Wind power prediction in different months of the year using machine learning techniques
Pun, Kesh Bahadur Basnet, Saurav Man Singh Jewell, Ward T.
Pun, Kesh Bahadur
Basnet, Saurav Man Singh
Jewell, Ward T.
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Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2021-04-19
Type
Conference paper
Genre
Keywords
Conferences,Machine learning,Wind power generation,Forecasting,Wind forecasting,Root mean square,Standards
Subjects (LCSH)
Citation
Pun, 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/KPEC51835.2021.9446205
Abstract
Integration 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.
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Publisher
IEEE
Journal
Book Title
Series
2022 IEEE Kansas Power and Energy Conference (KPEC);