In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided.
Skip Nav Destination
e-mail: andrew-kusiak@uiowa.edu
Article navigation
August 2009
Research Papers
Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach
Haiyang Zheng,
Haiyang Zheng
Department of Mechanical and Industrial Engineering, 3131 Seamans Center,
University of Iowa
, Iowa City, IA 52242-1527
Search for other works by this author on:
Andrew Kusiak
Andrew Kusiak
Department of Mechanical and Industrial Engineering, 3131 Seamans Center,
e-mail: andrew-kusiak@uiowa.edu
University of Iowa
, Iowa City, IA 52242-1527
Search for other works by this author on:
Haiyang Zheng
Department of Mechanical and Industrial Engineering, 3131 Seamans Center,
University of Iowa
, Iowa City, IA 52242-1527
Andrew Kusiak
Department of Mechanical and Industrial Engineering, 3131 Seamans Center,
University of Iowa
, Iowa City, IA 52242-1527e-mail: andrew-kusiak@uiowa.edu
J. Sol. Energy Eng. Aug 2009, 131(3): 031011 (8 pages)
Published Online: July 9, 2009
Article history
Received:
August 10, 2008
Revised:
March 6, 2009
Published:
July 9, 2009
Citation
Zheng, H., and Kusiak, A. (July 9, 2009). "Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach." ASME. J. Sol. Energy Eng. August 2009; 131(3): 031011. https://doi.org/10.1115/1.3142727
Download citation file:
Get Email Alerts
Analysis of Erosion of Surfaces in Falling Particle Concentrating Solar Power
J. Sol. Energy Eng (April 2025)
Related Articles
Trend Mining for Predictive Product Design
J. Mech. Des (November,2011)
Structural Health Monitoring With Autoregressive Support Vector Machines
J. Vib. Acoust (April,2009)
Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach
J. Sol. Energy Eng (February,2011)
Knowledge Discovery in Engineering Applications Using Machine Learning Techniques
J. Manuf. Sci. Eng (September,2022)
Related Proceedings Papers
Related Chapters
Real-Time Prediction Using Kernel Methods and Data Assimilation
Intelligent Engineering Systems through Artificial Neural Networks
A Utility Perspective of Wind Energy
Wind Turbine Technology: Fundamental Concepts in Wind Turbine Engineering, Second Edition
Improving Dynamic Performance of Wind Farms in a Distribution System Using DSTATCOM
International Conference on Software Technology and Engineering, 3rd (ICSTE 2011)