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dc.contributor.advisorDuran, Hasan Enginen_US
dc.contributor.advisorBingöl, Ferhaten_US
dc.contributor.authorYıldırım, Nurseda
dc.date.accessioned2015-05-11T11:29:58Z
dc.date.available2015-05-11T11:29:58Z
dc.date.issued2014
dc.identifier.citationYıldırım, N. (2014). Modelling and fitting of the wind data using different time series models and investigating the relared applications of fitted data. Urla and RisØ cases. Unpublished master's thesis, İzmir Institute of Technology, İzmir, Turkeyen_US
dc.identifier.urihttp://hdl.handle.net/11147/4287
dc.descriptionThesis (Master)--Izmir Institute of Technology, Energy Engineering, Izmir, 2014en_US
dc.descriptionIncludes bibliographical references (leaves: 83-85)en_US
dc.descriptionText in English; Abstract: Turkish and Englishen_US
dc.descriptionxiii, 121 leavesen_US
dc.description.abstractThis thesis is prepared as an outcome of Energy Engineering Master of Science program at IZTECH. Main purpose of this study is to investigate the possible ways of estimating the evolution of wind speed in Turkey, which is useful in predicting the wind power generation. Wind Energy has recently been recognized as one of the most promising renewable energy sources in the world. Despite its high potential, one major problem is that it is an intermittent energy source which follows, in general, statistically a quite noisy evolution with large variability and difficulty in forecasting. Standard time series models have been employed to forecast the wind speed in the literature (such as ARIMA, ARMA). The majority of these, however, are based on a univariate modelling. This is likely to create a significant loss in forecast accuracy as the important dynamics of wind such as ambient temperature, absolute pressure, wind direction and humidity are ignored. So, aim of the present study is to incorporate these factors in a multivariate VAR setting and estimate the wind speed in 4 different locations around Urla City (nearby Izmir-Turkey) by employing hourly data between June-2000 and October-2001. To provide a benchmark, I also compare estimations from VAR with the predictions from ARIMA and SARIMA models. The results indicate two important conclusions. First, it has been shown that all models provide an accurate estimate of wind speed. Second, multivariate VAR and SARIMA is clearly shown to outperform the ARIMA model by improving the wind speed predictions and producing less forecast errors. Thus, these models are demonstrated to be helpful in estimating the wind power generation as well.en_US
dc.language.isoengen_US
dc.publisherIzmir Institute of Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWinden_US
dc.subjectWind energyen_US
dc.subjectWind energy forecastingen_US
dc.subjectVARen_US
dc.subjectSARIMAen_US
dc.subjectWAsPen_US
dc.titleModelling and fitting of the wind data using different time series models and investigating the relared applications of fitted data. Urla and RisØ casesen_US
dc.title.alternativeRüzgar verilerinin çeşitli zaman serisi yöntemleriyle modellenmesi, üretilen verinin uygulama alanlarının incelenmesi. Urla ve RisØ örnekleri.en_US
dc.typemasterThesisen_US
dc.contributor.departmentIzmir Institute of Technology. Energy Systems Engineeringen_US
dc.relation.publicationcategoryTezen_US


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