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System bias correction of short-term hub-height wind forecasts using the Kalman filter
- Source :
- Protection and Control of Modern Power Systems, Vol 6, Iss 1, Pp 1-9 (2021)
- Publication Year :
- 2021
- Publisher :
- SpringerOpen, 2021.
-
Abstract
- Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m sā1, the mean absolute error by 14% from 2.71 to 2.34 m sā1, the bias from 0.22 to ā 0.19 m sā1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.
- Subjects :
- TK1001-1841
Wind power
Meteorology
Distribution or transmission of electric power
business.industry
Wind forecasts
Energy Engineering and Power Technology
Kalman filter
TK3001-3521
Grid
Atmospheric boundary layer
Turbine
Wind speed
Term (time)
Numerical model
Electric power system
Production of electric energy or power. Powerplants. Central stations
Weather Research and Forecasting Model
Environmental science
Bias correction
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
business
Wind energy
Subjects
Details
- Language :
- English
- ISSN :
- 23670983 and 23672617
- Volume :
- 6
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Protection and Control of Modern Power Systems
- Accession number :
- edsair.doi.dedup.....b570b92edde652fb24c352dbe7540abe