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dc.contributor.authorPun, Kesh
dc.contributor.authorBasnet, Saurav M.S.
dc.contributor.authorJewell, Ward T.
dc.identifier.citationPun, K., Basnet, S. M. S., & Jewell, W. (2021). Wind power prediction in different months of the year using machine learning techniques. Paper presented at the 2021 IEEE Kansas Power and Energy Conference, KPEC 2021, doi:10.1109/KPEC51835.2021.9446205en_US
dc.descriptionClick on the DOI link to access this conference paper at the publishers website (may not be free).en_US
dc.description.abstractIntegration of wind power into the grid has been rapidly increasing at both the transmission as well as distribution levels. Wind power generation is variable, nonlinear, and intermittent in nature. The monthly average and maximum wind power generation vary over the year. To effectively integrate wind power into the grid, it is vital to provide forecasting for different months. Therefore, the machine learning technique has been applied to forecast the wind power generation for each month separately. Its accuracy, root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) of forecasting error have been analyzed for every month and the whole year.en_US
dc.relation.ispartofseries2022 IEEE Kansas Power and Energy Conference (KPEC);
dc.subjectMachine learningen_US
dc.subjectWind power generationen_US
dc.subjectWind forecastingen_US
dc.subjectRoot mean squareen_US
dc.titleWind power prediction in different months of the year using machine learning techniquesen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2021, IEEEen_US

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