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

• Published in: Energy (Oxford) 2022-01, Vol.239, p.122340, Article 122340
• Main Authors: Li, Tenghui, Liu, Xiaolei, Lin, Zi, Morrison, Rory
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Artificial neural networks; Economic forecasting; Heuristic methods; Neural networks; Offshore; Offshore energy sources; Offshore operations; Offshore wind power; Optimization; Radial basis function; Radial basis function neural network (RBFNN); Turbines; Wind power; Wind turbine power curve (WTPC); Wind turbines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.122340

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. •The proposed model was validated by the Diebold and Mariano test.•The proposed model had strong robustness on inaccurate measurements.•A novel radial basis function (RBF) was introduced for multi-input.•Nonsymmetric fuzzy means (NSFM) effectively initialized RBF kernels.•A novel metaheuristic was developed for NSFM optimization.