Wind energy prediction and monitoring with neural computation

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.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[2]  Oliver Kramer,et al.  Analysis of wind energy time series with kernel methods and neural networks , 2011, 2011 Seventh International Conference on Natural Computation.

[3]  Amaury Lendasse,et al.  X-SOM and L-SOM: A double classification approach for missing value imputation , 2010, Neurocomputing.

[4]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[5]  Shuhui Li,et al.  Using neural networks to estimate wind turbine power generation , 2001 .

[6]  Oliver Kramer,et al.  Short-Term Wind Energy Forecasting Using Support Vector Regression , 2011, SOCO.

[7]  Yongqian Liu,et al.  Genetic algorithm-piecewise support vector machine model for short term wind power prediction , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[8]  Oliver Kramer,et al.  Monitoring of multivariate wind resources with self-organizing maps and slow feature analysis , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[9]  Kara Clark,et al.  How do Wind and Solar Power Affect Grid Operations: The Western Wind and Solar Integration Study; Preprint , 2009 .

[10]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[11]  Oliver Kramer,et al.  Power Prediction in Smart Grids with Evolutionary Local Kernel Regression , 2010, HAIS.

[12]  Oliver Kramer,et al.  Recognition and Visualization of Music Sequences Using Self-organizing Feature Maps , 2010, KI.

[13]  Konstantinos I. Diamantaras,et al.  Applying PCA neural models for the blind separation of signals , 2009, Neurocomputing.

[14]  Yong Qi,et al.  Kernel-SOM Based Visualization of Financial Time Series Forecasting , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[15]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

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

[17]  Bin Chen,et al.  Proximal support vector machine using local information , 2009, Neurocomputing.

[18]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Olli Simula,et al.  Process Monitoring and Modeling Using the Self-Organizing Map , 1999, Integr. Comput. Aided Eng..

[21]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[22]  Paras Mandal,et al.  Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[23]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[24]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[25]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[26]  Boleslaw K. Szymanski,et al.  Taming the Curse of Dimensionality in Kernels and Novelty Detection , 2004, WSC.

[27]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[28]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[29]  Helge J. Ritter,et al.  Neural computation and self-organizing maps - an introduction , 1992, Computation and neural systems series.

[30]  Amaury Lendasse,et al.  A SOM-based approach to estimating product properties from spectroscopic measurements , 2009, Neurocomputing.

[31]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[32]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[33]  Yan Zhou,et al.  A scalable support vector machine for distributed classification in ad hoc sensor networks , 2010, Neurocomputing.

[34]  Jiaxing He,et al.  Wind speed prediction using support vector regression , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[35]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[36]  Jing Shi,et al.  Bayesian adaptive combination of short-term wind speed forecasts from neural network models , 2011 .

[37]  Kimmo Raivio,et al.  A SOM Based Approach for Visualization of GSM Network Performance Data , 2005, IEA/AIE.

[38]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[39]  Debra Lew,et al.  Creating the Dataset for the Western Wind and Solar Integration Study (U.S.A.) , 2008 .

[40]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .