The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Training v-support vector regression: theory and algorithms
Neural Computation
A tutorial on support vector regression
Statistics and Computing
Neural Computation
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
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Ice accretion on power transmission and distribution lines is one of the major causes of power grid outages in northern regions. While such icing events are rare, they are very costly. Thus, it would be useful to predict how much ice will accumulate. Many current ice accretion forecasting systems use precipitation-type prediction and physical ice accretion models. These systems are based on expert knowledge and experimentations. An alternative strategy is to learn the patterns of ice accretion based on observations of previous events. This paper presents two different forecasting systems that are obtained by applying the learning algorithm of Support Vector Machines to the outputs of a Numerical Weather Prediction model. The first forecasting system relies on an icing model, just as the previous algorithms do. The second system learns an effective forecasting model directly from meteorological features. We use a rich data set of eight different icing events (from 2002 to 2008) to empirically compare the performance of the various ice accretion forecasting systems. Several experiments are conducted to investigate the effectiveness of the forecasting algorithms. Results indicate that the proposed forecasting system is significantly more accurate than other state-of-the-art algorithms.