1
Department of Mathematics, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
(myvizhi.m@kpriet.ac.in)
2
Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt
(ahmed.abdelmon3m15@gmail.com)
Abstract :
Accurate forecasting is essential for the long-term success of adding wind energy to the national power system. In this study, we look at forecasting wind turbine using a LSTM deep learning model. To forecast potential outcomes for a time series, it is sufficient to initially obtain pertinent details from past data. While many methods struggle with understanding the long-term dependencies encoded in data sets, LSTM options, an instance of the strategy in deep learning, show potential for efficiently overcoming this challenge. An overview of LSTM's architecture and forward propagation method is provided initially. LSTM network is applied to the wind turbine prediction dataset. This dataset has 9 features and 6575 records. There are four performance matrices used to test the model. The four matrices are mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). MAPE obtained the least error.
Keywords :
Deep Learning; LSTM; Error; MSE; MAE; MAPE; RMSE; Wind Turbine.
References :
[1] P. J. Schubel and R. J. Crossley, “Wind turbine blade design,” Energies, vol. 5, no. 9, pp. 3425–3449, 2012.
[2] S. Mujeeb, T. A. Alghamdi, S. Ullah, A. Fatima, N. Javaid, and T. Saba, “Exploiting deep learning for wind power forecasting based on big data analytics,” Appl. Sci., vol. 9, no. 20, p. 4417, 2019.
[3] A. Clifton, L. Kilcher, J. K. Lundquist, and P. Fleming, “Using machine learning to predict wind turbine power output,” Environ. Res. Lett., vol. 8, no. 2, p. 24009, 2013.
[4] A. Khosravi, L. Machado, and R. O. Nunes, “Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil,” Appl. Energy, vol. 224, pp. 550–566, 2018.
[5] M. Optis and J. Perr-Sauer, “The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production,” Renew. Sustain. Energy Rev., vol. 112, pp. 27–41, 2019.
[6] Karam M. Sallam, Ali Wagdy Mohamed, Neutrosophic MCDM Methodology for Evaluation Onshore Wind for Electricity Generation and Sustainability Ecological, Neutrosophic Systems with Applications, vol.4, (2023): pp. 53–61
[7] X. Deng, H. Shao, C. Hu, D. Jiang, and Y. Jiang, “Wind power forecasting methods based on deep learning: A survey,” Comput. Model. Eng. Sci., vol. 122, no. 1, p. 273, 2020.
[8] A. Alkesaiberi, F. Harrou, and Y. Sun, “Efficient wind power prediction using machine learning methods: A comparative study,” Energies, vol. 15, no. 7, p. 2327, 2022.
[9] Z. Ti, X. W. Deng, and H. Yang, “Wake modeling of wind turbines using machine learning,” Appl. Energy, vol. 257, p. 114025, 2020.
[10] H. Wang, G. Li, G. Wang, J. Peng, H. Jiang, and Y. Liu, “Deep learning based ensemble approach for probabilistic wind power forecasting,” Appl. Energy, vol. 188, pp. 56–70, 2017.
[11] H. Demolli, A. S. Dokuz, A. Ecemis, and M. Gokcek, “Wind power forecasting based on daily wind speed data using machine learning algorithms,” Energy Convers. Manag., vol. 198, p. 111823, 2019.
[12] Z. Lin and X. Liu, “Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network,” Energy, vol. 201, p. 117693, 2020.
[13] A. Stetco et al., “Machine learning methods for wind turbine condition monitoring: A review,” Renew. energy, vol. 133, pp. 620–635, 2019.
[14] J. Heinermann and O. Kramer, “Machine learning ensembles for wind power prediction,” Renew. Energy, vol. 89, pp. 671–679, 2016.
[15] A. Taylor, S. Leblanc, and N. Japkowicz, “Anomaly detection in automobile control network data with long short-term memory networks,” in 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2016, pp. 130–139.
[16] P. Zhou et al., “Attention-based bidirectional long short-term memory networks for relation classification,” in Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), 2016, pp. 207–212.
[17] G. S. Alsoruji, A. M. Sadoun, M. Abd Elaziz, M. A. Al-Betar, A. W. Abdallah, and A. Fathy, “On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer,” J. Mater. Res. Technol., vol. 23, pp. 4075–4088, 2023.
[18] M. Alizamir et al., “Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform,” Eng. Appl. Artif. Intell., vol. 123, p. 106199, 2023.
[19] C. Hu, Q. Wu, H. Li, S. Jian, N. Li, and Z. Lou, “Deep learning with a long short-term memory networks approach for rainfall-runoff simulation,” Water, vol. 10, no. 11, p. 1543, 2018.
[20] Y. Xu, L. Mou, G. Li, Y. Chen, H. Peng, and Z. Jin, “Classifying relations via long short term memory networks along shortest dependency paths,” in Proceedings of the 2015 conference on empirical methods in natural language processing, 2015, pp. 1785–1794.
[21] S. Zhou et al., “Constructing a health indicator based on long short-term memory and using an extreme inflection point with a slope model to enhance monotonicity,” J. Brazilian Soc. Mech. Sci. Eng., vol. 45, no. 3, p. 175, 2023.
[22] K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” arXiv Prepr. arXiv1503.00075, 2015.
[23] Khder Alakkari,Mostafa Abotaleb,Amr Badr,Ammar Kadi,A. M. Ghazi Al khatib,Bayan Mohamad Alshaib,El-Sayed M. El-kenawy, Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model, International Journal of Advances in Applied Computational Intelligence, Vol. 1 , No. 2 , (2022) : 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201)
[24] Y. Yi et al., “Digital twin-long short-term memory (LSTM) neural network based real-time temperature prediction and degradation model analysis for lithium-ion battery,” J. Energy Storage, vol. 64, p. 107203, 2023.
| Style | # |
|---|---|
| MLA | Myvizhi M., Ahmed Abdel-Monem. "Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM)." International Journal of Advances in Applied Computational Intelligence, Vol. 3, No. 2, 2023 ,PP. 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |
| APA | Myvizhi M., Ahmed Abdel-Monem. (2023). Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM). Journal of International Journal of Advances in Applied Computational Intelligence, 3 ( 2 ), 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |
| Chicago | Myvizhi M., Ahmed Abdel-Monem. "Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM)." Journal of International Journal of Advances in Applied Computational Intelligence, 3 no. 2 (2023): 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |
| Harvard | Myvizhi M., Ahmed Abdel-Monem. (2023). Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM). Journal of International Journal of Advances in Applied Computational Intelligence, 3 ( 2 ), 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |
| Vancouver | Myvizhi M., Ahmed Abdel-Monem. Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM). Journal of International Journal of Advances in Applied Computational Intelligence, (2023); 3 ( 2 ): 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |
| IEEE | Myvizhi M., Ahmed Abdel-Monem, Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM), Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 3 , No. 2 , (2023) : 48-57 (Doi : https://doi.org/10.54216/IJAACI.030205) |