×

A load curve based fuzzy modeling technique for short-term load forecasting. (English) Zbl 1031.93011

The authors propose a fuzzy rule-based model for short-term forecasting. Its development is carried out in a general setting of Computational Intelligence and comprises three main phases; that is (a) determination of the structure of the model, (b) selecting input variables through genetic optimization (genetic algorithm), and (c) fine tuning of the model through a hybrid of the genetic/least square error algorithm. The effectiveness of the model is augmented by the realization of the conclusion part in the form of cubic B-splines. The study comes with an extensive experimental part including some comparative analysis exploiting other neural network models.

MSC:

93A30 Mathematical modelling of systems (MSC2010)
93C42 Fuzzy control/observation systems
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Baba, N., A new approach finding the global minimum of error functions of neural networks, Neural Networks, 2, 367-373 (1989)
[2] Bakirtzis, A. G.; Petridis, V.; Kiartzis, S. J.; Alexiadis, M. C.; Maissis, A. H., A neural network short term load forecasting model for the Greek Power System, IEEE Trans. Power Systems, 11, 858-863 (1996)
[3] Bezdek, J. C., Pattern Recognition with Fuzzy Objective Functional Algorithm (1981), Plenum Press: Plenum Press New York
[4] Goodwin, G. C.; Sin, K. S., Adaptive Filtering Prediction and Control (1984), Prentice-Hall: Prentice-Hall Englewood Cliffs, NJ · Zbl 0653.93001
[5] Gross, G.; Galiana, F. D., Short term load forecasting, Proc. IEEE, 75, 1558-1573 (1987)
[6] Hathaway, R. J.; Bezdek, J. C., Switching regression models and fuzzy clustering, IEEE Trans. Fuzzy Systems, 1, 195-204 (1993)
[7] Jang, J.-S. R., ANFISAdaptive network based fuzzy inference system, IEEE Trans. Systems, Man Cybernet., 23, 665-685 (1993)
[8] Karr, C. L.; Gentry, E. J., Fuzzy control of pH using genetic algorithms, IEEE Trans. Fuzzy Systems, 1, 46-53 (1993)
[9] Kim, E.; Park, M.; Ji, S.; Park, M., A new approach to fuzzy modeling, IEEE Trans. Fuzzy Systems, 5, 328-337 (1997)
[10] H.M. Lee, H. Takagi, Integrating design stages of fuzzy systems using genetic algorithms, Proc. IEEE Int. Conf. Fuzzy Systems, San Francisco, CA, Apr. 1993, vol. 1, pp. 612-617.; H.M. Lee, H. Takagi, Integrating design stages of fuzzy systems using genetic algorithms, Proc. IEEE Int. Conf. Fuzzy Systems, San Francisco, CA, Apr. 1993, vol. 1, pp. 612-617.
[11] Mastorocostas, P. A.; Theocharis, J. B.; Petridis, V. S., A constrained orthogonal least-squares method for generating TSK fuzzy modelsapplication to short-term load forecasting, Fuzzy Sets and Systems, 118, 215-233 (2001)
[12] Newman, M.; Sproul, R. F., Principles of Interactive Computer Graphics (1979), McGraw-Hill: McGraw-Hill New York
[13] S.E. Papadakis, J.B. Theocharis, A novel genetic training algorithm with application to fuzzy neural networks, Proc. IASTED Int. Conf. System Modeling Identification and Control, Insbruck Austria, 1996, pp. 125-128.; S.E. Papadakis, J.B. Theocharis, A novel genetic training algorithm with application to fuzzy neural networks, Proc. IASTED Int. Conf. System Modeling Identification and Control, Insbruck Austria, 1996, pp. 125-128.
[14] Papadakis, S. E.; Theocharis, J. B.; Kiartzis, S. J.; Bakirtzis, A. G., A novel approach to short-term load forecasting using fuzzy neural networks, IEEE Trans. Power Systems, 13, 480-492 (1998)
[15] Papalexopoulos, A. D.; How, S.; Peng, T. M., An implementation of a neural network based load forecasting model for the EMS, IEEE Trans. Power Systems, 9, 1956-1962 (1994)
[16] Park, D. C.; El-Sharkawi, M. A.; Marks, R. J.; Atlas, L. E.; Damborg, M. J., Electric load forecasting using an artificial neural network, IEEE Trans. Power Systems, 6, 442-449 (1991)
[17] Peng, T. M.; Hubele, N. F.; Karady, G. G., Advancement in the application of neural networks for short term load forecasting, IEEE Trans. Power Systems, 7, 1, 250-258 (1992)
[18] Piras, A.; Germond, A.; Buchenel, B.; Imhof, K.; Jaccard, Y., Heterogeneous artificial neural network for short term electrical load forecasting, IEEE Trans. Power Systems, 11, 397-402 (1996)
[19] Srinivasan, D.; Chang, C. S.; Liew, A. C., Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting, IEEE Trans. Power Systems, 10, 1897-1903 (1995)
[20] Sun, J.; Grosky, W. I.; Hassoun, M. H., A fast algorithm for finding global minima of error functions in layered neural networks, Int. Joint Conf. Neural Networks, I, 715-720 (1990)
[21] Wang, C.-H.; Horng, J.-G., Constrained minimum-time path planning for robot manipulators via virtual knots of cubic B-spline functions, IEEE Trans. Automat. Control, 35, 573-577 (1990) · Zbl 0708.93056
[22] Wang, C.-H.; Wang, W.-Y.; Lee, T.-T.; Tseng, P.-S., Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control, IEEE Trans. Systems, Man Cybernet., 25, 841-851 (1995)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.