The prediction of the wind speed at different heights by machine learning methods
DOI:
https://doi.org/10.11121/ijocta.01.2016.00315Keywords:
Wind speed prediction, support vector machines, wind farm investmentAbstract
In Turkey, many enterprisers started to make investment on renewable energy systems after new legal regulations and stimulus packages about production of renewable energy were introduced. Out of many alternatives, production of electricity via wind farms is one of the leading systems. For these systems, the wind speed values measured prior to the establishment of the farms are extremely important in both decision making and in the projection of the investment. However, the measurement of the wind speed at different heights is a time consuming and expensive process. For this reason, the success of the techniques predicting the wind speeds is fairly important in fast and reliable decision-making for investment in wind farms. In this study, the annual wind speed values of Kutahya, one of the regions in Turkey that has potential for wind energy at two different heights, were used and with the help of speed values at 10 m, wind speed values at 30 m of height were predicted by seven different machine learning methods. The results of the analysis were compared with each other. The results show that support vector machines is a successful technique in the prediction of the wind speed for different heights.
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References
İlkiliç, C. Wind Energy and Assessment of Wind Energy Potential in Turkey, Renewable and Sustainable Energy Reviews, 16(2), 1165-1173, (2012). Crossref
2015-2019 Strategic Plan of the Energy and Natural Sources, Turkey's Ministry of Energy and Natural Sources, http://www.enerji.gov.tr/File/?path=ROOT%2f1%2fDocuments%2fStrategic+Plan %2fStrategicPlan2015-2019.pdf.
World Energy Council, World Energy Resources: 2013 Survey, Chapter 10 Wind, https://www.worldenergy.org/wp-content/uploads/2013/09/Complete_WER_2013_Survey.pdf
Turkish Wind Energy Statistics Report, January 2016, Turkish Wind Energy Association, http://www.tureb.com.tr/publications/turkish-wind-energy-statistics-report-january-2016 (Available: 5.4.2016).
Manwell, J., McGowan, J. and Rogers, A., Wind Energy Explained: Theory, Design, and Application. John Wiley & Sons, West Sussex, (2009). Crossref
Lei, M., Shiyan, M., Chuanwen, J., Hongling, L. and Yan, Z., A Review on the Forecasting of Wind Speed and Generated Power, Renewable and Sustainable Energy Reviews, 13, 915–920, (2009). Crossref
Mohandes, M.A, Rehman, S. and Halawani, T.O., A Neural Networks Approach for Wind Speed Prediction, Renewable Energy, 13(3), 345-54, (1998). Crossref
Mohandes, M.A., Halawani, T.O., Rehman, S. and Hussain, A.A., Support Vector Machines for Wind Speed Prediction, Renewable Energy, 29(6), 939-47, (2004). Crossref
Damousis, I.G., Alexiadis, M.C., Theocharis, J.B. and Dokopoulos, P.S., A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks using Spatial Correlation, Energy Conversion, 19(2), 352-61, (2004). Crossref
Kurban, M., Hocaoğlu, F. O. and Kantar, Y. M., Rüzgar Enerjisi Potansiyelinin Tahmininde Kullanılan İki Farklı Istatistiksel Dağılımın Karşılaştırmalı Analizi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 13(1), 103-109, (2007).
Shi, J., Guo, J. and Zheng, S., Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series, Renewable and Sustainable Energy Reviews, 16, 3471-3480, (2012). Crossref
Tüfekci, P., Prediction of Full Load Electrical Power Output of a Base Load Operated Combined Cycle Power Plant using Machine Learning Methods, International Journal of Electrical Power & Energy Systems, 60, 126-140, (2014). Crossref
Hornik, K., Christian, B. and Achim, Z., Open-source Machine Learning: R Meets Weka, Computational Statistics, 24(2), 225-232, (2009). Crossref
Vapnik, V., The Nature of Statistical Learning Theory, Springer, NY, (1995). Crossref
Pai, P.F., Yang, S.L. and Chang, P.T., Forecasting Output of Integrated Circuit Industry by Support Vector Regression Models with Marriage Honey-bees Optimization Algorithms, Expert Systems with Applications, 36, 10746-10751, (2009). Crossref
Castro-Neto, M., Jeong, Y.S., Jeong, M.K. and Han, L.D., Online-SVR for Shortterm Traffic Flow Prediction under Typical and Atypical Traffic Conditions, Expert Systems with Applications, 36, 6164-6173, (2009). Crossref
Hsu, S.H., Hsieh, J.J.P., Chih, T.C. and Hsu, K.C., A Two-stage Architecture for Stock Price Forecasting by Integrating Self-organizing Map and Support Vector Regression, Expert Systems with Applications, 36, 7947-7951, (2009). Crossref
Huang, C.L. and Tsai, C.Y., A Hybrid SOFM–SVR with a Filter-based Feature Selection for Stock Market Forecasting, Expert Systems with Applications, 36, 1529-1539, (2009). Crossref
Salcedo-Sanz, S., Ortiz-Garci, E.G., Perez-Bellido, A.M., Portilla-Figueras, J.A., Prieto, L., Paredes, D., et al., Performance Comparison of Multilayer Perceptrons and Support Vector Machines in a Short-term Wind Speed Prediction Problem, Neural Network World, 19(1), 37-51, (2009).
Erdal, H., Makine Öğrenmesi Yöntemlerinin İnşaat Sektörüne Katkısı: Basınç Dayanımı Tahminlemesi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 21(3), 109-114, (2015).
Yapraklı, T.Ş. and Erdal, H., Firma Başarısızlığı Tahminlemesi: Makine Öğrenmesine Dayalı Bir Uygulama, Bilişim Teknolojileri Dergisi, 9(1), 21-31, (2016).
Erdal, H.I., Namli, E., Yumurtaci Aydogmus, H. and Turkan, Y.S., Comparing Ensembles of Decision Trees and Neural Networks for One-day-ahead Stream Flow Prediction, Scientific Research Journal, I(IV), 43-55, (2013).
Elish, M. O., A Comparative Study of Fault Density Prediction in Aspect-oriented Systems using MLP, RBF, KNN, RT, DENFIS and SVR Models, Artificial Intelligence Review, 42(4), 695-703, (2014). Crossref
Lek, S. and Park, Y.S., Multilayer Perceptron. In: Jorgensen, S.E., Fath, B., (Eds.), Encyclopedia of Ecology, Academic Press, Oxford, 2455-2462, (2008). Crossref
Yumurtaci Aydogmus, H., Ekinci, A., Erdal, H.İ. and Erdal, H., Optimizing the Monthly Crude Oil Price Forecasting Accuracy via Bagging Ensemble Models, Journal of Economics and International Finance, 7(5), 127-136, (2015). Crossref
Yumurtaci Aydogmus, H., Erdal, H.İ., Karakurt, O., Namli, E., Turkan, Y.S. and Erdal, H., A Comparative Assessment of Bagging Ensemble Models for Modeling Concrete Slump Flow, Computers and Concrete, 16(5), 741-757, (2015). Crossref
Erdal, H.I. and Ekinci, A., A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures, Computational Economics, 42(2), 199-215, (2013). Crossref
Karlik, B. and Olgac, A.V., Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks, International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122, (2011).
Crouch, C., Crouch, D., Maclin, R. and Polumetla, A., Automatic Detection of RWIS Sensor Malfunctions (Phase I). Final Report, Published by Intelligent Transportation Systems Institute Center for Transportation Studies University of Minnesota, CTS 09-10, (2009).
Orr, M.J.L., Introduction to Radial Basis Function Networks: Centre for Cognitive Science, University of Edinburgh, 9-40, (1996).
Cleary, J.G. and Trigg, L.E., K*: An Instance-based Learner using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, (1995). Crossref
Painuli, S., Elangovan, M. and Sugumaran, V., Tool Condition Monitoring using K-star Algorithm, Expert Systems with Applications, 41, 2638-2643, (2014). Crossref
Jiawei, H. and Kamber, M., Data Mining: Concepts and Techniques. San Francisco, CA, itd: Morgan Kaufmann, 5, (2001).
Arif, M., Ishihara, T. and Inooka, H., Incorporation of Experience in Iterative Learning Controllers using Locally Weighted Learning, Automatica, 37, 881-888, (2001). Crossref
Witten, I.H., Frank, E. and Hall, M.A., Data Mining Practical Machine Learning Tools and Techniques, Third Edition, San Francisco, Morgan Kaufmann, (2011).
Csépe, Z., Makra, L., Voukantsis, D., Matyasovszky, I., Tusnády, G., Karatzas, K. and Thibaudon, M., Predicting Daily Ragweed Pollen Concentrations using Computational Intelligence Techniques over Two Heavily Polluted Areas in Europe, Science of The Total Environment, 476, 542-552, (2014). Crossref
Iba, W.L., Induction of One-level Decision Trees. In: Ninth International Conference on Machine Learning, Aberdeen, (1992). Crossref
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