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A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

Author

Listed:
  • Sen Wang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Yonghui Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Yan Zhou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Rabea Jamil Mahfoud

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Dongchen Hou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

Suggested Citation

  • Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:87-:d:301203
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    References listed on IDEAS

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    Cited by:

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    3. Wen, Yan & Pan, Su & Li, Xinxin & Li, Zibo & Wen, Wuzhenghong, 2024. "Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach," Energy, Elsevier, vol. 299(C).
    4. Kaiyan Wang & Haodong Du & Rong Jia & Hongtao Jia, 2022. "Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction," Sustainability, MDPI, vol. 14(19), pages 1-27, October.
    5. Nailya Maitanova & Jan-Simon Telle & Benedikt Hanke & Matthias Grottke & Thomas Schmidt & Karsten von Maydell & Carsten Agert, 2020. "A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports," Energies, MDPI, vol. 13(3), pages 1-23, February.

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