Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4765
Title: Machine Learning Prediction of Wind Turbine Energy Production
Authors: Erdem, O.E.
Sarucan, A.
Baysal, M.E.
Keywords: Data
Energy
Machine Learning
Forestry
Learning algorithms
Mean square error
Wind power
Wind turbines
Data
Efficient use of energy
Energy
Energy generations
Energy productions
Generation systems
Machine learning algorithms
Machine-learning
Predictive models
Statistical datas
Machine learning
Issue Date: 2023
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: This study used statistical data to create predictive models for effective and efficient use of energy generation systems using machine learning algorithms. Four different models predicted wind energy production and compared with actual production data. Model validity and verification were evaluated using mean square error, mean absolute error, and R-squared values. The model constructed with the random forest method is the most successful model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Description: Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549
URI: https://doi.org/10.1007/978-3-031-39777-6_21
https://hdl.handle.net/20.500.13091/4765
ISBN: 9783031397769
ISSN: 2367-3370
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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