Research Article
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Year 2023, Volume: 6 Issue: 2, 19 - 23, 31.12.2023

Fatma Kadriye Düden

Abstract

References

  • Shabbir, N., Ahmadi Ahangar, R., Kutt, L., Iqbal, M. N., Rosin, A. (2019). Forecasting short term wind energy generation using machine learning. In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) (pp. 1-4). IEEE. DOI: 10.1109/RTUCON48111.2019.8982365
  • Isık, A. H., Duden Orgen, F. K., Sirin, C., Tuncer, A. D., Gungor, A. (2019). Prediction of wind blowing durations of Eastern Turkey with machine learning for integration of renewable energy and organic farming‐stock raising. Tecnho-Science, 2, 47-53.
  • Rushdi, M. A., Rushdi, A. A., Dief, T. N., Halawa, A. M., Yoshida, S., Schmehl, R. (2020). Power prediction of airborne wind energy systems using multivariate machine learning. Energies, 13(9), 2367. https://doi.org/10.3390/en13092367.
  • Fugon, L., Juban, J., Kariniotakis, G. (2008). Data mining for wind power forecasting. In European Wind Energy Conference & Exhibition EWEC 2008 (pp. 6-pages). EWEC.
  • Colak, I., Sagiroglu, S., Yesilbudak, M. (2012). Data mining and wind power prediction: A literature review. Renewable Energy, 46, 241-247. https://doi.org/10.1016/j.renene.2012.02.015
  • Marugán, A. P., Márquez, F. P. G., Perez, J. M. P., Ruiz-Hernández, D. (2018). A survey of artificial neural network in wind energy systems. Applied energy, 228, 1822-1836. https://doi.org/10.1016/j.apenergy.2018.07.084.
  • Mora, E., Cifuentes, J., Marulanda, G. (2021). Short-term forecasting of wind energy: A comparison of deep learning frameworks. Energies, 14(23), 7943. https://doi.org/10.3390/en14237943.
  • Wu, T., Snaiki, R. (2022). Applications of machine learning to wind engineering. Frontiers in Built Environment, 8, 811460. https://doi.org/10.3389/fbuil.2022.811460
  • Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188-201. https://doi.org/10.1016/j.energy.2009.09.009
  • Nezhad, M. M., Heydari, A., Neshat, M., Keynia, F., Piras, G., Garcia, D. A. (2022). A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models. Renewable Energy, 190, 156-166. https://doi.org/10.1016/j.renene.2022.03.110
  • Mortezazadeh, M., Zou, J., Hosseini, M., Yang, S., Wang, L. (2022). Estimating urban wind speeds and wind power potentials based on machine learning with city fast fluid dynamics training data. Atmosphere, 13(2), 214. https://doi.org/10.3390/atmos13020214
  • Akpinar, E. K. (2006). A statistical investigation of wind energy potential. Energy Sources, Part A, 28(9), 807-820. https://doi.org/10.1080/009083190928038
  • Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., & Zhang, C. (2020). Short-term wind speed prediction model based on GA-ANN improved by VMD. Renewable Energy, 156, 1373-1388. https://doi.org/10.1016/j.renene.2019.12.047
  • Sareen, K., Panigrahi, B. K., Shikhola, T., Sharma, R. (2023). An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction. Energy, 278, 127799. https://doi.org/10.1016/j.energy.2023.127799
  • Deep, S., Sarkar, A., Ghawat, M., Rajak, M. K. (2020). Estimation of the wind energy potential for coastal locations in India using the Weibull model. Renewable energy, 161, 319-339.
  • Serban, A., Paraschiv, L. S., Paraschiv, S. (2020). Assessment of wind energy potential based on Weibull and Rayleigh distribution models. Energy Reports, 6, 250-267. https://doi.org/10.1016/j.egyr.2020.08.048
  • Sarkar, R., Julai, S., Hossain, S., Chong, W. T., Rahman, M. (2019). A comparative study of activation functions of NAR and NARX neural network for long-term wind speed forecasting in Malaysia. Mathematical Problems in Engineering. https://doi.org/10.1155/2019/6403081
  • Sarkar, M. R., Nahar, M. J., Nadia, A., Halim, M. A., Rafin, S. S. H., Rahman, M. M. (2019). Proficiency assessment of adaptive neuro-fuzzy inference system to predict wind power: A case study of Malaysia. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (pp. 1-5). IEEE.
  • Purohit, S., Ng, E. Y. K., Kabir, I. F. S. A. (2022). Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake. Renewable Energy, 184, 405-420. https://doi.org/10.1016/j.renene.2021.11.097
  • Demolli, H., Dokuz, A. S., Ecemis, A., Gokcek, M. (2019). Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management, 198, 111823. https://doi.org/10.1016/j.enconman.2019.111823
  • Berti, A., Van Zelst, S. J., van der Aalst, W. (2019). Process mining for python (PM4Py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169.
  • Hammoodi, M. S., Al Essa, H. A., Hanon, W. A. (2021, February). The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012133). IOP Publishing. 10.1088/1742-6596/1804/1/012133
  • Gao, W., Alsarraf, J., Moayedi, H., Shahsavar, A., Nguyen, H. (2019). Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Applied Soft Computing, 84, 105748. https://doi.org/10.1016/j.biombioe.2009.12.016
  • Al-Rousan, N., Al-Najjar, H., Alomari, O. (2021). Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods. Sustainable Energy Technologies and Assessments, 44, 100923. https://doi.org/10.1016/j.seta.2020.100923
  • Reiss, M. A., Temmer, M., Veronig, A. M., Nikolic, L., Vennerstrom, S., Schongassner, F., Hofmeister, S. J. (2016). Verification of high‐speed solar wind stream forecasts using operational solar wind models. Space Weather, 14(7), 495-510. https://doi.org/10.1002/2016SW001390

WIND SPEED PREDICTION USING DATA MINING APPROACHES: A CASE STUDY OF GÖKÇEADA, TURKEY

Year 2023, Volume: 6 Issue: 2, 19 - 23, 31.12.2023

Fatma Kadriye Düden

Abstract

In this study, the meteorology data set covering the wind speed, humidity, pressure and temperature data between the years 2014-2021 obtained from the Turkish State Meteorological Service is utilized. With this data set, an estimation is made for the Gokceada district in Canakkale-Turkey, with the WEKA software as pressure and temperature inputs and wind speed output. Gaussian Processes, Linear Regression, Multilayer Perceptron, Simple Linear Regression, SMOreg, Kstar, Decision Table, M5P algorithms in WEKA software are used for estimation. It is made for 7 different groups as temperature-pressure-humidity, temperature-pressure, temperature-humidity, humidity-pressure, temperature, pressure and humidity. According to the results, the best estimation for the temperature-pressure-humidity group is found to be 0.999 for the CC (correlation coefficient) value and 0.2994 for the RMSE (root-mean-square error) with the Kstar algorithm. For the temperature-humidity group, the CC value is 0.9607 and the RMSE value is 0.2777. Estimates from the temperature-pressure and humidity-pressure groups is not give accurate results in comparison to the other groups. The CC and RMSE results are obtained from the humidity and pressure groups are found to be 0.9998 and 0.9985, 0.2679 and 0.0464, respectively.

Keywords

Data mining Kstar wind speed prediction Gokceada: Turkey

Supporting Institution

Turkish State Meteorological Service

Thanks

Thanks for data to Turkish State Meteorological Service

References

  • Shabbir, N., Ahmadi Ahangar, R., Kutt, L., Iqbal, M. N., Rosin, A. (2019). Forecasting short term wind energy generation using machine learning. In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) (pp. 1-4). IEEE. DOI: 10.1109/RTUCON48111.2019.8982365
  • Isık, A. H., Duden Orgen, F. K., Sirin, C., Tuncer, A. D., Gungor, A. (2019). Prediction of wind blowing durations of Eastern Turkey with machine learning for integration of renewable energy and organic farming‐stock raising. Tecnho-Science, 2, 47-53.
  • Rushdi, M. A., Rushdi, A. A., Dief, T. N., Halawa, A. M., Yoshida, S., Schmehl, R. (2020). Power prediction of airborne wind energy systems using multivariate machine learning. Energies, 13(9), 2367. https://doi.org/10.3390/en13092367.
  • Fugon, L., Juban, J., Kariniotakis, G. (2008). Data mining for wind power forecasting. In European Wind Energy Conference & Exhibition EWEC 2008 (pp. 6-pages). EWEC.
  • Colak, I., Sagiroglu, S., Yesilbudak, M. (2012). Data mining and wind power prediction: A literature review. Renewable Energy, 46, 241-247. https://doi.org/10.1016/j.renene.2012.02.015
  • Marugán, A. P., Márquez, F. P. G., Perez, J. M. P., Ruiz-Hernández, D. (2018). A survey of artificial neural network in wind energy systems. Applied energy, 228, 1822-1836. https://doi.org/10.1016/j.apenergy.2018.07.084.
  • Mora, E., Cifuentes, J., Marulanda, G. (2021). Short-term forecasting of wind energy: A comparison of deep learning frameworks. Energies, 14(23), 7943. https://doi.org/10.3390/en14237943.
  • Wu, T., Snaiki, R. (2022). Applications of machine learning to wind engineering. Frontiers in Built Environment, 8, 811460. https://doi.org/10.3389/fbuil.2022.811460
  • Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188-201. https://doi.org/10.1016/j.energy.2009.09.009
  • Nezhad, M. M., Heydari, A., Neshat, M., Keynia, F., Piras, G., Garcia, D. A. (2022). A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models. Renewable Energy, 190, 156-166. https://doi.org/10.1016/j.renene.2022.03.110
  • Mortezazadeh, M., Zou, J., Hosseini, M., Yang, S., Wang, L. (2022). Estimating urban wind speeds and wind power potentials based on machine learning with city fast fluid dynamics training data. Atmosphere, 13(2), 214. https://doi.org/10.3390/atmos13020214
  • Akpinar, E. K. (2006). A statistical investigation of wind energy potential. Energy Sources, Part A, 28(9), 807-820. https://doi.org/10.1080/009083190928038
  • Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., & Zhang, C. (2020). Short-term wind speed prediction model based on GA-ANN improved by VMD. Renewable Energy, 156, 1373-1388. https://doi.org/10.1016/j.renene.2019.12.047
  • Sareen, K., Panigrahi, B. K., Shikhola, T., Sharma, R. (2023). An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction. Energy, 278, 127799. https://doi.org/10.1016/j.energy.2023.127799
  • Deep, S., Sarkar, A., Ghawat, M., Rajak, M. K. (2020). Estimation of the wind energy potential for coastal locations in India using the Weibull model. Renewable energy, 161, 319-339.
  • Serban, A., Paraschiv, L. S., Paraschiv, S. (2020). Assessment of wind energy potential based on Weibull and Rayleigh distribution models. Energy Reports, 6, 250-267. https://doi.org/10.1016/j.egyr.2020.08.048
  • Sarkar, R., Julai, S., Hossain, S., Chong, W. T., Rahman, M. (2019). A comparative study of activation functions of NAR and NARX neural network for long-term wind speed forecasting in Malaysia. Mathematical Problems in Engineering. https://doi.org/10.1155/2019/6403081
  • Sarkar, M. R., Nahar, M. J., Nadia, A., Halim, M. A., Rafin, S. S. H., Rahman, M. M. (2019). Proficiency assessment of adaptive neuro-fuzzy inference system to predict wind power: A case study of Malaysia. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (pp. 1-5). IEEE.
  • Purohit, S., Ng, E. Y. K., Kabir, I. F. S. A. (2022). Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake. Renewable Energy, 184, 405-420. https://doi.org/10.1016/j.renene.2021.11.097
  • Demolli, H., Dokuz, A. S., Ecemis, A., Gokcek, M. (2019). Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management, 198, 111823. https://doi.org/10.1016/j.enconman.2019.111823
  • Berti, A., Van Zelst, S. J., van der Aalst, W. (2019). Process mining for python (PM4Py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169.
  • Hammoodi, M. S., Al Essa, H. A., Hanon, W. A. (2021, February). The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012133). IOP Publishing. 10.1088/1742-6596/1804/1/012133
  • Gao, W., Alsarraf, J., Moayedi, H., Shahsavar, A., Nguyen, H. (2019). Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Applied Soft Computing, 84, 105748. https://doi.org/10.1016/j.biombioe.2009.12.016
  • Al-Rousan, N., Al-Najjar, H., Alomari, O. (2021). Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods. Sustainable Energy Technologies and Assessments, 44, 100923. https://doi.org/10.1016/j.seta.2020.100923
  • Reiss, M. A., Temmer, M., Veronig, A. M., Nikolic, L., Vennerstrom, S., Schongassner, F., Hofmeister, S. J. (2016). Verification of high‐speed solar wind stream forecasts using operational solar wind models. Space Weather, 14(7), 495-510. https://doi.org/10.1002/2016SW001390
There are 25 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Original Research Articles
Authors

Fatma Kadriye Düden 0000-0002-8911-1641

Publication Date December 31, 2023
Acceptance Date November 26, 2023
Published in Issue Year 2023 Volume: 6 Issue: 2

Cite

APA Düden, F. K. (2023). WIND SPEED PREDICTION USING DATA MINING APPROACHES: A CASE STUDY OF GÖKÇEADA, TURKEY. Scientific Journal of Mehmet Akif Ersoy University, 6(2), 19-23.
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