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

Tenghui Li, Xiaolei Liu, Zi Lin and Rory Morrison

Energy, 2022, vol. 239, issue PD

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.

Keywords: Offshore wind power; Wind turbine power curve (WTPC); Radial basis function neural network (RBFNN) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221025883

DOI: 10.1016/j.energy.2021.122340

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