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- W3172305890 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." @default.
- W3172305890 created "2021-06-22" @default.
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- W3172305890 date "2021-04-19" @default.
- W3172305890 modified "2023-09-23" @default.
- W3172305890 title "Wind Power Prediction in Different Months of the Year Using Machine Learning Techniques" @default.
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- W3172305890 doi "https://doi.org/10.1109/kpec51835.2021.9446205" @default.
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