Haiyang Zheng Andrew Kusiak
e-mail: email@example.com Department of Mechanical and Industrial Engineering, 3131 Seamans Center, University of Iowa, Iowa City, IA 52242-1527
Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach 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. DOI: 10.1115/1.3142727 Keywords: power ramp rate prediction, wind farm, data-mining algorithms, multivariate time series model, parameter selection
Wind power generation is rapidly expanding and is becoming a noticeable contributor to the electric grid. The fact that most largescale wind farms were developed in recent years has made studies of their performance overdue. Given the changing nature of the wind regime, wind farm power varies across all time scales. The ﬂuctuating power of wind farms is usually balanced by the power produced by the traditional power plants to meet the grid requirements. The change of power output in time is referred to as ramping and it is measured with the power ramp rate PRR . The prediction of PRR at 10 min intervals is of interest to the wind industry due to the tightening electric grid requirements 1 . Though the power prediction research has a long tradition in the wind industry, the interest in prediction of power ramps is emerging. There is no industry standard for PRR prediction. Power ramp rate on 10 min intervals is to beneﬁt the gird management and power scheduling in the wind industry. The literature related to power ramps is discussed next. Svoboda et al. 2 proposed a Lagrangian relaxation method to solve hydrothermal generation scheduling problems. Three PRR constraints were considered and illustrated with a numerical example. Ummels et al. 3 presented a simulation method to evaluate the integration of large-scale wind farm power with the conventional power generation sources from a cost, reliability, and environmental perspective. Based on the PRR constraints for the reserve activation and generation schedule, the capability of a thermal generation system for balancing a wind power was investigated. Potter and Negnevitsky 4 applied an adaptive-neuron-fuzzy inference approach to forecast short-term wind speed and direction. Torres et al. 5 used transformed data to build the autoregressive moving average ARMA time series model for prediction of mean hourly wind speed of up to 10 h into the future. Sfetsos 6 presented a novel method for forecasting mean hourly wind speed based on the time series analysis data and showed that the developed model outperformed the conventional forecasting models. Lange and Focken 7 presented various models for short-term wind power prediction, including physics-based, fuzzy, and neuContributed by the Solar Energy Engineering Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received August 10, 2008; ﬁnal manuscript received March 6, 2009; published online July 9, 2009. Review conducted by Spyros Voutsinas.
rofuzzy models. Using meteorological data, Barbounis et al. 8 constructed a local recurrent neural network model for long-term wind speed and power forecasting. Hourly wind farm forecasts of up to 72 h were produced. Developing power...
References: 1 David, A. S., 1994, Wind Turbine Technology: Fundamental Concepts of Wind Turbine Engineering, ASME, New York, p. 638. 2 Svoboda, A. J., Tseng, C., Li, C., and Johnson, R. B., 1997, “Short-Term Resource Scheduling With Ramp Constraints,” IEEE Trans. Power Syst., 12 1 , pp. 77–83. 3 Ummels, B. C., Gibescu, M., Pelgrum, E., Kling, W. L., and Brand, A. J., 2007, “Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch,” IEEE Trans. Energy Convers., 22 1 , pp. 44–51. 4 Potter, C. W., and Negnevitsky, M., 2006, “Very Short-Term Wind Forecasting for Tasmanian Power Generation,” IEEE Trans. Power Syst., 21 2 , pp. 965– 972. 5 Torres, J. L., Garcia, A., De Blas, M., and De Francisco, A., 2005, “Forecast of Hourly Average Wind Speed With ARMA Models in Spain,” Sol. Energy, 79 1 , pp. 65–77. 6 Sfetsos, A., 2002, “A Novel Approach for the Forecasting of the Mean Hourly Wind Speed Time Series,” Renewable Energy, 27 2 , pp. 163–174. 7 Lange, M., and Focken, U., 2006, Physical Approach to Short-Term Wind Power Prediction, Springer-Verlag, Berlin, p. 208. 8 Barbounis, T. G., Theocharis, J. B., Alexiadis, M. C., and Dokopoulos, P. S., 2006, “Long-Term Wind Speed and Power Forecasting Using Local Recurrent
Neural Network Models,” IEEE Trans. Energ. Convers., 21 1 , pp. 273–284. 9 Kusiak, A., and Song, Z., 2006, “Combustion Efﬁciency Optimization and Virtual Testing: A Data-Mining Approach,” IEEE Trans. Ind. Informat., 2 3 , pp. 176–184. 10 Kusiak, A., 2006, “Data Mining: Manufacturing and Service Applications,” Int. J. Prod. Res., 44 18–19 , pp. 4175–4191. 11 Berry, M. J. A., and Linoff, G. S., 2004, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd ed., Wiley, New York. 12 Backus, P., Janakiram, M., Mowzoon, S., Runger, G. C., and Bhargava, A., 2006, “Factory Cycle-Time Prediction With Data-Mining Approach,” IEEE Trans. Semicond. Manuf., 19 2 , pp. 252–258. 13 Tan, P. N., Steinbach, M., and Kumar, V., 2006, Introduction to Data Mining, Pearson/Addison Wesley, Boston, MA. 14 Kusiak, A., Zheng, H., and Song, Z., 2009, “Wind Farm Power Prediction: A Data Mining Approach,” Wind Energy, 12 3 , pp. 275–293. 15 Box, J. E. P., and Jenkins, G. M., 1976, Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, CA. 16 Brown, B. G., Katz, R. W., and Murphy, A. H., 1984, “Time Series Prediction Model to Simulate and Forecast Wind Speed and Wind Power,” J. Clim. Appl. Meteorol., 23 8 , pp. 1184–1195. 17 Friedman, J. H., 2002, “Stochastic Gradient Boosting,” Comput. Stat. Data Anal., 38 4 , pp. 367–378. 18 Friedman, J. H., 2001, “Greedy Function Approximation: A Gradient Boosting Machine,” Ann. Stat., 29 5 , pp. 1189–1232. 19 Witten, I. H., and Frank, E., 2005, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann, San Francisco, CA. 20 Kohavi, R., and John, G. H., 1997, “Wrappers for Feature Subset Selection,” Artif. Intell., 97 1–2 , pp. 273–324. 21 Espinosa, J., Vandewalle, J., and Wertz, V., 2005, Fuzzy Logic, Identiﬁcation and Predictive Control, Springer-Verlag, London, UK. 22 http://en.wikipedia.org/wiki/Curse_of_dimensionality. 23 Bishop, C. M., 1995, Neural Networks for Pattern Recognition, Oxford University, New York. 24 Seidel, P., Seidel, A., and Herbarth, O., 2007, “Multilayer Perceptron Tumor Diagnosis Based on Chromatography Analysis of Urinary Nucleoside,” Neural Networks, 20 5 , pp. 646–651. 25 Smola, A. J., and Schoelkopf, B., 2004, “A Tutorial on Support Vector Regression,” Stat. Comput., 14 3 , pp. 199–222. 26 Cristianini, N., and Shawe-Taylor, J., 2000, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University, New York, p. 189. 27 Prasad, A. M., Iverson, L. R., and Liaw, A., 2006, “Newer Classiﬁcation and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction,” Ecosystems, 9 2 , pp. 181–189. 28 Breiman, L., 2001, “Random Forest,” Mach. Learn., 45 1 , pp. 5–32. 29 Breiman, L., Friedman, J., Olshen, R. A., and Stone, C. J., 1984, Classiﬁcation and Regression Trees, Wadsworth International, Monterey, CA. 30 Wang, Y., and Witten, I. H., 2002, “Modeling for Optimal Probability Prediction,” Proceedings of the 19th International Conference in Machine Learning, Sydney, Australia, pp. 650–657.
031011-8 / Vol. 131, AUGUST 2009
Transactions of the ASME
Downloaded 02 Sep 2009 to 220.127.116.11. Redistribution subject to ASME license or copyright; see http://www.asme.org/terms/Terms_Use.cfm
Please join StudyMode to read the full document