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Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network

Author

Listed:
  • Duan, Jiandong
  • Wang, Peng
  • Ma, Wentao
  • Tian, Xuan
  • Fang, Shuai
  • Cheng, Yulin
  • Chang, Ying
  • Liu, Haofan

Abstract

Nowadays, various wind power forecasting methods have been developed to improve wind power utilization. Most of these techniques are designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, there are many outliers in real wind power data due to many uncertain factors such as weather, temperature, and other random factors. Meanwhile, the highly nonlinear process of converting wind energy into wind power may changes the statistical characteristics of errors. Therefore, the prediction model established based on the traditional MSE loss may lead to unsatisfactory results. As a result, a robust short-term wind power hybrid forecasting model based on Long Short-term Memory (LSTM) neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed in this work. The IVMD in which the parameter K in the IVMD is determined by the Maximum Correntropy Criterion (MCC) is used to decompose the original wind power data and the decomposed subseries is reconstructed by SE to improve the prediction efficiency. Then the MCC is also utilized to replace the MSE in the classic LSTM network to develop a novel robust hybrid model to forecast the wind power. Finally, four experiments were conducted using real data from two wind farms in China at different sampling intervals to evaluate the effectiveness of the proposed method, and the results show that proposed method is more effective than other traditional methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220320879
    DOI: 10.1016/j.energy.2020.118980
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    1. Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
    2. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    3. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    4. Wang, Cong & Zhang, Hongli & Ma, Ping, 2020. "Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network," Applied Energy, Elsevier, vol. 259(C).
    5. Sarshar, Javad & Moosapour, Seyyed Sajjad & Joorabian, Mahmood, 2017. "Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting," Energy, Elsevier, vol. 139(C), pages 680-693.
    6. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    7. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    8. 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.
    9. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    10. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
    11. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    12. 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.
    13. Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    14. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    15. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    16. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    17. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
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