The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Self-Organizing Maps
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
A SOM based approach for visualization of GSM network performance data
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Kernel-SOM Based Visualization of Financial Time Series Forecasting
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Support Vector Machines
Proximal support vector machine using local information
Neurocomputing
Recognition and visualization of music sequences using self-organizing feature maps
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Power prediction in smart grids with evolutionary local kernel regression
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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Wind energy has an important part to play as renewable energy resource in a sustainable world. For a reliable integration of wind energy high-dimensional wind time-series have to be analyzed. Fault analysis and prediction are an important aspect in this context. The objective of this work is to show how methods from neural computation can serve as forecasting and monitoring techniques, contributing to a successful integration of wind into sustainable and smart energy grids. We will employ support vector regression as prediction method for wind energy time-series. Furthermore, we will use dimension reduction techniques like self-organizing maps for monitoring of high-dimensional wind time-series. The methods are briefly introduced, related work is presented, and experimental case studies are exemplarily described. The experimental parts are based on real wind energy time-series data from the National Renewable Energy Laboratory (NREL) western wind resource data set.