Abstract
Wind energy prediction has an important part to play in a smart energy grid for load balancing and capacity planning. In this paper we explore, if wind measurements based on the existing infrastructure of windmills in neighbored wind parks can be learned with a soft computing approach for wind energy prediction in the ten-minute to six-hour range. For this sake we employ Support Vector Regression (SVR) for time series forecasting, and run experimental analyses on real-world wind data from the NREL western wind resource dataset. In the experimental part of the paper we concentrate on loss function parameterization of SVR. We try to answer how far ahead a reliable wind forecast is possible, and how much information from the past is necessary.We demonstrate the capabilities of SVR-based wind energy forecast on the micro-scale level of one wind grid point, and on the larger scale of a whole wind park.
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References
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001) Software available at, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Corchado, E., Árroyo, A., Tricio, V.: Soft computing models to identify typical meteorological days. Logic Journal of the IGPL (2010)
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., Feitosa, E.: A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12(6), 1725–1744 (2008)
Evangelista, P.F., Embrechts, M.J., Szymanski, B.K.: Taming the curse of dimensionality in kernels and novelty detection. In: Applied Soft Computing Technologies: The Challenge of Complexity, pp. 431–444. Springer, Heidelberg (2006)
Herrero, Á., Corchado, E., Gastaldo, P., Zunino, R.: Neural projection techniques for the visual inspection of network traffic. Neurocomputing 72(16-18), 3649–3658 (2009)
Kusiak, A., Li, W.: The prediction and diagnosis of wind turbine faults. Renewable Energy 36(1), 16–23 (2011)
Lew, D., Milligan, M., Jordan, G., Freeman, L., Miller, N., Clark, K., Piwko, R.: How do wind and solar power affect grid operations: The western wind and solar integration study. In: 8th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms (2009)
Li, G., Shi, J., Zhou, J.: Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy 36(1), 352–359 (2011)
Li, S., Wunsch, D.C., Ohair, E.A., Giesselmann, M.G.: Using neural networks to estimate wind turbine. Journal of Guidance, Control, and Dynamics 16(3), 276–282 (2001)
Milligan, M., Porter, K., DeMeo, E., Denholm, P., Holttinen, H., Kirby, B., Mille, N., Mills, A., OMalley, M., Schuerger, M., Soder, L.: Wind power myths debunked. IEEE Power and Energy Society (February 2009)
Mohandes, M., Halawani, T., Rehman, S., Hussain, A.A.: Support vector machines for wind speed prediction. Renewable Energy 29(6), 939–947 (2004)
Negnevitsky, M., Mandal, P., Srivastava, A.: Machine learning applications for load, price and wind power prediction in power systems. In: Intelligent System Applications to Power Systems (ISAP), pp. 1–6 (2009)
Potter, C.W., Lew, D., McCaa, J., Cheng, S., Eichelberger, S., Grimit, E.: Creating the dataset for the western wind and solar integration study (u.s.a.). In: 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms (2008)
Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integrated Computer-Aided Engineering 17, 103–115 (2010)
Shi, J., Yang, Y., Wang, P., Liu, Y., Han, S.: Genetic algorithm-piecewise support vector machine model for short term wind power prediction. In: Proceedings of the 8th World Congress on Intelligent Control and Automation, pp. 2254–2258 (2010)
Smola, A.J., Schölkopf, B., Scholkopf, B.: A tutorial on support vector regression (1998)
Steinwart, I., Christmann, A.: Support Vector Machines. Springer, New York (2008)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Zhao, P., Xia, J., Dai, Y., He, J.: Wind speed prediction using support vector regression. In: Industrial Electronics and Applications (ICIEA), pp. 882–886 (2010)
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Kramer, O., Gieseke, F. (2011). Short-Term Wind Energy Forecasting Using Support Vector Regression. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_29
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DOI: https://doi.org/10.1007/978-3-642-19644-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19643-0
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