The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

Abstract

This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

Keywords

• Wind speed estimation
• artificial neural networks
• data decomposition

References

1. [1] G. Lee., Y. Ding., M. Genton. and L. Xie. "Power curve estimation with multivariate environmental factors for inland and offshore wind farms". Journal of the American Statistical Association. vol. 110. no. 509. p. 56-67. 2015.
2. [2] Y. Hadri., V. Khokhlov. and M. Slizhe. "Regional climate models projections of wind speed in Morocco for period 2020-2050". Asian Journal of Environment & Ecology. vol. 6. no. 3. p. 1-7. 2018.
3. [3] E. Chiodo., M. Fantauzzi. and G. Mazzanti. "The compound inverse Rayleigh as an extreme wind speed distribution and its bayes estimation". Energies. vol. 15. no. 3. p. 861. 2022.
4. [4] L. Herbst and J. Lalk. "A case study of climate variability effects on wind resources in South Africa". Journal of Energy in Southern Africa. vol. 25. no. 3. p. 2-10. 2014.
5. [5] Z. Rajab., Y. Sassi., A. Taher., A. Khalil. and F. Mohamed. "A practical seasonal performance evaluation of small wind turbine in urban environment". Wind Engineering. vol. 43. no. 4. p. 344-358. 2019.
6. [6] L. Rapella., D. Faranda. and M. Gaetani. "Climate change on extreme winds already affects wind energy availability in Europe". EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9634. 2022.
7. [7] G. Fajardo-Pulido and G. Fuster-Lopez. " Preliminary study of wind speed characterization to install a 400 W wind turbine ". Ecorfan Journal Republic of Paraguay. p. 23-30. 2019.
8. [8] L. Rapella., D. Faranda., M. Gaetani., D. Philippe. and M. Ginesta. "Climate change on extreme winds already affects off-shore wind power availability in Europe". Environmental Research Letters. vol. 18. no. 3. p. 034040. 2023.

9. [9] N. Saeid and M. Seyed. "Choose suitable wind turbines for Manjil wind power plant using Monte Carlo simulation". International Journal of Computer Applications. vol. 15. no. 1. p. 26-34. 2011.
10. [10] J. Salmon and P. Taylor. "Errors and uncertainties associated with missing wind data and short records". Wind Energy. vol. 17. no. 7. p. 1111-1118. 2013.
11. [11] K. Chatfield., K. Simonyan., A. Vedaldi. and A. Zisserman. "Return of the devil in the details: delving deep into convolutional nets". The British Machine Vision Association. 2014.
12. [12] P. Gouverneur., F. Li., W. Adamczyk., T. Szikszay., K. Luedtke. and M. Grzegorzek. "Comparison of feature extraction methods for physiological signals for heat-based pain recognition". Sensors. vol. 21. no. 14. p. 4838. 2021.
13. [13] A. Pamuncak., M. Salami., A. Adha., B. Budiono. and I. Laory. "Estimation of structural response using convolutional neural network: application to the Suramadu Bridge". Engineering Computations. vol. 38. no. 10. p. 4047-4065. 2021.
14. [14] D. Cusumano., G. Meijer., J. Lenkowicz., G. Chiloiro., L. Boldrini., C. Masciocchi., N. Dinapoli., R. Gatta,, C. Casà., A. Damiani., B. Barbaro., M. Gambacorta., L. Azario., M. De Spirito., M. Intven. and V. Valentini. "A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer". La Radiologia Medica. vol. 126. no. 3. p. 421-429. 2020.
15. [15] W. Park and J. Park. "History and application of artificial neural networks in dentistry". European Journal of Dentistry. vol. 12. no. 04. p. 594-601. 2018.
16. [16] G. Parapuram. M. Mokhtari. and J. Hmida. "An artificially intelligent technique to generate synthetic geomechanical well logs for the bakken formation". Energies. vol. 11. no. 3. p. 680. 2018.
17. [17] I. Farkhoutdinov. "The use of artificial neural networks to solve the "make or buy" problem". Helix. vol. 9. no. 4. p. 5243-5247. 2019.
18. [18] G. Zhou., Y. Ji., X. Chen., and F. Zhang. "Artificial neural networks and the mass appraisal of real estate". International Journal of Online Engineering (Ijoe). vol. 14. no. 03. p. 180. 2018.
19. [19] K. Gharehbaghi. "Artificial neural network for transportation infrastructure systems". Matec Web of Conferences. vol. 81. p. 05001. 2016.
20. [20] S. Fard. "Solving universal approximation problem by hankel approximate identity neural networks in function spaces". The fourth International Conference on Information Science and Cloud Computing (ISCC2015). p. 31. 2016.
21. [21] N. Ganesan., K. Venkatesh., M. Rama., and A. Palani. "Application of neural networks in diagnosing cancer disease using demographic data". International Journal of Computer Applications. vol. 1. no. 26. p. 81-97. 2010.
22. [22] R. Suryanita., H. Maizir., E. Yuniarto., M. Zulfakar. and H. Jingga. "Damage level prediction of reinforced concrete building based on earthquake time history using artificial neural network". Matec Web of Conferences. vol. 138. p. 02024. 2017.
23. [23] Y. Yang., B. Yang., and C. Su. "Application of residual shear strength predicted by artificial neural network model for evaluating liquefaction-induced lateral spreading". Advances in Civil Engineering. vol. 2020. p. 1-15. 2020.
24. [24] K. Stanley and R. Miikkulainen. "Evolving neural networks through augmenting topologies". Evolutionary Computation. vol. 10. no. 2. p. 99-127. 2002.
25. [25] J. Ortiz-Rodriguez., M. Martinez-Blanco., E. Gallego. and H. Vega-Carrillo. "A computational tool design for evolutionary artificial neural networks in neutron spectrometry and dosimetry". Electronics, Robotics and Automotive Mechanics Conference. p. 113-118. 2009.
26. [26] H. Liu., H. Tian., D. Pan., and Y. Li. "Forecasting models for wind speed using wavelet. wavelet packet. time series and artificial neural networks". Applied Energy. vol. 107. p. 191-208. 2013.
27. [27] B. Doucoure., K. Agbossou. and A. Cardenas. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data". Renewable Energy. vol. 92. p. 202-211. 2016.
28. [28] H. Tian., X. Liang. and H. Liu. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks". Applied Energy. vol. 157. p. 183-194. 2015.
29. [29] M. Bilgili., B. Şahi̇n. and A. Yaşar. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data". Renewable Energy. vol. 32. no. 14. p. 2350-2360. 2007.
30. [30] P. Ramasamy., S. Chandel. and A. Yadav. "Wind speed prediction in the mountainous region of India using an artificial neural network model". Renewable Energy. vol. 80. p. 338-347. 2015.
31. [31] S. Salcedo–Sanz., Á. Pérez-Bellido., E. Ortiz-García., A. Portilla-Figueras., L. Prieto. and D. Paredes. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction". Renewable Energy. vol. 34. no. 6. p. 1451-1457. 2009.
32. [32] D. Petković., V. Nikolić., V. Mitić. and L. Kocić. "Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms". Flow Measurement and Instrumentation. vol. 54. p. 172-176. 2017.
33. [33] T. Blanchard and B. Samanta. "Wind speed forecasting using neural networks". Wind Engineering. vol. 44. no. 1. p. 33-48. 2019.
34. [34] A. Lodge and X. Yu. "Short term wind speed prediction using artificial neural networks". International Conference on Information Science and Technology (ICIST). p. 539-542. 2014.
35. [35] Ü. Filik and T. Filik. "Wind speed prediction using artificial neural networks based on multiple local measurements in Eskisehir". Energy Procedia. vol. 107. p. 264-269. 2017.
36. [36] G. Kariniotakis., G. Stavrakakis. and E. Nogaret. "Wind power forecasting using advanced neural networks models". Ieee Transactions on Energy Conversion. vol. 11. no. 4. p. 762-767. 1996.
37. [37] F. Gemici and A. Şahin. "Estimation of wind speed with artificial neural networks method for isparta using meteorological measurement data". International Journal of Energy Applications and Technologies. vol. 8. no. 2. p. 65-69. 2021.
38. [38] T. Komamizu., T. Yasuno. and H. Sori. "Study on output prediction system of wind power generation using complex‐valued neural network with multipoint GPV data". Ieej Transactions on Electrical and Electronic Engineering. vol. 8. no. 1. p. 33-39. 2012.
39. [39] R. Fukuoka., H. Suzuki., T. Komamizu., A. Kuwahara. and T. Yasuno. "Wind speed prediction model using LSTM and 1D-CNN". Journal of Signal Processing. vol. 22. no. 4. p. 207-210. 2018.
40. [40] G. Shigute and J. Li. "A wind speed estimation method for quadcopter using artificial neural network". International Journal of Engineering Research And. vol. V8. no. 04. 2019.
41. [41] A. Kumar., T. Cermak. and S. Misak. "Short-term wind power plant predicting with artificial neural network". International Scientific Conference on Electric Power Engineering (EPE). p. 584-588. 2015.
42. [42] M. Zhao., D. Jiang. and C. Liu. "Research on wind power forecasting method using phase space reconstruction and artificial neural network". International Conference on Sustainable Power Generation and Supply, SUPERGEN. p. 1-5. 2009.
43. [43] Z. Zheng., Y. Chen., X. Zhou., M. Huo., B. Zhao. and M. Guo. "Short-term wind power forecasting using empirical mode decomposition and RBFNN". International Journal of Smart Grid and Clean Energy. vol. 2. no. 2. p. 192-199. 2013.
44. [44] A. Camara., F. Wang. and X. Liu. "Energy consumption forecasting using seasonal arima with artificial neural networks models". International Journal of Business and Management. vol. 11. no. 5. p. 231. 2016.
45. [45] E. Bezerra., R. Leao. and A. Braga. "A self-adaptive approach for particle swarm optimization applied to wind speed forecasting". Journal of Control Automation and Electrical Systems. vol. 28. no. 6. p. 785-795. 2017.
46. [46] A. Cichocki., D. Mandic., A. Phan., G. Zhou., Q. Zhao., C. Caiafa. and H. Phan. "Tensor decompositions for signal processing applications: from two-way to multiway component analysis". Ieee Signal Processing Magazine. vol. 32. no. 2. p. 145-163. 2015.
47. [47] G. Duari and R. Kumar. "Data decomposition for outlier detection". 2023. https://doi.org/10.21203/rs.3.rs-2565842/v1
48. [48] H. Zhang., P. Li., H. Ye., D. Shi., Z. Xue., W. Fan. and F. Meng. "Data distribution and tensor influence analysis of different clustering methods". 2023. https://doi.org/10.21203/rs.3.rs-2754780/v1
49. [49] M. Kalugin and I. Evdokimov. "Numerical study of characteristic modes and frequencies of flow in high-speed compressors". Proceedings of the Institute for System Programming of Ras. vol. 29. no. 1. p. 21-38. 2017.
50. [50] E. Kwok., C. Li., Q. Zhao. and Y. Li. "A novel two-component decomposition for co-polar channels of gf-3 quad-pol data". The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences. vol. XLII-3. p. 745-749. 2018.
51. [51] G. Singh., G. Kaur. and V. Kumar. "Ecg denoising using adaptive selection of IMFs through EMD and EEMD". 2014 International Conference on Data Science & Engineering (ICDSE). p. 228-231. 2014.
52. [52] G. Li., Z. Yang. and H. Yang. "A denoising method of ship radiated noise signal based on modified CEEMDAN. dispersion entropy and interval thresholding". Electronics. vol. 8. no. 6. p. 597. 2019.
53. [53] S. Saminu., G. Xu., S. Zhang., A. Isselmou., A. Jabire., I. Karaye. and I. Ahmad. " Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals". Elektrika- Journal of Electrical Engineering. vol. 19. no. 2. p. 1-11. 2020.
54. [54] F. Hasan. "Chaotic signals denoising using empirical mode decomposition inspired by multivariate denoising". International Journal of Electrical and Computer Engineering (IJECE). vol. 10. no. 2. p. 1352. 2020.
55. [55] C. Lee., K. Huang., Y. Hsieh. and P. Chen. "Optimal intrinsic mode function based detection of motor bearing damages". Applied Sciences. vol. 9. no. 13. p. 2587. 2019.
56. [56] D. Fernandes and M. Suchetha. "Field-programmable gate array implementation of empirical mode decomposition algorithm for electrocardiogram processing". Asian Journal of Pharmaceutical and Clinical Research. vol. 10. no. 13. p. 77. 2017.
57. [57] S. Wang., Q. Liao., D. Liu. Y. Zhou., B. Xu., Y. Wang. and L. Lu. "Identification of power quality disturbances based on EEMD and TEO". Applied Mechanics and Materials. vol. 433-435. p. 469-476. 2013. https://doi.org/10.4028/www.scientific.net/amm.433-435.469
58. [58] P. Bing., W. Liu. and Z. Zhang. "A robust random noise suppression method for seismic data using sparse low-rank estimation in the time-frequency domain". Ieee Access. vol. 8. p. 183546-183556. 2020.
59. [59] Z. He., Z. Haiyang., J. Wang., X. Jin., S. Gao. and J. Pang. "Bp-adaboost algorithm based on variational mode decomposition optimized by envelope entropy for diagnosing the working conditions of a slideway seedling-picking mechanism". Applied Engineering in Agriculture. vol. 37. no. 4. p. 665-675. 2021.
60. [60] J. Li., J. Jiang., X. Fan,. H. Wang., L. Song., W. Liu., J. Yang. and L. Chen. "A new method for weak fault feature extraction based on improved MED". Shock and Vibration. vol. 2018. p. 1-11. 2018.
61. [61] M. Bradford., R. Warren., P. Baldrian., T. Crowther., D. Maynard., E. Oldfieldet., W. Wieder., S. Wood. and J. Kind. "Climate fails to predict wood decomposition at regional scales". Nature Climate Change. vol. 4. no. 7. p. 625-630. 2014.
62. [62] Z. Tian., S. Li. and Y. Wang. "A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed". Wind Energy. vol. 23. no. 2. p. 177-206. 2019.
63. [63] N. Huang., H. Chen., G. Cai., L. Fang. and Y. Wang. "Mechanical fault diagnosis of high voltage circuit breakers based on variational mode decomposition and multi-layer classifier". Sensors. vol. 16. no. 11. p. 1887. 2016.
64. [64] S. Fang., X. Wang. and C. Lu. "Rolling bearing fault diagnosis based on LCD–TEO and multifractal detrended fluctuation analysis". Mechanical Systems and Signal Processing. vol. 60-61. p. 273-288. 2015.
65. [65] A. Hemeda., E. Eladdad. and I. Lairje. "Local fractional analytical methods for solving wave equations with local fractional derivative". Mathematical Methods in the Applied Sciences. 2018.
66. [66] H. Li., B. Fan., R. Jia., F. Zhai., L. Bai. and X. Luo. "Research on multi-domain fault diagnosis of gearbox of wind turbine based on adaptive variational mode decomposition and extreme learning machine algorithms". Energies. vol. 13. no. 6. p. 1375. 2020.
67. [67] S. Barik. "Fault detection and classification of dc microgrid based on vmd". Compel the International Journal for Computation and Mathematics in Electrical and Electronic Engineering. vol. 42. no. 2. p. 302-322. 2022.
68. [68] M. Bouaicha., M. Guerroum., I. Adraoui., H. Gziri., A. Elmahjoub. and M. Zegrari. "Diagnosis of mechanical faults affecting a hydroelectric group by vibration analysis". International Journal of Emerging Technology and Advanced Engineering. vol. 11. no. 11. p. 86-100. 2021.
69. [69] Z. Jiang., Z. Ding., Y. Liu., Y. Wang., X. Hu. and Y. Yang. "A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products". Robotics and Computer-Integrated Manufacturing. vol. 61. p. 101838. 2020.
70. [70] L. Ning., L. Bing., J. Wei. and X. Cungen. "A fault pattern recognition method for rolling bearing based on celmdan and fuzzy entropy". Journal of Vibroengineering. vol. 22. no. 6. p. 1326-1337. 2020.
71. [71] Z. Wang., N. Yang., N. Li., W. Du. and J. Wang. "A new fault diagnosis method based on adaptive spectrum mode extraction". Structural Health Monitoring. vol. 20. no. 6. p. 3354-3370. 2021.
72. [72] D. Kolotkov., S. Anfinogentov. and V. Nakariakov. "Empirical mode decomposition analysis of random processes in the solar atmosphere". Astronomy and Astrophysics. vol. 592. p. A153. 2016.
73. [73] B. Pang., M. Nazari., Z. Sun., J. Li. and G. Tang. "An optimized variational mode extraction method for rolling bearing fault diagnosis". Structural Health Monitoring. vol. 21. no. 2. p. 558-570. 2021.
74. [74] E. Wang., L. Liu., H. Jia., J. Wang., Y. Xu. and X. Xie. "Fault diagnosis method of high voltage circuit breaker based on the combination of time-frequency multi-characteristics of acoustic signal". Journal of Vibroengineering. vol. 25. no. 1. p. 156-170. 2022.
75. [75] Y. Guo., S. Jiang., Y. Yang., X. Jin. and Y. Wei. "Gearbox fault diagnosis based on improved variational mode extraction". Sensors. vol. 22. no. 5. p. 1779. 2022.
76. [76] K. Yang., M. Xu., X. Yang., R. Yang. and Y. Chen. "A novel emg-based hand gesture recognition framework based on multivariate variational mode decomposition". Sensors. vol. 21. no. 21. p. 7002. 2021.
77. [77] T. Zhang and C. Fu. "Application of improved VMD-LSTM model in sports artificial intelligence". Computational Intelligence and Neuroscience. vol. 2022. p. 1-6. 2022.
78. [78] H. Peng and M. Zhang. "Application of deep neural network algorithm in speech enhancement of online English learning platform". Icst Transactions on Scalable Information Systems. p. e13. 2022.
79. [79] H. Li., B. Xu., F. Zhou., B. Yan. & F. Zhou. "Empirical variational mode decomposition based on binary tree algorithm". Sensors. vol. 22. no. 13. p. 4961. 2022.