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Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms
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Yin, Xiuxing and Zhao, Xiaowei (2019) Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms. Energy, 186 . 115704. doi:10.1016/j.energy.2019.07.034 ISSN 0360-5442.
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WRAP-big-data-driven-multi-objective-predictions-offshore-wind-farm-based-machine-learning-algorithms-Zhao-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.energy.2019.07.034
Abstract
This paper explores the big data driven multi-objective predictions for offshore wind farm based on machine learning. A data-driven prediction framework is proposed to predict the wind farm power output and structural fatigue. Unlike the existing methods that are normally based on analytical models, mainly focus on single objective and ignore the control contributions, the proposed framework uses the turbine control inputs, inflow wind velocity and directions as the predictor variables. It is constructed by training five typical machine learning approaches: the general regression neural network (GRNN), random forest (RF), support vector machine (SVM), gradient boosting regression (GBR) and recurrent neural network (RNN). The assessment of these approaches is based on the FLOw Redirection and Induction in Steady State (FLORIS) under 6 different scenarios. The test results in different cases are highly consistent with each other and validate that very minor accuracy differences exist among these approaches and they all can achieve the relative accuracy of around 99% or more, which is sufficiently accurate for practical applications. The RNN and SVM exhibit the best accuracy, and particularly the RNN has the best accuracy in thrust predictions. The results also demonstrate that the GRNN has the best computational efficiency.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Big data, Offshore wind power plants, Machine learning, Wind power plants | ||||||||
Journal or Publication Title: | Energy | ||||||||
Publisher: | Elsevier Ltd | ||||||||
ISSN: | 0360-5442 | ||||||||
Official Date: | 9 July 2019 | ||||||||
Dates: |
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Volume: | 186 | ||||||||
Article Number: | 115704 | ||||||||
DOI: | 10.1016/j.energy.2019.07.034 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 28 August 2019 | ||||||||
Date of first compliant Open Access: | 9 July 2020 | ||||||||
RIOXX Funder/Project Grant: |
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