论文信息 - Electric Load Forecasting using SVMS

Electric Load Forecasting using SVMS

Support vector machines (SVMs) have been proposed as a novel technique and applied to regression recently. In this paper, SVMS are used for load forecasting. The training sample sets are chosen and preprocessed before every forecasting. Then the interference of the non-correlative and bad samples for the forecasting can be avoided. The effectiveness and the feasibility of forecasting of the employed method are examined through some simulations

[1] Robert J. Marks,et al. Electric load forecasting using an artificial neural network , 1991 .

[2] Stamatios V. Kartalopoulos,et al. Understanding neural networks and fuzzy logic , 1995 .

[3] Tarun Khanna,et al. Foundations of neural networks , 1990 .

[4] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[5] Edgar Sanchez-Sinencio,et al. Artificial Neural Networks: Paradigms, Applications, and Hardware Implementations , 1994 .

[6] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .

[7] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8] Vladimir Vapnik,et al. Statistical learning theory , 1998 .

[9] Francis Eng Hock Tay,et al. Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[10] A. Harvey. Time series models , 1983 .