Shengqi Wu
ABSTRACT
Accurately predicting the power generation of wind turbines can help with maintaining the safety and stability of the electric power grid, especially for those grids that rely heavily on wind power. A model based on polynomial regression is constructed to predict the output of a wind turbine under certain environment parameters. A comparison between an active learning model and a random-selection model based on the same regression algorithm is carried out to examine the effectiveness of active learning on reducing the amount of data required for training a model.
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