Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia

Yasmeen Mubarak Aldossary, Nabil Hewahi, Abdulla Alasaadi

Abstract


With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability, and its accuracy is crucial to effectively using wind resources. Therefore, this study proposes a novel WSF framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM) to achieve high forecasting accuracy for the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The hybrid decomposition method combines the Wavelet Packet Decomposition (WPD) and the Seasonal Adjustment Method (SAM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The BiLSTM is applied to forecast all the deseasonalized decomposed subseries. Five years of hourly wind speed observations acquired from a location in the Al-Jouf region were used to prove the effectiveness of the proposed model. The comparative experimental results, including 27 other models, demonstrated the proposed model's superiority in single and multiple WSF with an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987.

Keywords


wind speed forecasting; data decomposition; wavelet packet decomposition; seasonal adjustment method; bidirectional long short-term memory

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References


British Petroleum, Bp Statistical Review of World Energy, The Energy Institute, 2022.

A. Olabi and M. Abdelkareem, “Renewable energy and climate change,” Renewable and Sustainable Energy Reviews, vol. 158, p. 112111, April 2022, doi: 10.1016/J.RSER.2022.112111.

K. Almutairi, G. Thoma, and A. Durand-Morat, “Ex-Ante Analysis of Economic, Social and Environmental Impacts of Large-Scale Renewable and Nuclear Energy Targets for Global Electricity Generation by 2030,” Sustainability, vol. 10, no. 8, August 2018, doi: 10.3390/su10082884.

A. Amran, M. Amran, R. Alyousef, and H. Alabduljabbar, “Renewable and sustainable energy production in Saudi Arabia according to Saudi Vision 2030; Current status and future prospects,” Journal of Cleaner Production, vol. 247, p. 119602, February 2020, doi: 10.1016/J.JCLEPRO.2019.119602.

K. Almutairi and R. Alahmadi, “The Feasibility of the First Utility-Scale Wind Farm in Saudi Arabia (The 400 MW Dumat Al-Jandal Project),” Journal of Clean Energy Technologies, vol. 10, no. 1, pp. 1–10, March 2022, doi: 10.18178/JOCET.2022.10.1.531.

R. Shumway and D. Stoffer, Time Series Analysis and Its Applications. in Springer Texts in Statistics. Cham: Springer International Publishing, 2017, doi: 10.1007/978-3-319-52452-8.

G. Nason and B. Silverman, “The Stationary Wavelet Transform and some Statistical Applications,” in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, Eds. New York, NY: Springer New York, 1995, pp. 281–299, doi: 10.1007/978-1-4612-2544-7_17.

Y. Chen, S. Yu, S. Islam, C. Lim, and S. Muyeen, “Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions,” Energy Reports, vol. 8, pp. 8805–8820, 2022, doi: 10.1016/J.EGYR.2022.07.005.

B. Brown, R. Katz, and A. Murphy, “Time Series Models to Simulate and Forecast Wind Speed and Wind Power,” Journal of Climate and Applied Meteorology, vol. 23, no. 8, pp. 1184–1195, 1984, doi: 10.1175/1520-0450(1984)023<1184:TSMTSA>2.0.CO;2.

Z. Wu, G. Luo, Z. Yang, Y. Guo, K. Li, and Y. Xue, “A comprehensive review on deep learning approaches in wind forecasting applications,” CAAI Transactions on Intelligence Technology, vol. 7, no. 2, pp. 129–143, 2022, doi: https://doi.org/10.1049/cit2.12076.

M. Silva, N. Freitas, M. dos S Silva, and M. Sakamoto, “Wind Speed Forecasting: A Review,” Int. Journal of Engineering Research and Application, vol. 8, no. 1, pp. 4–09, 2018, doi: 10.9790/9622-0801010409.

M. Yesilbudak, S. Sagiroglu, and I. Colak, “A new approach to very short term wind speed prediction using K-nearest neighbor classification,” Energy Conversion and Management, vol. 69, pp. 77–86, 2013. doi:10.1016/j.enconman.2013.01.033

M. Yesilbudak, S. Sagiroglu, and I. Colak, “A wind speed forecasting approach based on 2-dimensional input space,” in 2012 International Conference on Renewable Energy Research and Applications (ICRERA), 2012, pp. 1–5.

H. Jiang, J. Wang, S. Qin, and Q. Zhou, “Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China,” Renew Energy, vol. 76, pp. 91–101, 2015, doi: 10.1016/J.RENENE.2014.11.011.

G. Li and J. Shi, “Application of Bayesian model averaging in modeling long-term wind speed distributions,” Renew Energy, vol. 35, no. 6, pp. 1192–1202, 2010, doi: 10.1016/J.RENENE.2009.09.003.

M. Yu, “Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm,” International Journal of Green Energy, vol. 17, no. 5, pp. 332–348, 2020, doi: 10.1080/15435075.2020.1731816.

N. Pearre and L. Swan, “Statistical approach for improved wind speed forecasting for wind power production,” Sustainable Energy Technologies and Assessments, vol. 27, pp. 180–191, 2018, doi: 10.1016/J.SETA.2018.04.010.

F. Cassola and M. Burlando, “Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output,” Applied energy, vol. 99, pp. 154–166, 2012.

A. Altan, S. Karasu, and E. Zio, “A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer,” Appl Soft Comput, vol. 100, p. 106996, 2021, doi: 10.1016/J.ASOC.2020.106996.

X. Mi, H. Liu, and Y. Li, “Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine,” Energy Convers Manag, vol. 180, pp. 196–205, Jan. 2019, doi: 10.1016/J.ENCONMAN.2018.11.006.

H. Li, J. Wang, H. Lu, and Z. Guo, “Research and application of a combined model based on variable weight for short term wind speed forecasting,” Renew Energy, vol. 116, pp. 669–684, Feb. 2018, doi: 10.1016/J.RENENE.2017.09.089.

T. Qian, D. Li, F. Jiang, and M. Chen, “Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks,” Energy, vol. 238, p. 121981, 2022, doi: 10.1016/J.ENERGY.2021.121981.

Y. Ren and P. Suganthan, “Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting,” Journal of Power and Energy Engineering, vol. 2, pp. 176–185, 2014, doi: 10.4236/jpee.2014.24025.

F. Berrezzek, K. Khelil, and T. Bouadjila, “Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks,” Revue d’Intelligence Artificielle, vol. 33, no. 6, pp. 447–452, 2019, doi: 10.18280/RIA.330607.

L. Saoud, H. Al-Marzouqi, and M. Deriche, “Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods,” IEEE Access, vol. 9, pp. 127356–127367, 2021, doi: 10.1109/ACCESS.2021.3111667.

K. Jaseena and B. Kovoor, “Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks,” Energy Convers Manag, vol. 234, p. 113944, Apr. 2021, doi: 10.1016/J.ENCONMAN.2021.113944.

Z. Duan, H. Liu, H. Wu, Y. Li, and S. Dong, “Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network,” Measurement, vol. 148, p. 106971, 2019, doi: 10.1016/J.MEASUREMENT.2019.106971.

X. Mi, H. Liu, and Y. Li, “Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks,” Energy Convers Manag, vol. 155, pp. 188–200, Jan. 2018, doi: 10.1016/J.ENCONMAN.2017.10.085.

H. Jiang, J. Wang, S. Qin, and Q. Zhou, “Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China,” Renew Energy, vol. 76, pp. 91–101, Apr. 2015, doi: 10.1016/J.RENENE.2014.11.011.

L. Kong, J. Wang, W. Zhang, J. Wang, and T. Han, “A novel hybrid approach for wind speed prediction,” Information Sciences, vol. 273, pp. 304–318, 2014, doi: https://doi.org/10.1016/j.ins.2014.02.159.

K. Barashid, A. Munshi, and A. Alhindi, “Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia,” Energies, vol. 16, no. 2, p. 938, 2023, doi: 10.3390/EN16020938.

Y. Parvej and S. Shaahid, “Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 10, no. 1, pp. 257–264, 2021, doi: 10.30534/IJATCSE/2021/361012021.

M. Mohandes, T. Halawani, S. Rehman, and A. Hussain, “Support vector machines for wind speed prediction,” Renew Energy, vol. 29, no. 6, pp. 939–947, 2004, doi: 10.1016/J.RENENE.2003.11.009.

Y. Faniband and S. Shaahid, “Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia,” E3S Web of Conferences, vol. 173, p. 01004, 2020, doi: 10.1051/E3SCONF/202017301004.

T. Brahimi, F. Alhebshi, H. Alnabilsi, A. Bensenouci, and M. Rahman, “Prediction of Wind Speed Distribution Using Artificial Neural Network: The Case of Saudi Arabia,” Procedia Computer Science, vol. 163, pp. 41–48, 2019, doi: 10.1016/j.procs.2019.12.084.

T. Halawani, M. Mohandes, and S. Rehman, “A neural networks approach for wind speed prediction,” Renew Energy, vol. 13, no. 3, pp. 345–354, 1998, doi: 10.1016/S0960-1481(98)00001-9.

U. Salman, S. Rehman, B. Alawode, and L. Alhems, “Short term prediction of wind speed based on long-short term memory networks,” FME Transactions, vol. 49, no. 3, pp. 643–652, 2021, doi: 10.5937/FME2103643S.

M. Lipu, Md. Miah, M. Hannan, A. Hussain, M. Sarker, A. Ayob, M. Saad, and Md. Mahmud, “Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects,” IEEE Access, vol. 9, pp. 102460–102489, 2021, doi: 10.1109/ACCESS.2021.3097102.

J. Quilty and J. Adamowski, “Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework,” Journal of Hydrology, vol. 563, pp. 336–353, 2018, doi: 10.1016/J.JHYDROL.2018.05.003.

J. Hu and J. Wang, “Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression,” Energy, vol. 93, pp. 1456–1466, 2015, doi: 10.1016/J.ENERGY.2015.10.041.

C. Burrus, R. Gopinath, and H. Guo, Wavelets and Wavelet Transforms, OpenStax-CNX, 2015.

J. Xue, H. Zhang, C. Zheng, and X. Yan, “Wavelet packet transform for feature extraction of EEG during mental tasks,” International Conference on Machine Learning and Cybernetics, vol. 1, pp. 360–363, 2003, doi: 10.1109/ICMLC.2003.1264502.

B. Ye and X. Zhao, “Convolution wavelet packet transform and its applications to signal processing,” Digit Signal Process, vol. 20, no. 5, pp. 1352–1364, 2010, doi: 10.1016/J.DSP.2010.01.007.

M. El-Hendawi and Z. Wang, “An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting,” Electric Power Systems Research, vol. 182, p. 106265, 2020, doi: 10.1016/J.EPSR.2020.106265.

Z. Guo, J. Wu, H. Lu, and J. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011, doi: 10.1016/J.KNOSYS.2011.04.019.

M. Qi and P. Zhang, “Neural network forecasting for seasonal and trend time series,” European Journal of Operational Research, vol. 160, no. 2, pp. 501–514, 2005, doi: 10.1016/J.EJOR.2003.08.037.

Y. Dong, J. Wu, J. Wang, H. Lu, and X. Lu, “Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model,” Energy Convers Manag, vol. 70, pp. 1–9, Jun. 2013, doi: 10.1016/J.ENCONMAN.2013.02.010.

D. Sun, J. Wang, W. Zhu, and W. Zhang, “A trend fixed on firstly and seasonal adjustment model combined with the ?-SVR for short-term forecasting of electricity demand,” Energy Policy, vol. 37, no. 11, pp. 4901–4909, 2009, doi: 10.1016/J.ENPOL.2009.06.046.

Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” 2018, doi: 10.48550/arxiv.1801.02143.

M. Khan, H. Wang, A. Riaz, A. Elfatyany, and S. Karim, “Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification,” Journal of Supercomputing, vol. 77, no. 7, pp. 7021–7045, July 2021, doi: 10.1007/S11227-020-03560-Z/FIGURES/6.

Q. Zhu, J. Chen, D. Shi, L. Zhu, X. Bai, X. Duan, and Y. Liu, “Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction,” IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 509–523, January 2020, doi: 10.1109/TSTE.2019.2897136.

N. Masseran, A. Razali, K. Ibrahim, and W. Zin, “Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia,” Energy, vol. 37, no. 1, pp. 649–656, January 2012, doi: 10.1016/J.ENERGY.2011.10.035.

KAPSARC, “KAPSARC Data Portal,” 2023. https://datasource.kapsarc.org/pages/home/ (accessed Mar. 04, 2023).

S. Barhmi and O. Fatni, “Hourly wind speed forecasting based on support vector machine and artificial neural networks,” IAES International Journal of Artificial Intelligence, vol. 8, no. 3, p. 286, 2019.

Y. Hong and C. Rioflorido, “A hybrid deep learning-based neural network for 24-h ahead wind power forecasting,” Applied Energy, vol. 250, pp. 530–539, September 2019, doi: 10.1016/J.APENERGY.2019.05.044.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i3.14088.g8800

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