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Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm

Author

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  • Li, Tenghui
  • Liu, Xiaolei
  • Lin, Zi
  • Morrison, Rory

Abstract

Offshore wind energy is drawing increased attention for the decarbonization of electricity generation. Due to the unpredictable and complex nature of offshore aero-hydro dynamics, the Wind Turbine Power Curve (WTPC) model is an important tool for power forecasting and, hence, providing a reliable, predictable, and stable power supply. With the development of data-driven approaches, the Artificial Neural Network (ANN) has become a popular method for estimating WTPCs. This paper integrates the Isolation Forest (iForest), Nonsymmetric Fuzzy Means (NSFM) Radial Basis Neural Network (RBFNN), and metaheuristic algorithm to form a novel WTPC model. iForest performed anomaly detection and removal, NSFM RBFNN approximated the WTPC, and the metaheuristic solved NSFM optimization without training RBFNN. Four real-world datasets were used to assess the performance of NSFM RBFNN. According to multiple evaluation metrics and the Diebold-Mariano test, the accuracy of NSFM RBFNN was significantly better than the other competitive neural network-based methods. Additionally, NSFM RBFNN was shown to be more robust to anomalies than competitors, which is highly beneficial for practical applications.

Suggested Citation

  • Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221025883
    DOI: 10.1016/j.energy.2021.122340
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    8. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    9. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    10. Liu, Gang & Wang, Kun & Hao, Xiaochen & Zhang, Zhipeng & Zhao, Yantao & Xu, Qingquan, 2022. "SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system," Energy, Elsevier, vol. 241(C).
    11. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.
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