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Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast

Year 2024, Volume: 39 Issue: 2, 287 - 300, 11.07.2024
https://doi.org/10.21605/cukurovaumfd.1513981

Abstract

This paper proposes a deep recurrent neural network (DRNN) approach to model the one-hour-ahead wind speed forecasting by using various meteorological sensory data from the North Wyke farm platform (NWFP). To refine model input, mutual information analysis is applied to eliminate irrelevant sensory data. The DRNN architecture employs three recurrent layers Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and simple Recurrent Neural Network (RNN) to capture temporal relationships. The proposed networks are tested using real-life, one-year data from the NWFP. The results showed a strong correlation between the actual and predicted wind speed for LSTM, GRU, and RNN layers-based DRNN, however, simple RNN slightly outperformed the other two recurrent layers. The distribution of the network errors over the year is also analyzed. Although the observed meteorological data between the years was from different distributions, the proposed network generalized well even though these data were altered due to global warming.

References

  • 1. Ahmed, A., Khalid, M., 2019. A Review on the Selected Applications of Forecasting Models in Renewable Power Systems, Renewable and Sustainable Energy Reviews, 100, 9-21.
  • 2. Chen, Y., Dong, Z., Wang, Y., Su, J., Han, Z., Zhou, D., Zhang, K., Zhao, Y., Bao, Y., 2021. Short-term Wind Speed Predicting Framework Based on the Eemd-ga-lstm Method under Large-scale Wind History. Energy Conversion and Management, 227, 113559.
  • 3. Hayes, L., Stocks, M., Blakers, A., 2021. Accurate Longterm Power Generation Model for Offshore Wind Farms in Europe using Era5 Reanalysis. Energy, 229, 120603.
  • 4. Deng, X., Shao, H., Hu, C., Jiang, D., Jiang, Y., 2020. Wind Power Forecasting Methods Based on Deep Learning: A Survey. Computer Modeling in Engineering & Sciences, 122(1), 273-301.
  • 5. Mi, X., Liu, H., Li, Y., 2019. Wind Speed Prediction Model Using Singular Spectrum Analysis, Empirical Mode Decomposition and Convolutional Support Vector Machine. Energy Conversion and Management, 180, 196-205.
  • 6. Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z., 2009. A Review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13(4), 915-920.
  • 7. Azimi, R., Ghofrani, M., Ghayekhloo, M., 2016. A Hybrid Wind Power Forecasting Model Based on Data Mining and Wavelets Analysis. Energy Conversion and Management, 127, 208-225.
  • 8. Santhosh, M., Venkaiah, C., Vinod K.D.M., 2020. Current Advances and Approaches in Wind Speed and Wind Power Forecasting for Improved Renewable Energy Integration: A Review. Engineering Reports, 2(6), e12178.
  • 9. Lipu, M.S.H., Miah, M.S., Hannan, M.A., Hussain, A., Sarker, M.R., Ayob, A., Saad, M. H.M., Mahmud, M.S., 2021. Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects. IEEE Access, 9, 102460-102489.
  • 10. Puri V., Kumar, N., 2021. Wind Energy Forecasting Using Artificial Neural Network in Himalayan Region. Modeling Earth Systems and Environment, 1-10.
  • 11. Li, L.L., Chang, Y.B., Tseng, M.L., Liu J.Q., Lim, M.K., 2020. Wind Power Prediction Using a Novel Model on Wavelet Decomposition- Support Vector Machines-Improved Atomic Search Algorithm. Journal of Cleaner Production, 270, 121817.
  • 12. Sfetsos, A. 2000. A Comparison of Various Forecasting Techniques Applied to Mean Hourly Wind Speed Time Series. Renewable Energy, 21(1), 23-35.
  • 13. Lin, W., Wu, Z., Lin, L., Wen, A., Li, J., 2017. An Ensemble Random Forest Algorithm for Insurance Big Data Analysis. IEEE Access, 5, 16568–16575.
  • 14. Tian, Z., Li, S., Wang, Y., 2020. A Prediction Approach Using Ensemble Empirical Mode Decomposition-Permutation Entropy and Regularized Extreme Learning Machine for Short-term Wind Speed. Wind Energy, 23(2), 177-206.
  • 15. Huang, G.B., Zhu, Q.Y., Siew, C.K, 2006. Extreme Learning Machine: Theory and Applications. Neurocomputing, 70(1), 489-501.
  • 16. Afrasiabi, M., Mohammadi, M., Rastegar, M., Afrasiabi, S., 2021. Advanced Deep Learning Approach for Probabilistic Wind Speed Forecasting. IEEE Transactions on Industrial Informatics, 17(1), 720-727.
  • 17. Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., Zheng, M., 2019. Wind Power Short-term Prediction Based on lstm and Discrete Wavelet Transform. Applied Sciences, 9(6).
  • 18. Liu, H., Mi, X., Li, Y., 2018. Smart Multi-step Deep Learning Model for Wind Speed Forecasting Based on Variational Mode Decomposition, Singular Spectrum Analysis, lstm Network and Elm. Energy Conversion and Management, 59, 54-64.
  • 19. Ding, M., Zhou, H., Xie, H., Wu, M., Nakanishi, Y., Yokoyama, R., 2019. A Gated Recurrent Unit Neural Networks Based Wind Speed Error Correction Model for Short-term Wind Power Forecasting. Neurocomputing, 365, 54-61.
  • 20. Kisvari, A., Lin, Z., Liu, X., 2021. Wind Power Forecasting a Data-driven Method Along with Gated Recurrent Neural Network. Renewable Energy, 163, 1895-1909.
  • 21. Duan, J., Zuo, H., Bai, Y., Duan, J., Chang, M., Chen, B., 2021. Short-term Wind Speed Forecasting Using Recurrent Neural Networks with Error Correction. Energy, 217, 119397.
  • 22. Wang, L., Li, X., Bai, Y., 2018. Short-term Wind Speed Prediction Using an Extreme Learning Machine Model with Error Correction. Energy Conversion and Management, 162, 239-250.
  • 23. Memarzadeh, G., Keynia, F., 2020. A New Short-term Wind Speed Forecasting Method Based on Fine-tuned lstm Neural Network and Optimal Input Sets. Energy Conversion and Management, 213, 112824.
  • 24. Liu, H., Mi, X., Li, Y., Duan, Z., Xu, Y., 2019. Smart Wind Speed Deep Learning-based Multi-Step Forecasting Model Using Singular Spectrum Analysis, Convolutional Gated Recurrent Unit Network and Support Vector Regression. Renewable Energy, 43, 842-854.
  • 25. Yu, C., Li, Y., Zhang, M., 2017. An Improved Wavelet Transform Using Singular Spectrum Analysis for Wind Speed Forecasting Based on Elman Neural Network. Energy Conversion and Management, 148, 895-904.
  • 26. Yu, C., Li, Y., Bao, Y., Tang, H., Zhai, G., 2018. A Novel Framework for Wind Speed Prediction Based on Recurrent Neural Networks and Support Vector Machine. Energy Conversion and Management, 178, 137-145.
  • 27. Orr, R.J., Griffith, B.A., Rose, S., Hatch, D., Hawkins, J., Murray, P.J., 2011. Designing and Creating the North Wyke Farm Platform. Catchment Science.
  • 28. Hawkin, J., 2015. Design, Establishment and Development, http://resources.rothamsted.ac.uk /sites/default/files/groups/NorthWykeFarmPlatform/FPUG.Doc.001EstabDevelopver1.5.pdf, Access date: 11/02/2023.
  • 29. Boden, M., 2002. A Guide to Recurrent Neural Networks and Backpropagation. The Dallas Project.
  • 30. Elman, J.L., 1990. Finding Structure in Time. Cognitive Science, 14(2), 179-211.
  • 31. Goodfellow, I., Bengio, Y., Courville, 2016. Deep Learning. MIT Press.
  • 32. Hochreiter, S., Schmidhuber, J., 1997. Long Short-term Memory. Neural Computation, 9(8), 1735-1780.
  • 33. Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelling. arXiv Preprint arXiv:1412.3555.
  • 34. Kaplan, D., Glass, L., 1997. Understanding Nonlinear Dynamics. Springer Science & Business Media.
  • 35. Chollet, F., 2017. Deep Learning with Python. Manning.
  • 36. Werbos, P.J., 1990. Backpropagation Through Time: What it Does and How to do It. Proceedings of the IEEE, 78(10), 1550-1560.

Derin Tekrarlayan Sinir Ağları ve Çiftlik Platformu Özellikleri Kullanılarak Bir Saat Önceden Rüzgâr Hızı Tahmini

Year 2024, Volume: 39 Issue: 2, 287 - 300, 11.07.2024
https://doi.org/10.21605/cukurovaumfd.1513981

Abstract

Bu makale, Kuzey Wyke çiftliği platformundan (NWFP) çeşitli meteorolojik veriler kullanarak bir saat öncesine yönelik rüzgâr hızı tahmini modellemek için derin tekrarlı sinir ağı (DRNN) yaklaşımını önermektedir. Model girişini iyileştirmek için karşılıklı bilgi analizi kullanılarak ilgisi olmayan veriler elenmiştir. DRNN mimarisi, zamansal ilişkileri yakalamak üzere üç tekrarlı katmanı içerir: Uzun Kısa Vadeli Bellek (LSTM), Kapılı Tekrarlı Birim (GRU) ve basit Tekrarlı Sinir Ağı (RNN). Önerilen ağlar, NWFP'den gerçek zamanlı, bir yıllık veri kullanılarak test edilmiştir. Sonuçlar, LSTM, GRU ve basit RNN katmanları temelli DRNN için gerçek ve tahmin edilen rüzgâr hızı arasında güçlü bir korelasyon olduğunu göstermiştir; ancak basit RNN, diğer iki tekrarlı katmandan biraz daha iyi performans sergilemiştir. Ayrıca, ağ hatalarının yıl boyunca dağılımı analiz edilmiştir. Gözlemlenen meteorolojik verilerin yıllar arasında farklı dağılımlardan olmasına rağmen, önerilen ağ, bu veriler küresel ısınma nedeniyle değişmiş olsa bile iyi genelleme yapmıştır.

References

  • 1. Ahmed, A., Khalid, M., 2019. A Review on the Selected Applications of Forecasting Models in Renewable Power Systems, Renewable and Sustainable Energy Reviews, 100, 9-21.
  • 2. Chen, Y., Dong, Z., Wang, Y., Su, J., Han, Z., Zhou, D., Zhang, K., Zhao, Y., Bao, Y., 2021. Short-term Wind Speed Predicting Framework Based on the Eemd-ga-lstm Method under Large-scale Wind History. Energy Conversion and Management, 227, 113559.
  • 3. Hayes, L., Stocks, M., Blakers, A., 2021. Accurate Longterm Power Generation Model for Offshore Wind Farms in Europe using Era5 Reanalysis. Energy, 229, 120603.
  • 4. Deng, X., Shao, H., Hu, C., Jiang, D., Jiang, Y., 2020. Wind Power Forecasting Methods Based on Deep Learning: A Survey. Computer Modeling in Engineering & Sciences, 122(1), 273-301.
  • 5. Mi, X., Liu, H., Li, Y., 2019. Wind Speed Prediction Model Using Singular Spectrum Analysis, Empirical Mode Decomposition and Convolutional Support Vector Machine. Energy Conversion and Management, 180, 196-205.
  • 6. Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., Yan, Z., 2009. A Review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13(4), 915-920.
  • 7. Azimi, R., Ghofrani, M., Ghayekhloo, M., 2016. A Hybrid Wind Power Forecasting Model Based on Data Mining and Wavelets Analysis. Energy Conversion and Management, 127, 208-225.
  • 8. Santhosh, M., Venkaiah, C., Vinod K.D.M., 2020. Current Advances and Approaches in Wind Speed and Wind Power Forecasting for Improved Renewable Energy Integration: A Review. Engineering Reports, 2(6), e12178.
  • 9. Lipu, M.S.H., Miah, M.S., Hannan, M.A., Hussain, A., Sarker, M.R., Ayob, A., Saad, M. H.M., Mahmud, M.S., 2021. Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects. IEEE Access, 9, 102460-102489.
  • 10. Puri V., Kumar, N., 2021. Wind Energy Forecasting Using Artificial Neural Network in Himalayan Region. Modeling Earth Systems and Environment, 1-10.
  • 11. Li, L.L., Chang, Y.B., Tseng, M.L., Liu J.Q., Lim, M.K., 2020. Wind Power Prediction Using a Novel Model on Wavelet Decomposition- Support Vector Machines-Improved Atomic Search Algorithm. Journal of Cleaner Production, 270, 121817.
  • 12. Sfetsos, A. 2000. A Comparison of Various Forecasting Techniques Applied to Mean Hourly Wind Speed Time Series. Renewable Energy, 21(1), 23-35.
  • 13. Lin, W., Wu, Z., Lin, L., Wen, A., Li, J., 2017. An Ensemble Random Forest Algorithm for Insurance Big Data Analysis. IEEE Access, 5, 16568–16575.
  • 14. Tian, Z., Li, S., Wang, Y., 2020. A Prediction Approach Using Ensemble Empirical Mode Decomposition-Permutation Entropy and Regularized Extreme Learning Machine for Short-term Wind Speed. Wind Energy, 23(2), 177-206.
  • 15. Huang, G.B., Zhu, Q.Y., Siew, C.K, 2006. Extreme Learning Machine: Theory and Applications. Neurocomputing, 70(1), 489-501.
  • 16. Afrasiabi, M., Mohammadi, M., Rastegar, M., Afrasiabi, S., 2021. Advanced Deep Learning Approach for Probabilistic Wind Speed Forecasting. IEEE Transactions on Industrial Informatics, 17(1), 720-727.
  • 17. Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., Zheng, M., 2019. Wind Power Short-term Prediction Based on lstm and Discrete Wavelet Transform. Applied Sciences, 9(6).
  • 18. Liu, H., Mi, X., Li, Y., 2018. Smart Multi-step Deep Learning Model for Wind Speed Forecasting Based on Variational Mode Decomposition, Singular Spectrum Analysis, lstm Network and Elm. Energy Conversion and Management, 59, 54-64.
  • 19. Ding, M., Zhou, H., Xie, H., Wu, M., Nakanishi, Y., Yokoyama, R., 2019. A Gated Recurrent Unit Neural Networks Based Wind Speed Error Correction Model for Short-term Wind Power Forecasting. Neurocomputing, 365, 54-61.
  • 20. Kisvari, A., Lin, Z., Liu, X., 2021. Wind Power Forecasting a Data-driven Method Along with Gated Recurrent Neural Network. Renewable Energy, 163, 1895-1909.
  • 21. Duan, J., Zuo, H., Bai, Y., Duan, J., Chang, M., Chen, B., 2021. Short-term Wind Speed Forecasting Using Recurrent Neural Networks with Error Correction. Energy, 217, 119397.
  • 22. Wang, L., Li, X., Bai, Y., 2018. Short-term Wind Speed Prediction Using an Extreme Learning Machine Model with Error Correction. Energy Conversion and Management, 162, 239-250.
  • 23. Memarzadeh, G., Keynia, F., 2020. A New Short-term Wind Speed Forecasting Method Based on Fine-tuned lstm Neural Network and Optimal Input Sets. Energy Conversion and Management, 213, 112824.
  • 24. Liu, H., Mi, X., Li, Y., Duan, Z., Xu, Y., 2019. Smart Wind Speed Deep Learning-based Multi-Step Forecasting Model Using Singular Spectrum Analysis, Convolutional Gated Recurrent Unit Network and Support Vector Regression. Renewable Energy, 43, 842-854.
  • 25. Yu, C., Li, Y., Zhang, M., 2017. An Improved Wavelet Transform Using Singular Spectrum Analysis for Wind Speed Forecasting Based on Elman Neural Network. Energy Conversion and Management, 148, 895-904.
  • 26. Yu, C., Li, Y., Bao, Y., Tang, H., Zhai, G., 2018. A Novel Framework for Wind Speed Prediction Based on Recurrent Neural Networks and Support Vector Machine. Energy Conversion and Management, 178, 137-145.
  • 27. Orr, R.J., Griffith, B.A., Rose, S., Hatch, D., Hawkins, J., Murray, P.J., 2011. Designing and Creating the North Wyke Farm Platform. Catchment Science.
  • 28. Hawkin, J., 2015. Design, Establishment and Development, http://resources.rothamsted.ac.uk /sites/default/files/groups/NorthWykeFarmPlatform/FPUG.Doc.001EstabDevelopver1.5.pdf, Access date: 11/02/2023.
  • 29. Boden, M., 2002. A Guide to Recurrent Neural Networks and Backpropagation. The Dallas Project.
  • 30. Elman, J.L., 1990. Finding Structure in Time. Cognitive Science, 14(2), 179-211.
  • 31. Goodfellow, I., Bengio, Y., Courville, 2016. Deep Learning. MIT Press.
  • 32. Hochreiter, S., Schmidhuber, J., 1997. Long Short-term Memory. Neural Computation, 9(8), 1735-1780.
  • 33. Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelling. arXiv Preprint arXiv:1412.3555.
  • 34. Kaplan, D., Glass, L., 1997. Understanding Nonlinear Dynamics. Springer Science & Business Media.
  • 35. Chollet, F., 2017. Deep Learning with Python. Manning.
  • 36. Werbos, P.J., 1990. Backpropagation Through Time: What it Does and How to do It. Proceedings of the IEEE, 78(10), 1550-1560.
There are 36 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other), Electrical Engineering (Other)
Journal Section Articles
Authors

Emre Özbilge 0000-0002-2295-752X

Yonal Kırsal 0000-0001-7031-1339

Publication Date July 11, 2024
Submission Date January 11, 2024
Acceptance Date June 27, 2024
Published in Issue Year 2024 Volume: 39 Issue: 2

Cite

APA Özbilge, E., & Kırsal, Y. (2024). Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 39(2), 287-300. https://doi.org/10.21605/cukurovaumfd.1513981