Research on Ultra-Short-Term Wind Power Forecasting Based on Refactored Representation of Environmental Features
Pages 375 - 379
Abstract
The Ultra-short-term wind power forecasting is of great significance to the safe and stable operation of the power system and the optimal allocation of energy. Aiming at the actual needs of the increasing accuracy of wind power forecasting, this paper draws on the method of environmental feature decomposition and reconstruction by EEMD, and predicts the wind power by using improved Random Forest algorithm. After calculation experiments on time series data from Xinjiang wind power plant, the prediction effect of reconstructed environmental features by EEMD and Random Tree, is compared with other machine learning algorithms (including SVM and Decision Tree). The results show that after feature reconstructed by EEMD, the prediction scores of RMSE and MAE of all three algorithms are reduced, and the prediction method of Random Forest has the best effect.
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Published: 11 April 2022
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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
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