LONG-TERM WIND POWER AND GLOBAL WARMING PREDICTION USING MARS, ANN, CART, LR, AND RF

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dc.contributor.authorYilmaz, Yavuz
dc.contributor.authorNalcaci, Gamze
dc.contributor.authorKanczurzewska, Marta
dc.contributor.authorWeber, Gerhard Wilhelm
dc.date.accessioned2024-02-23T14:37:31Z
dc.date.available2024-02-23T14:37:31Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractThe modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30 year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects.en_US
dc.description.sponsorshipMinistry of Science and Higher Education in Poland [0213/SBAD/0117]en_US
dc.description.sponsorshipAcknowledgment. This work was supported by the Ministry of Science and Higher Education in Poland (Grant No. 0213/SBAD/0117) .en_US
dc.identifier.doi10.3934/jimo.2023162
dc.identifier.issn1547-5816
dc.identifier.issn1553-166X
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.3934/jimo.2023162
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16139
dc.identifier.wosWOS:001132628100001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherAmer Inst Mathematical Sciences-Aimsen_US
dc.relation.ispartofJournal Of Industrial And Management Optimizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWind Power Predictionen_US
dc.subjectMars Regressionen_US
dc.subjectTemperature Effects Of Global Warmingen_US
dc.subjectLong Term Forecastingen_US
dc.titleLONG-TERM WIND POWER AND GLOBAL WARMING PREDICTION USING MARS, ANN, CART, LR, AND RFen_US
dc.typeArticleen_US

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