volatile property. Then estimated by company model. Numerical results show high efficiency proposed model, as well minor decrease absolute error.
acm.org
elsevier.com
sci-hub.se 参考文章(28)
A.V. Sedelnikov, V.Z. Manusov, The estimate of loss of electric power under conditions of uncertainty by using fuzzy regression analysis with asymmetrical triangular coefficients Proceedings. The 8th Russian-Korean International Symposium on Science and Technology, 2004. KORUS 2004.. ,vol. 1, pp. 287- 291 ,(2004) , 10.1109/KORUS.2004.1555350
Xu Ruoning, S-curve regression model in fuzzy environment Fuzzy Sets and Systems. ,vol. 90, pp. 317- 326 ,(1997) , 10.1016/S0165-0114(96)00120-0
Shakouri GH, J. Nazarzadeh, S.K.Y. Nikravesh, Exogeneity investigation and modeling energy demand via parallel dynamic linear models for maximum simultaneous power demand international conference on control applications. ,vol. 1, pp. 325- 330 ,(2003) , 10.1109/CCA.2003.1223341
Christian Döring, Marie-Jeanne Lesot, Rudolf Kruse, Data analysis with fuzzy clustering methods Computational Statistics & Data Analysis. ,vol. 51, pp. 192- 214 ,(2006) , 10.1016/J.CSDA.2006.04.030
A.M. Al-Kandari, S.A. Soliman, M.E. El-Hawary, Fuzzy short-term electric load forecasting International Journal of Electrical Power & Energy Systems. ,vol. 26, pp. 111- 122 ,(2004) , 10.1016/S0142-0615(03)00069-3
T. Runkler, F. Klawonn, R. Kruse, V. J. Rayward-Smith, F. Hoppner, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition ,(1999)
Phil Diamond, Fuzzy least squares Information Sciences. ,vol. 46, pp. 141- 157 ,(1988) , 10.1016/0020-0255(88)90047-3
Julián Moral-Carcedo, José Vicéns-Otero, Modelling the non-linear response of Spanish electricity demand to temperature variations Energy Economics. ,vol. 27, pp. 477- 494 ,(2005) , 10.1016/J.ENECO.2005.01.003
Michael Beenstock, Ephraim Goldin, Dan Nabot, The demand for electricity in Israel Energy Economics. ,vol. 21, pp. 168- 183 ,(1999) , 10.1016/S0140-9883(98)00005-X
Selçuk Bilgen, Sedat Keleş, Abdullah Kaygusuz, Ahmet Sarı, Kamil Kaygusuz, Global warming and renewable energy sources for sustainable development: A case study in Turkey Renewable & Sustainable Energy Reviews. ,vol. 12, pp. 372- 396 ,(2008) , 10.1016/J.RSER.2006.07.016