Title:
Wind-power-prediction-problem
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- Update:
- 2013-04-06
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- 刘行
Description: Use of metabolic gray forecast, sample adaptive BP neural network and time sequence analysis respectively conducted wind electric power real-time forecast and before prediction, and using the entropy take the right method to determine a combination of the right weight, the introduction of self-control mechanism, to build feedback, proposed combination forecasting method and based on the time sequence Kalman filtering method. The research results show that the combination forecasting model can reduce the prediction point error appears, the Kalman filter can significantly abatement fluctuations of the original sequence.
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湘潭大学- 庞达凌 李卓 刘行.pdf
