Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance

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dc.contributor.authorAtalan, Yasemin Ayaz
dc.contributor.authorAtalan, Abdulkadir
dc.date.accessioned2025-01-27T21:19:48Z
dc.date.available2025-01-27T21:19:48Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThis study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R-2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R-2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.
dc.identifier.doi10.3390/app15010241
dc.identifier.issn2076-3417
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85214520699
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app15010241
dc.identifier.urihttps://hdl.handle.net/20.500.12428/28737
dc.identifier.volume15
dc.identifier.wosWOS:001394871400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectwind energy prediction
dc.subjectrenewable energy
dc.subjectmachine learning
dc.subjectANOVA
dc.subjectstatistical validation
dc.titleTesting the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
dc.typeArticle

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