An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm

Authors

  • Cem Emeksiz Tokat Gaziosmanpasa University
  • Gülden Demir

DOI:

https://doi.org/10.18201/ijisae.2018448459

Keywords:

Bagging algorithm, Data mining, Renewable energy, Wind speed estimation

Abstract

Wind speed is the most important parameter of the wind energy conversion system. Therefore temperature, humiditiy and pressure data, which has significant effect on the wind speed, have become extremely important. In the literature, various models have been used to realize the wind speed estimation. In this study; Six different data mining algorithms were used to determine the effect of meteorological parameters on wind speed estimation. The data were collected from the measurement station established on the campus of Gaziosmanpaşa University. We focused on the bagging algorithm to determine the appropriate combination of wind speed estimates.  The bagging algorithm was used for the first time in estimation of wind speed by taking into account meteorological parameters. To find the most efficiency method on such problem 10-fold cross validation technique was used for comparision. From results, It is concluded that bagging algorithm and temperature-humiditiy-pressure combination showed the best performance. Additionaly, temperature and pressure data are more effective in the wind speed estimation.

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References

REN21, RENEWABLES 2017 GLOBAL STATUS REPORT, 2017, [Online]. Available: http://www.ren21.net/wp-content/uploads/2017/06/178399_GSR_2017_Full_Report_0621_Opt.pdf.; Accessed on June 28 2018.

WEC, Global Wind Statistics, Global Wind Energy Councıl,2016, [Online].Available:http://www.gwec.net/wpcontent/uploads/vip/GWEC_PRstats2016_EN_WEB.pdf.; Accessed on June 28 2018.

I. Kırbas, “Short-term multi-step wind speed prediction using statistical methods and artificial neural networks,” SAKARYA UNIVERSITY JOURNAL OF SCIENCE, 22 (1), 24-38, 2018.

S. H. Mirhashemi, M. Tabatabayi, “Evaluation ten models of weka software to predict the potential evapotranspiration month for the next month in the synoptic weather station Arak,” International Journal of Agriculture and Crop Sciences, 8(1), 5, 2015.

E. Cadenas, W. Rivera, R. Campos-Amezcua, C. Heard, “Wind speed prediction using a univariate ARIMA model and a multivariate NARX model,” Energies, 9(2), 109, 2016.

M. A. Ansari, N. S. Pal, H. Malik, “Wind speed and power prediction of prominent wind power potential states in India using GRNN, In Power Electronics, Intelligent Control and Energy Systems (ICPEICES),” IEEE International Conference on IEEE , 1-6, 2016.

M. Zeng, X. Zhang, J. Li, Q. Meng, “Multifractal analysis of short-term wind speed time series with different sampling frequencies,” 12th World Congress on Intelligent Control and Automation (WCICA), pp. 3213–3218, 2016.

I. Khandelwal, R. Adhikari, G. Verma, “Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition,” Procedia Comput. Sci., vol. 48, pp. 173–179, 2015.

M. Khanna, N. K. Srinath, J. K. Mendiratta, “Feature Extraction of Time Series Data for Wind Speed Power Generation,” 6th International Conference on Advanced Computing (IACC), pp. 169–173, 2016.

H. G. Lee, M. Piao, Y. H. Shin, “Wind Power Pattern Forecasting

Based on Projected Clustering and Classification Methods,” Etri

Journal, 37(2), 283-294, 2015.

Y. Wanga, H. Mab, D. Wanga, G. Wangc, J. Wua, J. Biand, J.

Liud, “A new method for wind speed forecasting based on copula

theory,” Environmental Research, 160, 365–371, 2018.

R. Velo, P. López, F. Maseda, “Wind speed estimation using

multilayer perceptron,” Energy Conversion and Management, 81,

–9, 2014.

M. Timur, F. Aydın, T.Ç. Akıncı, “İstanbul Göztepe Bölgesinin Makine Öğrenmesi Yöntemi ile Rüzgâr Hızının Tahmin Edilmesi,” Makine Teknolojileri Elektronik Dergisi 8(4), 75-80, (in Turkish) 2011.

M. Zontul, F. Aydın, G. Doğan, S. Şener, O. Kaynar, “Wind Speed Forecasting Using REPTree and Bagging Methods in Kirklareli-Turkey,” J. Theor. Appl. Inf. Technol., 56, 17–29, 2013.

A. Meng, J. Ge, H. Yin, S. Chen, “Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm,” Energy Conversion and Management,114, 75-88, 2016.

L. Breiman, “Bagging predictors,” Univ. California, Dept. Stat., Berkeley, Tech. Rep. 421, 1994.

T. Jiang, J. Li, Y. Zheng, C. Sun, “Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges,” Energies, 4, 1087-1101; doi:10.3390/en4071087, 2011.

K. Machová, F. Barčák, P. Bednár, “A Bagging Method using Decision Trees in the Role of Base Classifiers,” Acta Polytechnica Hungarica, Vol. 3, No. 2, 2006.

C. Doukas, I. Maglogiannis, P. Tragas, D. Liapis, G. Yovanof, “Patient fall detection using support vector machines,” International Federation for Information Processing, vol. 247, pp. 147-156, 2007.

J. Platt, “Fast training of support vector machines using sequential minimal optimization,” In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in kernel methods: Support vector learning. Cambridge, MA: MIT Press. 1998.

A. J. Smola, B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, 14(3), 199-222, 2004.

C. K. Madhusudana, H. Kumar, S. Narendranath, “Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal,” Engineering Science and Technology, an International Journal, 19 1543–1551, 2016.

Y. Kumar, G. Sahoo, “Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA,” I.J. Information Technology and Computer Science, 7, 43-49, 2012.

S. Kalmegh, “Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News,” International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 2, February, 2015.

A. Behnood, V. Behnood, M. M. Gharehveran, K. E. Alyamac, “Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm,” Construction and Building Materials, 142, 199–207, 2017.

R. Sharma, S. Kumar, R. Maheshwari, “Comparative Analysis of Classification Techniques in Data Mining Using Different Datasets,” International Journal of Computer Science and Mobile Computing, Vol. 4, Issue. 12, December, pg.125 – 134, 2015.

R. Meenal, A. I. Selvakumar, “Assessment of SVM, Empirical and ANN based solar radiation prediction models with most influencing input parameters,” Renewable Energy, Volume 121, June, Pages 324-343, 2018.

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Published

27.12.2018

How to Cite

Emeksiz, C., & Demir, G. (2018). An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 6(4), 311–321. https://doi.org/10.18201/ijisae.2018448459

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Section

Research Article