Artificial Intelligence Review - Special issue on lazy learning
Dynamics from multivariate time series
Physica D
Short-Term Load Forecasting Based on Fuzzy C-Mean Clustering and Weighted Support Vector Machines
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Local prediction of non-linear time series using support vector regression
Pattern Recognition
Dual features functional support vector machines for fault detection of rechargeable batteries
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multilayer neuro-fuzzy network for short term electric load forecasting
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
Input and structure selection for k-NN approximator
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Learning algorithms for a class of neurofuzzy network and application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Support vector machines for quality monitoring in a plastic injection molding process
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
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The forecasting of electricity demand has become one of the major research fields in electrical engineering. Accurately estimated forecasts are essential part of an efficient power system planning and operation. In this paper, a modified version of the support vector regression (SVR) is presented to solve the load forecasting problem. The proposed model is derived by modifying the risk function of the SVR algorithm with the use of locally weighted regression (LWR) while keeping the regularization term in its original form. In addition, the weighted distance algorithm based on the Mahalanobis distance for optimizing the weighting function's bandwidth is proposed to improve the accuracy of the algorithm. The performance of the new model is evaluated with two real-world datasets, and compared with the local SVR and some published models using the same datasets. The results show that the proposed model exhibits superior performance compare to that of LWR, local SVR, and other published models.