| Abstract: |
| Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the
power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact
on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of
power system operation under uncertainties in an economical and technical way.
Methods: In this paper, a combined approach based on Extreme Learning Machine (ELM) and an error correction
model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the
short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the
short-term forecasting error by persistence method.
Results: For short-term forecasting, the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE
(Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term
forecasting after error correction, most of forecasting errors lie in the interval of [−10 MW, 10 MW]. The error
distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %.
Conclusion: The ultra-short-term wind power forecasting accuracy is further improved by using error correction in
terms of normalized root mean squared error (NRMSE). |
| Key words: Ultra-short-term forecasting, Wind power forecasting, Extreme Learning Machine, Error correction |
| DOI：10.1186/s41601-016-0016-y |
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| Fund: |
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