Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye's Wind Data

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dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.contributor.authorOyucu, Saadin
dc.contributor.authorAksoz, Ahmet
dc.date.accessioned2024-10-26T18:05:40Z
dc.date.available2024-10-26T18:05:40Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThis study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in T & uuml;rkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.
dc.description.sponsorshipEuropean Union's Horizon Europe research and innovation program; Horizon Europe
dc.description.sponsorshipThe research was conducted collaboratively by the MOBILERS team at Sivas Cumhuriyet University. The authors also acknowledge Horizon Europe for the support of our research groups. The authors also acknowledge Munur Sacit Herdem for his English proofreading and editing.
dc.identifier.doi10.3390/app14093725
dc.identifier.issn2076-3417
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85192731505
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/app14093725
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29132
dc.identifier.volume14
dc.identifier.wosWOS:001219792300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectmachine learning
dc.subjectwind energy
dc.subjectMLOps
dc.subjectRMSE
dc.subjectlatency
dc.titleIntegrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye's Wind Data
dc.typeArticle

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