Wind speed prediction by utilizing geographic information system and machine learning approach: A case study of Karabük province in Türkiye

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dc.authoridhttps://orcid.org/0000-0003-2373-3357
dc.authoridhttps://orcid.org/0000-0003-2209-3394
dc.authoridhttps://orcid.org/0000-0002-3407-6121
dc.contributor.authorGürsoy, Emrehan
dc.contributor.authorGürdal, Mehmet
dc.contributor.authorGedik, Engin
dc.date.accessioned2025-01-17T06:25:24Z
dc.date.available2025-01-17T06:25:24Z
dc.date.issued2024-12-22
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractThis study analyzed wind speed data for years in Karabük province, Türkiye, using an Artificial Neural Network (ANN) with a Multilayer Perceptron (MLP) feed-forward network. The Bayesian Regularization algorithm was employed, a well-known training algorithm for Multi-Layer Perceptron (MLP) networks. The study investigated the relationship between wind speed and various meteorological parameters such as month, air temperature, relative humidity, and air pressure. The results obtained from the ANN model provided a reliable methodology for predicting future wind speed values in Karabük province. To evaluate the performance of the ANN model, metrics such as Mean Absolute Error (MAE), Average Relative Deviation (ARD), Mean Squared Error (MSE), and R-squared (R2) were utilized. The ANN model demonstrated its efficacy by revealing the highest average wind speeds of 2.7 m/s in Safranbolu province during August, with corresponding MAE, ARD%, MSE, and R2 performance metrics of −0.029, −0.380%, 0.0028, and 0.999, respectively. The maximum measured and predicted Mean Wind Speed (MWS) values were identified in different months across various locations, specifically in August for Eflani, July for both Eskipazar and Karabük CC and September for Safranbolu. Notably, the highest recorded MWS was observed at 42.8 m/s in Eskipazar during July, while the lowest MWS was recorded at 16.4 m/s in Eskipazar in October. Besides, by employing Geographic Information System (GIS) analysis, the average wind speeds were ranked for different districts, with Safranbolu, Eflani, Eskipazar, and Karabük CC having the highest to lowest wind speeds, respectively.
dc.identifier.citationGürsoy, E., Gürdal, M., & Gedik, E. (2024). Wind speed prediction by utilizing geographic information system and machine learning approach: A case study of Karabük province in Türkiye. International Journal of Green Energy, 1–17. https://doi.org/10.1080/15435075.2024.2445093
dc.identifier.doi10.1080/15435075.2024.2445093
dc.identifier.issn1543-5075
dc.identifier.issn1543-5083
dc.identifier.scopus2-s2.0-85212822428
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1080/15435075.2024.2445093
dc.identifier.urihttps://hdl.handle.net/20.500.14619/15003
dc.identifier.wosWOS:001382094000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.ispartofInternational Journal of Green Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural network
dc.subjectgeographic information system
dc.subjectKarabük
dc.subjectmulti-layer perceptron
dc.subjectWind speed
dc.titleWind speed prediction by utilizing geographic information system and machine learning approach: A case study of Karabük province in Türkiye
dc.typeReview Article

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