Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
| dc.contributor.author | Çakıroğlu, Celal | |
| dc.contributor.author | Demir, Sercan | |
| dc.contributor.author | Özdemir, Mehmet Hakan | |
| dc.contributor.author | Aylak, Batin Latif | |
| dc.contributor.author | Sariisik, Gencay | |
| dc.contributor.author | Abualigah, Laith | |
| dc.date.accessioned | 2024-04-04T18:41:59Z | |
| dc.date.available | 2024-04-04T18:41:59Z | |
| dc.date.issued | 2023 | |
| dc.department | TAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü | en_US |
| dc.description.abstract | Wind energy increasingly attracts investment from many countries as a clean and renewable energy source. Since wind energy investment cost is high, the efficiency of a potential wind power plant should be determined using wind power prediction models and wind speed data before installation. Accurate wind power estimation is crucial to set up comprehensive strategies for wind power generation. This study estimated the power produced in a wind turbine using six different regression algorithms based on machine learning using temperature, humidity, pressure, air density, and wind speed data. The proposed estimation model was evaluated on the data received between 2011 and 2020 at station 17,112 in Çanakkale, Turkey. XGBoost, Random Forest, LightGBM, CatBoost, AdaBoost, and M5-Prime algorithms were used to create predictive models. Furthermore, model explanations were presented using the SHAP methodology. Among the regression algorithms evaluated according to the R2 performance metric, the best performance was obtained from the XGBoost algorithm. Regarding computational speed, the LightGBM model emerged as the most efficient model. The wind speed was | |
| dc.identifier.citation | Çakıroğlu, C., Demir, S., Özdemir, Mehmet H., Aylak, Batin L., Sariisik, G., Abualigah, L. (2023). Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis. 237, 1-12. Elsevier. | |
| dc.identifier.doi | 10.1016/j.eswa.2023.121464 | |
| dc.identifier.endpage | 12 | en_US |
| dc.identifier.issue | 237 | en_US |
| dc.identifier.scopus | 2-s2.0-85171140867 | |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12846/1030 | |
| dc.identifier.wos | WOS:001075995300001 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Renewable energy | en_US |
| dc.subject | Wind power | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Predictive modeling | en_US |
| dc.title | Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis | |
| dc.type | Article |
