Main Article Content
Accurate and reliable wind power forecasting plays a vital role in the operation and management of power systems. Hence, it has become necessary to research and develop a high-accuracy wind power forecasting model. However, owing to highly nonlinear and non-stationary patterns of wind power time-series, creating a wind forecasting model capable of predicting such series accurately is both complicated and challenging. Aiming at this challenge, this paper introduces a new decomposition-based hybrid model based on multiple decomposition techniques, neural network with random weights (NNRW), and linear combiner. In our approach, the original time-series is decomposed into a collection of sub-series by different decomposition techniques. Each sub-series is modeled and predicted separately using NNRW. The predicted signals of each decomposition model are then reconstructed independently. Finally, all of the reconstructed results are integrated by the combiner using a linear combination method. The predictive performance of the proposed method was compared with other state-of-the-art techniques in over 12 wind power time-series. The experimental results show that the predictive performance of the proposed method remarkably outperforms the other competitors, proving the developed model to be effective, efficient, and practicable.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
2. B. Bhandari, K.-T. Lee, G.-Y. Lee, Y.-M. Cho, and S.-H. Ahn, “Optimization of hybrid renewable energy power systems: A review,” International journal of precision engineering and manufacturing-green technology, vol. 2, no. 1, pp. 99–112, 2015.
3. E. B. Ssekulima, M. B. Anwar, A. Al Hinai, and M. S. El Moursi, “Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review,” IET Renewable Power Generation, vol. 10, no. 7, pp. 885–989, 2016.
4. H. Liu, X. Mi, 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 Conversion and Management, vol. 155, pp. 188–200, 2018.
5. X. An, D. Jiang, M. Zhao, and C. Liu, “Shortterm prediction of wind power using emd and chaotic theory,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 2, pp. 1036–1042, 2012.
6. L. Han, R. Zhang, X. Wang, A. Bao, and H. Jing, “Multi-step wind power forecast based on vmd-lstm,” IET Renewable Power Generation, vol. 13, no. 10, pp. 1690–1700, 2019.
7. J. P. d. S. Catal˜ao, H. M. I. Pousinho, and V. M. F. Mendes, “Short-term wind power forecasting in portugal by neural networks and wavelet transform,” Renewable energy, vol. 36, no. 4, pp. 1245–1251, 2011.
8. A. Laouafi, M. Mordjaoui, A. Medoued, T. E. Boukelia, and A. Ganouche, “Wind power forecasting approach using neuro-fuzzy system combined with wavelet packet decomposition, data preprocessing, and forecast combination framework,” Wind Engineering, vol. 41, no. 4, pp. 235– 244, 2017.
9. C. Wang, H. Zhang, and P. Ma, “Wind power forecasting based on singular spectrum analysis and a new hybrid laguerre neural network,” Applied Energy, vol. 259, p. 114139, 2020.
10. Z. Qian, Y. Pei, H. Zareipour, and N. Chen, “A review and discussion of decomposition-based hybrid models for wind energy forecasting applications,” Applied energy, vol. 235, pp. 939–953, 2019.
11. W. Cao, X. Wang, Z. Ming, and J. Gao, “A review on neural networks with random weights,” Neurocomputing, vol. 275, pp. 278–287, 2018.
12. W. F. Schmidt, M. A. Kraaijveld, and R. P. Duin, “Feedforward neural networks with random weights,” in Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, pp. 1–4, IEEE, 1992.
13. Y.-H. Pao, G.-H. Park, and D. J. Sobajic, “Learning and generalization characteristics of the random vector functional-link net,” Neurocomputing, vol. 6, no. 2, pp. 163–180, 1994.
14. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489– 501, 2006.
15. L. Tang, Y. Wu, and L. Yu, “A non-iterative decomposition-ensemble learning paradigm using rvfl network for crude oil price forecasting,” Applied Soft Computing, vol. 70, pp. 1097–1108, 2018.
16. E. Fijani, R. Barzegar, R. Deo, E. Tziritis, and S. Konstantinos, “Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters,” Science of the total environment, vol. 648, pp. 839–853, 2019.
17. R. Prasad, R. C. Deo, Y. Li, and T. Maraseni, “Soil moisture forecasting by a hybrid machine learning technique: Elm integrated with ensemble empirical mode decomposition,” Geoderma, vol. 330, pp. 136–161, 2018.
18. N. Huang, C. Yuan, G. Cai, and E. Xing, “Hybrid short term wind speed forecasting using variational mode decomposition and a weighted regularized extreme learning machine,” Energies, vol. 9, no. 12, pp. 1–19, 2016.
19. T. Peng, J. Zhou, C. Zhang, and Y. Zheng, “Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and adaboost-extreme learning machine,” Energy Conversion and Management, vol. 153, pp. 589–602, 2017.
20. H. Liu, H.-Q. Tian, and Y.-F. Li, “Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms,” Energy conversion and management, vol. 100, pp. 16–22, 2015.
21. L. Tang, Y. Wu, and L. Yu, “A randomizedalgorithm-based decomposition-ensemble learning methodology for energy price forecasting,” Energy, vol. 157, pp. 526–538, 2018.
22. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998.
23. K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE transactions on signal processing, vol. 62, no. 3, pp. 531–544, 2014.
24. D. S. Broomhead and G. P. King, “Extracting qualitative dynamics from experimental data,” Physica D: Nonlinear Phenomena, vol. 20, no. 2- 3, pp. 217–236, 1986.
25. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 7, pp. 674–693, 1989.
26. R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on information theory, vol. 38, no. 2, pp. 713–718, 1992.
27. P. Du, J. Wang, W. Yang, and T. Niu, “A novel hybrid model for short-term wind power forecasting,” Applied Soft Computing, vol. 80, pp. 93–106, 2019.
28. Y. Li, H. Shi, F. Han, Z. Duan, and H. Liu, “Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy,” Renewable Energy, vol. 135, pp. 540–553, 2019.
29. G. E. P. Box and G. M. Jenkins, Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden-Day, 1970.
30. L.-L. Li, X. Zhao, M.-L. Tseng, and R. R. Tan, “Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm,” Journal of Cleaner Production, vol. 242, p. 118447, 2020.
31. A. P. Marug´an, F. P. G. M´arquez, J. M. P. Perez, and D. Ruiz-Hern´andez, “A survey of artificial neural network in wind energy systems,” Applied energy, vol. 228, pp. 1822–1836, 2018.
32. Y. Ren, P. Suganthan, and N. Srikanth, “Ensemble methods for wind and solar power forecasting—a state-of-the-art review,” Renewable and Sustainable Energy Reviews, vol. 50, pp. 82–91, 2015.
33. A. Tascikaraoglu and M. Uzunoglu, “A review of combined approaches for prediction of shortterm wind speed and power,” Renewable and Sustainable Energy Reviews, vol. 34, pp. 243– 254, 2014.
34. Y.-H. Pao and Y. Takefuji, “Functional-link net computing: Theory, system architecture, and functionalities,” Computer, vol. 25, no. 5, pp. 76–79, 1992.
35. G. Li, P. Niu, X. Duan, and X. Zhang, “Fast learning network: A novel artificial neural network with a fast learning speed,” Neural Computing and Applications, vol. 24, no. 7-8, pp. 1683–1695, 2014.
36. A. A. Abdoos, “A new intelligent method based on combination of vmd and elm for short term wind power forecasting,” Neurocomputing, vol. 203, pp. 111–120, 2016.
37. J. Naik, P. Satapathy, and P. Dash, “Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression,” Applied Soft Computing, vol. 70, pp. 1167–1188, 2018.
38. C. R. Rao, S. K. Mitra, et al., Generalized inverse of a matrix and its applications. New York: Springer, 1972.
39. D. Serre, Matrices: Theory and Applications. New York: Springer, 2002.
40. H. Liu, X. Mi, and Y. Li, “An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and elm algorithm,” Renewable energy, vol. 123, pp. 694–705, 2018.
41. Y. Ren, P. Suganthan, and N. Srikanth, “A comparative study of empirical mode decompositionbased short-term wind speed forecasting methods,” IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 236–244, 2014.
42. H. Yin, Z. Dong, Y. Chen, J. Ge, L. L. Lai, A. Vaccaro, and A. Meng, “An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization,” Energy conversion and management, vol. 150, pp. 108– 121, 2017.
43. Y. Zhang, J. Le, X. Liao, F. Zheng, and Y. Li, “A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing,” Energy, vol. 168, pp. 558–572, 2019.
44. J. Demˇsar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine learning research, vol. 7, pp. 1–30, 2006.
45. R. L. Iman and J. M. Davenport, “Approximations of the critical region of the fbietkan statistic,” Communications in Statistics-Theory and Methods, vol. 9, no. 6, pp. 571–595, 1980.
46. M. Lindauer, J. N. van Rijn, and L. Kotthoff, “The algorithm selection competitions 2015 and 2017,” Artificial Intelligence, vol. 272, pp. 86– 100, 2019.
47. I. A. Carvalho, “On the statistical evaluation of algorithmic’s computational experimentation with infeasible solutions,” Information Processing Letters, vol. 143, pp. 24–27, 2019.
48. Y. Rizk and M. Awad, “On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks,” Neurocomputing, vol. 325, pp. 1–19, 2019.