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Electrical load forecasting is an important tool in managing transmission and distribution facilities, financial resources, manpower, and materials at electrical power utility companies. A simple and accurate electrical load forecasting scheme is required. Short-term load forecasting (STLF) involves predicting the load from few hours to a week ahead. A simple non-parametric kernel regression (KR) approach for STLF is presented. Kernel regression is a linear approach with the ability to handle nonlinear information. A Gaussian kernel whose bandwidth selected by the Direct Plug-in (DPI) method is utilized. The performance comparison of the proposed method with artificial neural network (ANN), ordinary least squares (OLS), and ridge regression (RR) predictions on the same data set is presented. Experimental results show that kernel regression performs better than ANN forecaster on the given data set. The method proposed provides analytical solution, features optimal bandwidth selection, which is more instructive compared to ANN architecture and its other parameters. [ABSTRACT FROM AUTHOR]

An R-functions based classification approach along with a regularization framework is proposed. The abnormal software process detection problem was used as the test bed. The R-functions based classification method is termed as the R-cloud method. The approach was validated both on synthetic and real-world data. Regularization allows to achieve good generalization and classification performance. In addition, the R-cloud approach gives the benefit of the analytical representation of the decision boundary. The introductory study on practical use of the R-cloud classifiers yielded promising results. The prototyping has shown that application of the R-functions based pattern recognition technique is a significant and practical tool for fault detection in providing fault tolerant computing. [ABSTRACT FROM AUTHOR]