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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Author(s): Ruiqing Shan*, Jitao Niu, Xuzheng Chai and Qingfa Gu

Volume 17, Issue 4, 2024

Published on: 07 September, 2023

Page: [401 - 407] Pages: 7

DOI: 10.2174/2352096516666230818145947

Price: $65

Abstract

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching.

Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power.

Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results.

Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA.

Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

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[1]
W. Zhang, L. Zhang, J. Wang, and X. Niu, "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting", Appl. Energy, vol. 277, p. 115561, 2020.
[http://dx.doi.org/10.1016/j.apenergy.2020.115561]
[2]
S. Roga, "Wind energy investigation of straight-bladed vertical axis wind turbine using computational analysis", EAI Endorsed Trans. Energy Web., vol. 8, no. 33, p. e5, 2020.
[3]
R. Sukanta, and L.D. Sudhir, "DMST approach for analysis of 2 and 3 bladed type darrieus vertical axis wind turbine", EAI Endorsed Trans. Energy Web., vol. 8, no. 33, p. e2, 2020.
[4]
J. He, and Y. Cao, "Wind power mid-long term load forecasting based on LSTM-RF combination forecasting method", J. Shanghai Univ. Electric Power, vol. 36, no. 04, pp. 341-350, 2020.
[5]
H.B. Azad, S. Mekhilef, and V.G. Ganapathy, "Long-term wind speed forecasting and general pattern recognition using neural networks", IEEE Trans. Sustain. Energy, vol. 5, no. 2, pp. 546-553, 2014.
[http://dx.doi.org/10.1109/TSTE.2014.2300150]
[6]
Z. Jin, J. Zhu, L. Xu, P. Xie, and P. Xuan, "Simulation of wind power output series based on space-time auto-regressive moving average model", Dianli Xitong Zidonghua/Autom., vol. 43, no. 03, pp. 101-107, 2019.
[7]
Chen Chen, "Short term wind power prediction based on Calman filter algorithm", J. Zhejiang. Univ., vol. 2015, 2015.
[8]
A.P. Marugán, F.P.G. Márquez, J.M.P. Perez, and D. Ruiz-Hernández, "A survey of artificial neural network in wind energy systems", Appl. Energy, vol. 228, pp. 1822-1836, 2018.
[http://dx.doi.org/10.1016/j.apenergy.2018.07.084]
[9]
Y. Liu, Z. Shao, W. Zheng, and J. Zhao, "Piecewise support vector machine model for theoretical wind-power calculation", Taiyang Neng Xuebao, vol. 40, no. 03, pp. 673-680, 2019.
[10]
O. Tinghui, X. Zha, Q. Liang, X. Yi, and H. Huang, "Medium- or long-term wind power prediction with combined models of meteorological multi-Variables", Power Syst. Technol., vol. 40, no. 03, pp. 847-852, 2016.
[11]
X. Hao, Z. Yang, H/ Yan, Z. Han, M. Hui, and D. Zhang, "A medium and long term wind power prediction method based on improved GM-ARMA combination model", Electrotech. J., vol. 2021, no. 7, pp. 11-13, 2021.
[http://dx.doi.org/10.19768/j.cnki.dgjs.2021.07.004]
[12]
X.U. Yuan, Q. Cheng, L.I. Yang, H. Zhang, Y.U. Wei, and H.E. Bing, "Mid-long term load forecasting of power system based on big data clustering", Proceedings of the CSU-EPSA, p. 8, 2017.
[13]
X. Qiu, P.N. Suganthan, and G A J. Amaratunga, "Electricity load demand time series forecasting with empirical mode decomposition based random vector functional link network", 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 09-12 Oct, 2016, Budapest, Hungary, 2016.
[14]
D. Niu, and S. Dai, "A short-term load forecasting model with a modified particle swarm optimization algorithm and least squares support vector machine based on the denoising method of empirical mode decomposition and grey relational analysis", Energies, vol. 10, no. 3, p. 408, 2017.
[http://dx.doi.org/10.3390/en10030408]
[15]
A-O. Boudraa, and J-C. Cexus, "EMD-based signal filtering", IEEE Trans. Instrum. Meas., vol. 56, no. 6, pp. 2196-2202, 2007.
[http://dx.doi.org/10.1109/TIM.2007.907967]
[16]
H. Chuan, S. Qin, and H. He, "Application of BP neural network and ICA feature extraction in power load forecasting", Proceedings of the CSU-EPSA, vol. 26, no. 08, pp. 40-46, 2014.
[17]
Hanchuan Peng, Fuhui Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy", IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, 2005.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[18]
L. Xue, and T. Sun, "Quality difference of urbanization in China based on BP neural network", Hubei Agricultural Sciences, vol. 54, no. 1, pp. 229-234, 2015.
[19]
S.Q. Liu, X.F. Lu, R.M. Liu, W.H. Zeng, and M.C. He, "The application of the Bp neutral network in the estimation of non-point source pollution", Procedia Environ. Sci., vol. 13, pp. 263-273, 2012.
[http://dx.doi.org/10.1016/j.proenv.2012.01.025]
[20]
F. Yu, and X. Xu, "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network", Appl. Energy, vol. 134, pp. 102-113, 2014.
[http://dx.doi.org/10.1016/j.apenergy.2014.07.104]
[21]
G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups", IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82-97, 2012.
[http://dx.doi.org/10.1109/MSP.2012.2205597]

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