Research Article
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Year 2021, Volume: 8 Issue: 1, 6 - 11, 31.03.2021

Mehmet Şen Muciz Özcan

https://doi.org/10.31593/ijeat.800937
Cited By: 3

Abstract

References

  • Wai, R., J., Wang, W., H., Lin, C., Y. 2008. High- Performance Stand-Alone Photovoltaic Generation System. IEEE Transactions On Industrial Electronics, 55(1),240-250.
  • Özcan M., Ünlersen M. F., Mutluer M. 2018. Financial Analysis Of The Solar Energy Plant Established In Konya Using The Production Data. 4th Int. Conference on Engineering and Natural Science(ICENS 2018), 2-6 May, Kyiv, Ukranie, 92-92.
  • Orhan Y., Özcan M. 2019. Turkey's 2023 Target in Electricity Generation. The International Aluminium-Themed Engineering and Natural Sciences Conference (IATENS’19), 4-6 October, Seydişehir, Turkey, 259-263.
  • Azad A. K., Rasul M. G., Islam R., and Shishir I. R. 2015. Analysis of Wind Energy Prospect for Power Generation by Three Weibull Distribution Methods. Energy Procedia, 75, 722-727.
  • Uzun Y., Özcan M. 2020. Rule extraction and performance estimation by using variable neighborhood search for solar power plant in Konya. Turkish Journal of Electrical Engineering and Computer Science, 28(2), 635-645.
  • Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen,H. ve Feitosa, E. 2008. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews, 12(1),1725-1744.
  • Lei M., Shiyan L., Chuanwen J., Hongling L. ve Yan Z. 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews, 13(4):915-920.
  • Giebel, G., Landberg, L., Kariniotakis, G. ve Brownsword, R. State-of-the-art on methods and software tools for short-term prediction of wind energy production. Proceedings of European wind energy conference, Madrid, 2003.
  • T.C. Meteoroloji Genel Müdürlüğü, 2019. https://www.meteoblue.com/tr/hava/historyclimate/weatherarchive/konya_t%c3%bcrkiye_306571 .(04 June 2020.)
  • Skapura, D. M. Building Neural Networks Addison-Wesley, New York 1996.
  • Cigizoglu, H.K., and Kisi, O. 2005. Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrology, 36 (1), 49-64.
  • Haykin, S. Neural Networks: A Comprehensive Foundation MacMillan. New York 1994.
  • Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul 2003.
  • Okkan U., Mollamahmutoğlu A., 2010. Yiğitler Çayı Günlük Akımlarının Yapay Sinir Ağları ve Regresyon Analizi ile Modellenmesi, DPÜ Fen Bilimleri Enstitüsü Dergisi 23(1), 33-48.
  • Kahramanlı H and Allahverdi N. 2009. Rule extraction from trained adaptive neural networks using articial immune systems. Expert Systems with Applications, 36(1), 1513-1522.
  • Cong Chen T., D. Jian Han, F. T. K. Au, L. G. Than. 2003. Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate. IEEE Trans. on Neural Net., 3(6), 1873-1878.
  • Marghny H. 2011. Rules extraction from constructively trained neural networks based on genetic algorithms. Journal Neuro Computing, 74(17), 3180-3192.
  • Minns, A.W. and Hall, M.J. 1996. Artificial neural networks as rainfall runoff models. Hydrological Sciences Journal, 41(3), 399-417.
  • Sharma R. and Suhag S. 2017. Novel control strategy for hybrid renewable energy-based standalone system. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3), 2261-2277.
  • Hsu, K., Gupta, H.V. and Sorooshian, S. 1995. Artificial neural network modelling of the rainfall runoff process. Water Res. Research, 31(1), 2517-2530.
  • Şen, M. 2020. “Rüzgâr Enerji Santrallerinin Modellenmesi ve Kısa Devre Analizi”. Master. thesis, Necmettin Erbakan University, Institure of Sciences, Konya, Turkey, 31-44.
  • Raju L, Sakaya M and Mahadevan S. 2017. Implementation of energy management and demand side management ofa solar microgrid using a hybrid platform. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3), 2219-2231.
  • Campolo, M., Andreussi, P. and Soldati, A. 1999. River flood forecasting with a neural network model. Water Resources Research, 35(1), 1191-1197.

Maximum wind speed forecasting using historical data and artificial neural networks modeling

Year 2021, Volume: 8 Issue: 1, 6 - 11, 31.03.2021

Mehmet Şen Muciz Özcan

https://doi.org/10.31593/ijeat.800937
Cited By: 3

Abstract

Estimation of the wind speed makes a very important contribution to the seamless integration of wind power plants into the grid. In this way, the maximum amount of electricity can be generated by estimating the amount of energy that can be generated from wind energy. The measurements of the wind speed in the region, where the plant is plant to be established, made before the installation of the wind power plants (WPP), takes between 6 and 18 months. In this study, it was investigated what could be done to make a foresight and estimation about the wind speed in the future for the selected region. In order to accurately determine the wind speed, it was tried to be estimated by using artificial neural networks (ANN) included in the MATLAB package program. In this study, 365 data belonging to the previous years of the region to be studied were provided and used to train the ANN of the planned study. In practice, the parameters of temperature, humidity and pressure, which are among the factors affecting wind speed, were taken into consideration. An R value of 91.20% in the training phase, 93.04% in the validation phase and 92.76% in the test phase was obtained. High accuracy values were obtained at all phases and it was shown in this study that ANN can estimate reliably without memorizing.

Keywords

ANN Artificial Neural Network Prediction Renewable sources Wind Speed

References

  • Wai, R., J., Wang, W., H., Lin, C., Y. 2008. High- Performance Stand-Alone Photovoltaic Generation System. IEEE Transactions On Industrial Electronics, 55(1),240-250.
  • Özcan M., Ünlersen M. F., Mutluer M. 2018. Financial Analysis Of The Solar Energy Plant Established In Konya Using The Production Data. 4th Int. Conference on Engineering and Natural Science(ICENS 2018), 2-6 May, Kyiv, Ukranie, 92-92.
  • Orhan Y., Özcan M. 2019. Turkey's 2023 Target in Electricity Generation. The International Aluminium-Themed Engineering and Natural Sciences Conference (IATENS’19), 4-6 October, Seydişehir, Turkey, 259-263.
  • Azad A. K., Rasul M. G., Islam R., and Shishir I. R. 2015. Analysis of Wind Energy Prospect for Power Generation by Three Weibull Distribution Methods. Energy Procedia, 75, 722-727.
  • Uzun Y., Özcan M. 2020. Rule extraction and performance estimation by using variable neighborhood search for solar power plant in Konya. Turkish Journal of Electrical Engineering and Computer Science, 28(2), 635-645.
  • Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen,H. ve Feitosa, E. 2008. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews, 12(1),1725-1744.
  • Lei M., Shiyan L., Chuanwen J., Hongling L. ve Yan Z. 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews, 13(4):915-920.
  • Giebel, G., Landberg, L., Kariniotakis, G. ve Brownsword, R. State-of-the-art on methods and software tools for short-term prediction of wind energy production. Proceedings of European wind energy conference, Madrid, 2003.
  • T.C. Meteoroloji Genel Müdürlüğü, 2019. https://www.meteoblue.com/tr/hava/historyclimate/weatherarchive/konya_t%c3%bcrkiye_306571 .(04 June 2020.)
  • Skapura, D. M. Building Neural Networks Addison-Wesley, New York 1996.
  • Cigizoglu, H.K., and Kisi, O. 2005. Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrology, 36 (1), 49-64.
  • Haykin, S. Neural Networks: A Comprehensive Foundation MacMillan. New York 1994.
  • Öztemel, E., Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul 2003.
  • Okkan U., Mollamahmutoğlu A., 2010. Yiğitler Çayı Günlük Akımlarının Yapay Sinir Ağları ve Regresyon Analizi ile Modellenmesi, DPÜ Fen Bilimleri Enstitüsü Dergisi 23(1), 33-48.
  • Kahramanlı H and Allahverdi N. 2009. Rule extraction from trained adaptive neural networks using articial immune systems. Expert Systems with Applications, 36(1), 1513-1522.
  • Cong Chen T., D. Jian Han, F. T. K. Au, L. G. Than. 2003. Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate. IEEE Trans. on Neural Net., 3(6), 1873-1878.
  • Marghny H. 2011. Rules extraction from constructively trained neural networks based on genetic algorithms. Journal Neuro Computing, 74(17), 3180-3192.
  • Minns, A.W. and Hall, M.J. 1996. Artificial neural networks as rainfall runoff models. Hydrological Sciences Journal, 41(3), 399-417.
  • Sharma R. and Suhag S. 2017. Novel control strategy for hybrid renewable energy-based standalone system. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3), 2261-2277.
  • Hsu, K., Gupta, H.V. and Sorooshian, S. 1995. Artificial neural network modelling of the rainfall runoff process. Water Res. Research, 31(1), 2517-2530.
  • Şen, M. 2020. “Rüzgâr Enerji Santrallerinin Modellenmesi ve Kısa Devre Analizi”. Master. thesis, Necmettin Erbakan University, Institure of Sciences, Konya, Turkey, 31-44.
  • Raju L, Sakaya M and Mahadevan S. 2017. Implementation of energy management and demand side management ofa solar microgrid using a hybrid platform. Turkish Journal of Electrical Engineering & Computer Sciences, 25(3), 2219-2231.
  • Campolo, M., Andreussi, P. and Soldati, A. 1999. River flood forecasting with a neural network model. Water Resources Research, 35(1), 1191-1197.
There are 23 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Article
Authors

Mehmet Şen 0000-0001-7609-2210

Muciz Özcan 0000-0001-5277-6650

Publication Date March 31, 2021
Submission Date September 28, 2020
Acceptance Date December 23, 2020
Published in Issue Year 2021 Volume: 8 Issue: 1

Cite

APA Şen, M., & Özcan, M. (2021). Maximum wind speed forecasting using historical data and artificial neural networks modeling. International Journal of Energy Applications and Technologies, 8(1), 6-11. https://doi.org/10.31593/ijeat.800937
AMA Şen M, Özcan M. Maximum wind speed forecasting using historical data and artificial neural networks modeling. IJEAT. March 2021;8(1):6-11. doi:10.31593/ijeat.800937
Chicago Şen, Mehmet, and Muciz Özcan. “Maximum Wind Speed Forecasting Using Historical Data and Artificial Neural Networks Modeling”. International Journal of Energy Applications and Technologies 8, no. 1 (March 2021): 6-11. https://doi.org/10.31593/ijeat.800937.
EndNote Şen M, Özcan M (March 1, 2021) Maximum wind speed forecasting using historical data and artificial neural networks modeling. International Journal of Energy Applications and Technologies 8 1 6–11.
IEEE M. Şen and M. Özcan, “Maximum wind speed forecasting using historical data and artificial neural networks modeling”, IJEAT, vol. 8, no. 1, pp. 6–11, 2021, doi: 10.31593/ijeat.800937.
ISNAD Şen, Mehmet - Özcan, Muciz. “Maximum Wind Speed Forecasting Using Historical Data and Artificial Neural Networks Modeling”. International Journal of Energy Applications and Technologies 8/1 (March 2021), 6-11. https://doi.org/10.31593/ijeat.800937.
JAMA Şen M, Özcan M. Maximum wind speed forecasting using historical data and artificial neural networks modeling. IJEAT. 2021;8:6–11.
MLA Şen, Mehmet and Muciz Özcan. “Maximum Wind Speed Forecasting Using Historical Data and Artificial Neural Networks Modeling”. International Journal of Energy Applications and Technologies, vol. 8, no. 1, 2021, pp. 6-11, doi:10.31593/ijeat.800937.
Vancouver Şen M, Özcan M. Maximum wind speed forecasting using historical data and artificial neural networks modeling. IJEAT. 2021;8(1):6-11.

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