Forecasting of Wind Energy Generation in Alberta

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dc.contributor.advisorSezer, A. Deniz
dc.contributor.authorLuo, Yilan
dc.contributor.committeememberWood, David H.
dc.contributor.committeememberDeardon, Rob
dc.date2018-11
dc.date.accessioned2018-09-26T15:55:53Z
dc.date.available2018-09-26T15:55:53Z
dc.date.issued2018-09-14
dc.description.abstractIn this paper, our goal is to build a model for the future wind power generation of Alberta, as Alberta’s wind power capacity is growing, and new wind farms are expected to be built in the near future. An important feature of the wind power data is spatial and temporal correlation. To capture this, we model the wind power generation in Alberta as a spatio-temporal process. We apply the method of Gaussian random fields to analyze the wind power time series of 20 wind farms of Alberta. Following the work of Gneiting et al. [11] , we build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable symmetric, and non-symmetric. We compare the performance of the models using simple kriging. We also use kriging to demonstrate the performance of the models to forecast the future wind generation for both an existing wind farm and a new farm in Alberta. In the end, we also formulate the mean and variance of the aggregate wind power generation in Alberta.en_US
dc.identifier.citationLuo, Y. (2018). Forecasting of Wind Energy Generation in Alberta. University of Calgary, Calgary, AB. doi:10.11575/PRISM/32986en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32986
dc.identifier.urihttp://hdl.handle.net/1880/108045
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectGaussian Process
dc.subjectSpatial-Temporal Process
dc.subjectWind Energy Generation
dc.subjectForecasting
dc.subjectAggregate Wind Energy
dc.subject.classificationEducation--Mathematicsen_US
dc.subject.classificationStatisticsen_US
dc.subject.classificationEnergyen_US
dc.titleForecasting of Wind Energy Generation in Alberta
dc.typemaster thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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