Machine Learning Prediction of Wind Turbine Energy Production
| dc.contributor.author | Erdem, O.E. | |
| dc.contributor.author | Sarucan, A. | |
| dc.contributor.author | Baysal, M.E. | |
| dc.date.accessioned | 2023-11-11T09:03:39Z | |
| dc.date.available | 2023-11-11T09:03:39Z | |
| dc.date.issued | 2023 | |
| dc.description | Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549 | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.1007/978-3-031-39777-6_21 | |
| dc.identifier.isbn | 9783031397769 | |
| dc.identifier.issn | 2367-3370 | |
| dc.identifier.scopus | 2-s2.0-85172732344 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-031-39777-6_21 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.13091/4765 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
| dc.relation.ispartof | Lecture Notes in Networks and Systems | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Data | en_US |
| dc.subject | Energy | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Forestry | en_US |
| dc.subject | Learning algorithms | en_US |
| dc.subject | Mean square error | en_US |
| dc.subject | Wind power | en_US |
| dc.subject | Wind turbines | en_US |
| dc.subject | Data | en_US |
| dc.subject | Efficient use of energy | en_US |
| dc.subject | Energy | en_US |
| dc.subject | Energy generations | en_US |
| dc.subject | Energy productions | en_US |
| dc.subject | Generation systems | en_US |
| dc.subject | Machine learning algorithms | en_US |
| dc.subject | Machine-learning | en_US |
| dc.subject | Predictive models | en_US |
| dc.subject | Statistical datas | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Machine Learning Prediction of Wind Turbine Energy Production | en_US |
| dc.type | Conference Object | en_US |
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| gdc.description.department | KTÜN | en_US |
| gdc.description.departmenttemp | Erdem, O.E., Konya Technical University, Selçuklu, Konya, Turkey; Sarucan, A., Konya Technical University, Selçuklu, Konya, Turkey; Baysal, M.E., Konya Technical University, Selçuklu, Konya, Turkey | en_US |
| gdc.description.endpage | 180 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q4 | |
| gdc.description.startpage | 173 | en_US |
| gdc.description.volume | 759 LNNS | en_US |
| gdc.description.wosquality | N/A | |
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| gdc.virtual.author | Sarucan, Ahmet | |
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