Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu

Abstract

Wind energy stands out as a prominent renewable energy source, characterized by its high efficiency, feasibility, and wide applicability. Nonetheless, the integration of wind energy into the electrical system encounters significant obstacles due to the unpredictability and variability of wind speed. Accurate wind speed prediction is essential for estimating the short-, medium-, and long-term power output of wind turbines. Various methodologies and models exist for wind speed time series prediction. This research paper proposes a combination of two approaches to enhance forecasting accuracy: deep learning, particularly Long Short-Term Memory (LSTM), and the Autoregressive Integrated Moving Average (ARIMA) model. LSTM, by retaining patterns over longer periods, improves prediction rates. Meanwhile, the ARIMA model enhances the likelihood of staying within predefined boundaries. The study utilizes daily average wind speed data from the Gelibolu district of Çanakkale province spanning 2014 to 2021. Evaluation using the root mean square error (RMSE) shows the superior forecast accuracy of the LSTM model compared to ARIMA. The LSTM model achieved an RMSE of 6.3% and a mean absolute error of 16.67%. These results indicate the potential utility of the proposed approach in wind speed forecasting, offering performance comparable to or exceeding other studies in the literature.

Keywords

• Wind speed prediction
• Wind energy
• LSTM
• ARIMA
• Deep learning

References

1. Torunoğlu Gedik, Ö. (2015). Türkiye'de yenilenebilir enerji kaynakları ve çevresel etkileri. [Doctoral dissertation, Istanbul Technical University].
2. Makarieva, A. M., Gorshkov, V. G., & Li, B. L. (2008). Energy budget of the biosphere and civilization: Rethinking environmental security of global renewable and non-renewable resources. Ecological Complexity, 5(4), 281-288. https://doi.org/10.1016/j.ecocom.2008.05.005
3. Ssekulima, E. B., Anwar, M. B., Al Hinai, A., & El Moursi, M. S. (2016). Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review. IET Renewable Power Generation, 10(7), 885-989. https://doi.org/10.1049/iet-rpg.2015.0477
4. Bokde, N., Feijóo, A., Villanueva, D., & Kulat, K. (2019). A review on hybrid empirical mode decomposition models for wind speed and wind power prediction. Energies, 12(2), 254. https://doi.org/10.3390/en12020254
5. Wohland, J., Omrani, N. E., Keenlyside, N., & Witthaut, D. (2019). Significant multidecadal variability in German wind energy generation. Wind Energy Science, 4(3), 515-526. https://doi.org/10.5194/wes-4-515-2019
6. Sinap, V. (2023). Makine öğrenmesi teknikleri ile counter-strike: Global offensive raunt sonuçlarının tahminlenmesi. Journal of Intelligent Systems: Theory and Applications, 6(2), 119-129. https://doi.org/10.38016/jista.1235031
7. Çakır, F. (2020). Demiryolu yolcu taşıma talebinin yapay sinir ağları ile tahmini. [Master's thesis, Aksaray University].
8. Akbulut, S., & Adem, K. (2023). Derin öğrenme ve makine öğrenmesi yöntemleri kullanılarak gelişmekte olan ülkelerin finansal enstrümanlarının etkileşimi ile Bist 100 tahmini. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(1), 52-63. https://doi.org/10.28948/ngmuh.1131191

9. Eşsiz, E. S. (2022). Short-term wind power prediction with harmony search algorithm: Belen region. Turkish Journal of Engineering, 6(3), 251-255. https://doi.org/10.31127/tuje.970959
10. Balcı, M., Yüzgeç, U., & Dokur, E. (2022, May). Rüzgâr hızı tahmini için ayrıştırmaya dayalı hibrit yöntemlerin karşılaştırmalı bir çalışması. In International Conference on Emerging Sources in Science, 118-135.
11. Balti, H., Abbes, A. B., Mellouli, N., Sang, Y., Farah, I. R., Lamolle, M., & Zhu, Y. (2021). Big data-based architecture for drought forecasting using LSTM, ARIMA, and Prophet: Case study of the Jiangsu Province, China. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1-8. https://doi.org/10.1109/ICOTEN52080.2021.9493513
12. Baykal, T., Taylan, D., & Terzi, Ö. (2023). Isparta İli için gelecekteki olası meteorolojik kuraklık değerlendirmesi. Doğal Afetler ve Çevre Dergisi, 9(1), 90-100. https://doi.org/10.21324/dacd.1165500
13. Canıtez, M. A., & Savaş, S. (2022). Kripto para piyasa değeri tahmini için özellik tabanlı LSTM ve ARIMA karşılaştırması. In 3rd International Conference on Applied Engineering and Natural Sciences, 1311-1317.
14. Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia exports using a hybrid model ARIMA-LSTM. Procedia Computer Science, 179, 480-487. https://doi.org/10.1016/j.procs.2021.01.031
15. Demirtop, A., & Işık, A. H. (2023). Yapay sinir ağları ile rüzgâr enerji verimliliğine yönelik yeni bir tahmin yaklaşımı: Çanakkale İli Bozcaada Örneği. Uluslararası Mühendislik Tasarım ve Teknoloji Dergisi, 5(1-2), 25-32.
16. Devi, A. S., Maragatham, G., Boopathi, K., & Rangaraj, A. G. (2020). Hourly day-ahead wind power forecasting with the EEMD-CSO-LSTM-EFG deep learning technique. Soft Computing, 24(16), 12391-12411. https://doi.org/10.1007/s00500-020-04680-7
17. Elsaraiti, M., & Merabet, A. (2021). A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed. Energies, 14(20), 6782. https://doi.org/10.3390/en14206782
18. Erden, C. (2023). Derin Öğrenme ve ARIMA Yöntemlerinin Tahmin Performanslarının Kıyaslanması: Bir Borsa İstanbul Hissesi Örneği. Yönetim ve Ekonomi Dergisi, 30(3), 419-438. https://doi.org/10.18657/yonveek.1208807
19. Ji, L., Zou, Y., He, K., & Zhu, B. (2019). Carbon futures price forecasting based with ARIMA-CNN-LSTM model. Procedia Computer Science, 162, 33-38. https://doi.org/10.1016/j.procs.2019.11.254
20. Kamber, E., Körpüz, S., Can, M., Aydoğmuş, H. Y., & Gümüş, M. (2021). Yapay Sinir Ağlarina Dayali Kisa Dönemli Elektrik Yükü Tahmini. Endüstri Mühendisliği, 32(2), 364-379. https://doi.org/10.46465/endustrimuhendisligi.820509
21. Liu, X., Lin, Z., & Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM. Energy, 227, 120492. https://doi.org/10.1016/j.energy.2021.120492
22. Othman, M. M. (2023). Modeling of daily groundwater level using deep learning neural networks. Turkish Journal of Engineering, 7(4), 331-337. https://doi.org/10.31127/tuje.1169908
23. Sevinç, A., & Kaya, B. (2021). Derin öğrenme ve istatistiksel modelleme yöntemiyle sıcaklık tahmini ve karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (28), 1222-1228. https://doi.org/10.31590/ejosat.1014106
24. Shao, B., Song, D., Bian, G., & Zhao, Y. (2021). Wind speed forecast based on the LSTM neural network optimized by the firework algorithm. Advances in Materials Science and Engineering, 2021(1), 4874757. https://doi.org/10.1155/2021/4874757
25. Wang, J., & Wang, J. (2023, March). Short-term Wind Speed Forecast Using ARIMA Based on EEMD Decomposition. In Journal of Physics: Conference Series, 2450(1), 012020. https://doi.org/10.1088/1742-6596/2450/1/012020
26. Zhang, R., Guo, Z., Meng, Y., Wang, S., Li, S., Niu, R., ... & Li, Y. (2021). Comparison of ARIMA and LSTM in forecasting the incidence of HFMD combined and uncombined with exogenous meteorological variables in Ningbo, China. International Journal of Environmental Research and Public Health, 18(11), 6174. https://doi.org/10.3390/ijerph18116174
27. Zhang, M., Wang, Y., Zhang, H., Peng, Z., & Tang, J. (2023). A novel and robust wind speed prediction method based on spatial features of wind farm cluster. Mathematics, 11(3), 499. https://doi.org/10.3390/math11030499
28. Zhao, J., Nie, G., & Wen, Y. (2023). Monthly precipitation prediction in Luoyang city based on EEMD-LSTM-ARIMA model. Water Science & Technology, 87(1), 318-335. https://doi.org/10.2166/wst.2022.425
29. Bektaş, B. N. (2019). Understanding the conservation process of gallipoli historical site. [Master's thesis, Middle East Technical University].
30. https://www.mgm.gov.tr/
31. Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019). Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica, 67(5), 1471-1481. https://doi.org/10.1007/s11600-019-00330-1
32. Sundermeyer, M., Ney, H., & Schlüter, R. (2015). From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 517-529. https://doi.org/10.1109/TASLP.2015.2400218
33. Kamath, U., Liu, J., & Whitaker, J. (2019). Deep learning for NLP and speech recognition, 84. Cham, Switzerland: Springer.
34. Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, 115-143.
35. Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. Neural and Evolutionary Computing. https://doi.org/10.48550/arXiv.1909.09586
36. Yang, H., Luo, L., Chueng, L. P., Ling, D., & Chin, F. (2019). Deep learning and its applications to natural language processing. Deep learning: Fundamentals, theory and applications, 89-109. https://doi.org/10.1007/978-3-030-06073-2_4
37. Wang, Y., Jiang, L., Yang, M. H., Li, L. J., Long, M., & Fei-Fei, L. (2018). Eidetic 3D LSTM: A model for video prediction and beyond. In International Conference on Learning Representations.
38. Wang, X., Wang, Y., Yuan, P., Wang, L., & Cheng, D. (2021). An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach. Hydrological Sciences Journal, 66(9), 1488-1502. https://doi.org/10.1080/02626667.2021.1937631
39. Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21, 1-12. https://doi.org/10.1186/s12874-021-01235-8
40. Loganathan, N., & Ibrahim, Y. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins Sarima application. South Asian Journal of Tourism and Heritage, 3(2), 50-60.
41. Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2013). Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, 476, 433-441. https://doi.org/10.1016/j.jhydrol.2012.11.017
42. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623
43. Shcherbakov, M. V., Brebels, A., Shcherbakova, N. L., Tyukov, A. P., Janovsky, T. A., & Kamaev, V. A. E. (2013). A survey of forecast error measures. World Applied Sciences Journal, 24(24), 171-176. https://doi.org/10.5829/idosi.wasj.2013.24.itmies.80032
44. Barbounis, T. G., Theocharis, J. B., Alexiadis, M. C., & Dokopoulos, P. S. (2006). Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion, 21(1), 273-284. https://doi.org/10.1109/TEC.2005.847954
45. Lo, A. W., Siah, K. W., & Wong, C. H. (2019). Machine learning with statistical imputation for predicting drug approvals, 60, 10.1162.
46. Arya, F. K., & Zhang, L. (2015). Time series analysis of water quality parameters at Stillaguamish River using order series method. Stochastic Environmental Research and Risk Assessment, 29, 227-239. https://doi.org/10.1007/s00477-014-0907-2
47. Beard, E., Marsden, J., Brown, J., Tombor, I., Stapleton, J., Michie, S., & West, R. (2019). Understanding and using time series analyses in addiction research. Addiction, 114(10), 1866-1884. https://doi.org/10.1111/add.14643
48. Franses, P. H., & Paap, R. (2004). Periodic time series models. OUP Oxford.
49. Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, 10(07), 3897-3904.