Wind farm power prediction: a data‐mining approach

In this paper, models for short- and long-term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short-term prediction model is 12 h, and the maximum forecast length of the long-term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short- and long-term prediction. Two basic prediction methods are presented: the direct prediction model, whereby the power prediction is generated directly from the weather forecasting data, and the integrated prediction model, whereby the prediction of wind speed is generated with the weather data, and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  P. Seidel,et al.  Multilayer perceptron tumour diagnosis based on chromatography analysis of urinary nucleosides , 2007, Neural Networks.

[2]  T. O. Halawani,et al.  A neural networks approach for wind speed prediction , 1998 .

[3]  J. Friedman.  Stochastic gradient boosting , 2002 .

[4]  Andrew Kusiak,et al.  Data Mining in Manufacturing: A Review , 2006 .

[5]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[6]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

[7]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[8]  Athanasios Sfetsos,et al.  A novel approach for the forecasting of mean hourly wind speed time series , 2002 .

[9]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[10]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[11]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[12]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[13]  Andrew Kusiak,et al.  Combustion efficiency optimization and virtual testing: a data-mining approach , 2006, IEEE Transactions on Industrial Informatics.

[14]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

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

[16]  Vincent Wertz,et al.  Fuzzy Logic, Identification and Predictive Control , 2004 .

[17]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[18]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[19]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[20]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[21]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

[22]  P. Backus,et al.  Factory cycle-time prediction with a data-mining approach , 2006, IEEE Transactions on Semiconductor Manufacturing.

[23]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[24]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.