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Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information

Author

Listed:
  • Osório, G.J.
  • Matias, J.C.O.
  • Catalão, J.P.S.

Abstract

The non-stationary and stochastic nature of wind power reveals itself a difficult task to forecast and manage. In this context, with the continuous increment of wind farms and their capacity production in Portugal, there is an increasing need to develop new forecasting tools with enhanced capabilities. On the one hand, it is crucial to achieve higher accuracy and less uncertainty in the predictions. On the other hand, the computational burden should be kept low to enable fast operational decisions. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for wind power forecasting in the short-term, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. The strength of this paper is the integration of already existing models and algorithms, which jointly show an advancement over present state of the art. The results obtained show a significant improvement over previously reported methodologies.

Suggested Citation

  • Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
    • Handle: RePEc:eee:renene:v:75:y:2015:i:c:p:301-307
    DOI: 10.1016/j.renene.2014.09.058
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    Cited by:

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    3. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    4. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    5. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    6. Nabi Taheri & Mauro Tucci, 2024. "Enhancing Regional Wind Power Forecasting through Advanced Machine-Learning and Feature-Selection Techniques," Energies, MDPI, vol. 17(21), pages 1-23, October.
    7. Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
    8. Vadim Manusov & Pavel Matrenin & Muso Nazarov & Svetlana Beryozkina & Murodbek Safaraliev & Inga Zicmane & Anvari Ghulomzoda, 2023. "Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
    9. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    10. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
    11. Jafar Tavoosi & Amir Abolfazl Suratgar & Mohammad Bagher Menhaj & Amir Mosavi & Ardashir Mohammadzadeh & Ehsan Ranjbar, 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    12. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    13. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
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