Wind Power Generation Interval Prediction Method Based on ELM and Error Correction
The process of wind power generation has instability and randomness,and traditional point prediction results have poor accuracy and cannot obtain the range of fluctuation of predicted values.This paper proposes an in-terval forecasting method for wind power generation based on extreme learning machine(ELM)and error correction.First,we used the Pearson correlation coefficient to excavate important features in the data set;then,established an ELM network to generate predicted values,and compared the generated predicted values with the original wind power value to obtain the error of wind power.Then the data were combined into a new data set and input into the trained ELM network model to obtain the corrected error.Finally,the wind power prediction interval was obtained by the pro-posed interval construction method.The experimental simulation shows that the corrected error is smaller than the o-riginal error,and the constructed interval has higher reliability and narrower interval bandwidth,which can describe the output range of wind power generation more accurately.
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