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

Li, T., Liu, X. , Lin, Z. and Morrison, R. (2022) Ensemble offshore Wind Turbine Power Curve modelling – an integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm. Energy, 239(Part D), 122340. (doi: 10.1016/j.energy.2021.122340)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Tenghui and Liu, Dr Xiaolei and Morrison, Rory

Creator Roles

Li, T.: Conceptualization, Methodology, Software, Investigation, Validation, Formal analysis, Data curation, Writing – original draft
Liu, X.: Conceptualization, Resources, Investigation, Writing – review and editing, Supervision
Morrison, R.: Writing – review and editing

• Authors: Li, T., Liu, X., Lin, Z., and Morrison, R.
• Subjects: T Technology > T Technology (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• ISSN (Online): 1873-6785
• Published Online: 14 October 2021
• Copyright Holders: Copyright © 2021 Elsevier Ltd.
• First Published: First published in Energy 239(Part D): 122340
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
305200		DTP 2018-19 University of Glasgow	Mary Beth Kneafsey	Engineering and Physical Sciences Research Council (EPSRC)	EP/R513222/1	MVLS - Graduate School