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Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

Abstract

In recent years, the development and influence of wind power in the power system have witnessed, which has led to a significant increase in the production and use of wind energy worldwide. Considering the variability of wind velocity, planning, and managing wind intermittency are important parts of wind energy development, so predicting wind speeds for high-efficiency energy production is one of the most important power system planning issues. Nowadays, machine learning methods are widely used to model complex and nonlinear systems such as wind speed or solar radiation. In this chapter, wind speed prediction models using machine learning applications are presented to solve power system planning problems. This study utilized two machine learning applications called multilayer perceptron (MLP) and group method of data handling (GMDH) to predict wind speed. To evaluate the proposed models, the authors will predict the wind speed for 15 months as a short-term wind speed prediction. Wind speed prediction in the 15 months horizon is done hourly for each day. The presented results illustrate the proposed models’ capability and effectiveness for predicting short-term wind speeds based on historical wind speed data and the good correlation between the predicted and actual values of data. Wind speed forecasting and wind resource assessment can show the right investment direction to decision-makers and investors, thereby developing the wind energy industry and creating a sustainable power system.

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References

  1. O. Sadeghian, A. Moradzadeh, B. Mohammadi-Ivatloo, B. Mohammadi-Ivatloo, M. Abapour, F.P.G. Marquez, Generation units maintenance in combined heat and power integrated systems using the mixed integer quadratic programming approach. Energies 13(11), 2840 (2020). https://doi.org/10.3390/en13112840

    Article  Google Scholar 

  2. R. K. Pachauri et al., Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. (IPCC, 2014)

    Google Scholar 

  3. A. Moradzadeh, O. Sadeghian, K. Pourhossein, B. Mohammadi-Ivatloo, A. Anvari-Moghaddam, Improving residential load disaggregation for sustainable development of energy via principal component analysis. Sustainability (Switzerland) 12(8), 3158 (2020). https://doi.org/10.3390/SU12083158

    Article  Google Scholar 

  4. Ren21. 2019. Renewables 2019 Global Status Report. Paris: Ren21Secretariat. Accessed 16 Nov 2019

    Google Scholar 

  5. S. Madadi, B. Mohammadi-Ivatloo, S. Tohidi, Dynamic line rating forecasting based on integrated factorized Ornstein–Uhlenbeck processes. IEEE Trans Power Deliv 35(2), 851–860 (2019)

    Article  Google Scholar 

  6. S. Madadi, B. Mohammadi-Ivatloo, and S. Tohidi, Probabilistic Real-Time Dynamic Line Rating Forecasting Based on Dynamic Stochastic General Equilibrium with Stochastic Volatility (IEEE Transactions on Power Delivery, 2020)

    Google Scholar 

  7. Y. Hao, C. Tian, A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Appl. Energy 238, 368–383 (2019)

    Article  Google Scholar 

  8. D.B. Alencar, C.M. Affonso, R.C.L. Oliveira, C.R. Jose Filho, Hybrid approach combining SARIMA and neural networks for multi-step ahead wind speed forecasting in Brazil. IEEE Access 6, 55986–55994 (2018)

    Article  Google Scholar 

  9. A. Moradzadeh, S. Zakeri, M. Shoaran, B. Mohammadi-Ivatloo, F. Mohamamdi, Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms. Sustainability (Switzerland) 12(17), 7076 (2020). https://doi.org/10.3390/su12177076

    Article  Google Scholar 

  10. M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, Z. Yan, A review on the forecasting of wind speed and generated power. Renew. Sust. Energ. Rev. 13(4), 915–920 (2009)

    Article  Google Scholar 

  11. F. Cassola, M. Burlando, Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl. Energy 99, 154–166 (2012)

    Article  Google Scholar 

  12. W. Zhang, Z. Qu, K. Zhang, W. Mao, Y. Ma, X. Fan, A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Convers. Manag. 136, 439–451 (2017)

    Article  Google Scholar 

  13. S.N. Singh, A. Mohapatra, Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renew. Energy 136, 758–768 (2019)

    Article  Google Scholar 

  14. S.-K. Sim, P. Maass, P.G. Lind, Wind speed modeling by nested ARIMA processes. Energies 12(1), 69 (2019)

    Article  Google Scholar 

  15. E. Erdem, J. Shi, ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88(4), 1405–1414 (2011)

    Article  Google Scholar 

  16. S. Smyl, A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. Int. J. Forecast. 36(1), 75–85 (2020)

    Article  Google Scholar 

  17. P. Jiang, Z. Liu, Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting. Appl. Soft Comput. 82, 105587 (2019)

    Article  Google Scholar 

  18. W. Zhao, Y.-M. Wei, Z. Su, One day ahead wind speed forecasting: A resampling-based approach. Appl. Energy 178, 886–901 (2016)

    Article  Google Scholar 

  19. S. Scher, J. Molinder, Machine learning-based prediction of icing-related wind power production loss. IEEE Access 7, 129421–129429 (2019)

    Article  Google Scholar 

  20. H. Li, J. Wang, R. Li, H. Lu, Novel analysis–forecast system based on multi-objective optimization for air quality index. J. Clean. Prod. 208, 1365–1383 (2019)

    Article  Google Scholar 

  21. P. Du, J. Wang, W. Yang, T. Niu, Container throughput forecasting using a novel hybrid learning method with error correction strategy. Knowl.-Based Syst. 182, 104853 (2019)

    Article  Google Scholar 

  22. A. Mansour-Saatloo, A. Moradzadeh, B. Mohammadi-Ivatloo, A. Ahmadian, A. Elkamel, Machine learning based PEVs load extraction and analysis. Electronics (Switzerland) 9(7), 1–15 (2020). https://doi.org/10.3390/electronics9071150

    Article  Google Scholar 

  23. H. Yang, Z. Jiang, H. Lu, A hybrid wind speed forecasting system based on a ‘decomposition and ensemble’strategy and fuzzy time series. Energies 10(9), 1422 (2017)

    Article  Google Scholar 

  24. X. Kong, X. Liu, R. Shi, K.Y. Lee, Wind speed prediction using reduced support vector machines with feature selection. Neurocomputing 169, 449–456 (2015)

    Article  Google Scholar 

  25. A. Moradzadeh, A. Mansour-Saatloo, B. Mohammadi-Ivatloo, A. Anvari-Moghaddam, Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Appl. Sci. (Switzerland) 10(11), 3829 (2020). https://doi.org/10.3390/app10113829

    Article  Google Scholar 

  26. D.H. Lim, S.H. Lee, M.G. Na, Smart soft-sensing for the feedwater flowrate at PWRs using a GMDH algorithm. IEEE Trans. Nucl. Sci. 57(1), 340–347 (2010)

    Article  Google Scholar 

  27. G.-R. Ji, P. Han, and Y.-J. Zhai, Wind speed forecasting based on support vector machine with forecasting error estimation. In 2007 International Conference On Machine Learning and Cybernetics, (2007), vol. 5, pp. 2735–2739

    Google Scholar 

  28. N. Shabbir, R. AhmadiAhangar, L. Kütt, M. N. Iqbal, and A. Rosin, Forecasting short term wind energy generation using machine learning. In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2019, pp. 1–4

    Google Scholar 

  29. C. Wan, Z. Xu, P. Pinson, Z.Y. Dong, K.P. Wong, Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans. Power Syst. 29(3), 1033–1044 (2013)

    Article  Google Scholar 

  30. X. Luo et al., Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Trans. Indust. Inform. 14(11), 4963–4971 (2018)

    Article  Google Scholar 

  31. M. Khodayar, J. Wang, Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE Trans. Sustain. Energy 10(2), 670–681 (2018)

    Article  Google Scholar 

  32. A. Ahmadi, M. Nabipour, B. Mohammadi-Ivatloo, A.M. Amani, S. Rho, M.J. Piran, Long-term wind power forecasting using tree-based learning algorithms. IEEE Access 8, 151511–151522 (2020). https://doi.org/10.1109/ACCESS.2020.3017442

    Article  Google Scholar 

  33. D. Zhang, X. Peng, K. Pan, Y. Liu, A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Convers. Manag. 180, 338–357 (2019)

    Article  Google Scholar 

  34. L. Zhang, F. Tian, Performance study of multilayer perceptrons in a low-cost electronic nose. IEEE Trans. Instrum. Meas. 63(7), 1670–1679 (2014)

    Article  Google Scholar 

  35. A. Moradzadeh, K. Khaffafi, Comparison and evaluation of the performance of various types of neural networks for planning issues related to optimal management of charging and discharging electric cars in intelligent power grids. Emerging Sci. J. 1(4), 201–207 (2017). https://doi.org/10.28991/ijse-01123

    Article  Google Scholar 

  36. A. Moradzadeh, K. Pourhossein, B. Mohammadi-Ivatloo, F. Mohammadi, Locating inter-turn faults in transformer windings using isometric feature mapping of frequency response traces. IEEE Trans. Indust. Inform, 1–1 (2020). https://doi.org/10.1109/tii.2020.3016966

  37. I. H. Witten, E. Frank, and Mark A. Hall, Data Mining: Practical Machine Learning (2011)

    Google Scholar 

  38. A. Moradzadeh and K. Pourhossein, Early detection of turn-to-turn faults in power transformer winding: an experimental study. In Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019 (2019), pp. 199–204, doi: https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007169

  39. S. Souahlia, K. Bacha, A. Chaari, MLP neural network-based decision for power transformers fault diagnosis using an improved combination of Rogers and Doernenburg ratios DGA. Int. J. Electr. Power Energy Syst. 43(1), 1346–1353 (2012). https://doi.org/10.1016/j.ijepes.2012.05.067

    Article  Google Scholar 

  40. A. Moradzadeh and K. Pourhossein, Early detection of turn-to-turn faults in power transformer winding: an experimental study. In Proceedings 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019, (2019), pp. 199–204, doi: https://doi.org/10.1109/ACEMP-OPTIM44294.2019.9007169

  41. A.G. Ivakhnenko, New methods of control-system investigation. Control 3(30), 96–99 (1960)

    Google Scholar 

  42. A. G. Ivakhnenko, Polynomial Theory of Complex Systems: IEEE Transactions on Systems, Man and Cybernetics. (ISYMAW, 1971)

    Google Scholar 

  43. A. Moradzadeh and K. Pourhossein, Application of support vector machines to locate minor short circuits in transformer windings. In 2019 54th International Universities Power Engineering Conference (UPEC), (2019), pp. 1–6

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Arash Moradzadeh, Amin Mansour-Saatloo, Morteza Nazari-Heris & Behnam Mohammadi-Ivatloo

  2. Department of Architectural Engineering, Pennsylvania State University, State College, PA, USA

    Morteza Nazari-Heris & Somayeh Asadi

  3. Department of Energy Technology, Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

Authors
  1. Arash Moradzadeh
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  2. Amin Mansour-Saatloo
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  3. Morteza Nazari-Heris
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  4. Behnam Mohammadi-Ivatloo
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  5. Somayeh Asadi
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Corresponding author

Correspondence to Behnam Mohammadi-Ivatloo .

Editor information

Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Moradzadeh, A., Mansour-Saatloo, A., Nazari-Heris, M., Mohammadi-Ivatloo, B., Asadi, S. (2021). Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_12

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  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

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