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Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss

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  • Ning Li

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
    State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710048, China)

  • Fuxing He

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Wentao Ma

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p -power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.

Suggested Citation

  • Ning Li & Fuxing He & Wentao Ma, 2019. "Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss," Energies, MDPI, vol. 12(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:673-:d:207343
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    References listed on IDEAS

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    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    3. Yuchong Huo & Ping Jiang & Yuan Zhu & Shuang Feng & Xi Wu, 2015. "Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties," Energies, MDPI, vol. 8(2), pages 1-21, February.
    4. Marco R. M. Cruz & Desta Z. Fitiwi & Sérgio F. Santos & Sílvio J. P. S. Mariano & João P. S. Catalão, 2018. "Prospects of a Meshed Electrical Distribution System Featuring Large-Scale Variable Renewable Power," Energies, MDPI, vol. 11(12), pages 1-17, December.
    5. Cuiping Li & Shining Zhang & Jiaxing Zhang & Jun Qi & Junhui Li & Qi Guo & Hongfei You, 2018. "Method for the Energy Storage Configuration of Wind Power Plants with Energy Storage Systems used for Black-Start," Energies, MDPI, vol. 11(12), pages 1-16, December.
    6. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
    7. Wei Dong & Qiang Yang & Xinli Fang, 2018. "Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques," Energies, MDPI, vol. 11(8), pages 1-19, July.
    8. Pingping Yun & Yongfeng Ren & Yu Xue, 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method," Energies, MDPI, vol. 11(12), pages 1-23, December.
    9. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    10. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    11. Mohammadi, Kasra & Shamshirband, Shahaboddin & Yee, Por Lip & Petković, Dalibor & Zamani, Mazdak & Ch, Sudheer, 2015. "Predicting the wind power density based upon extreme learning machine," Energy, Elsevier, vol. 86(C), pages 232-239.
    12. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    13. Gugliani, G.K. & Sarkar, A. & Ley, C. & Mandal, S., 2018. "New methods to assess wind resources in terms of wind speed, load, power and direction," Renewable Energy, Elsevier, vol. 129(PA), pages 168-182.
    14. Xiaoyu Shi & Xuewen Lei & Qiang Huang & Shengzhi Huang & Kun Ren & Yuanyuan Hu, 2018. "Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory," Energies, MDPI, vol. 11(11), pages 1-20, November.
    15. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    16. Tang, Pingzhou & Chen, Di & Hou, Yushuo, 2016. "Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 243-248.
    17. Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.
    18. Yining Wang & Da Xie & Xitian Wang & Yu Zhang, 2018. "Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model," Energies, MDPI, vol. 11(11), pages 1-19, November.
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    Cited by:

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    2. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    3. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    4. Yaqi Wang & Renzhou Gui, 2022. "A Hybrid Model for GRU Ultra-Short-Term Wind Speed Prediction Based on Tsfresh and Sparse PCA," Energies, MDPI, vol. 15(20), pages 1-20, October.
    5. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
    6. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    7. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
    8. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).

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