Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Very Short-Term Wind Power Ensemble Forecasting without Numerical Weather Prediction through the Predictor Design

  • Lee, Duehee (Dept. of Electrical Engineering, Konkuk University) ;
  • Park, Yong-Gi (Dept. of Electrical and Electronic Engineering, Youngsan University) ;
  • Park, Jong-Bae (Dept. of Electrical Engineering, Konkuk University) ;
  • Roh, Jae Hyung (Dept. of Electrical Engineering, Konkuk University)
  • Received : 2017.05.15
  • Accepted : 2017.08.05
  • Published : 2017.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

Keywords

References

  1. Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, Rob J. Hyndman, "Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond," International Journal of Forecasting, vol. 32, no. 3, pp. 896-913, July 2016. https://doi.org/10.1016/j.ijforecast.2016.02.001
  2. Chen, Fei, and Jimy Dudhia. "Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity," Monthly Weather Review, vol. 129, no. 4, pp. 569-585, Apr. 2001. https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
  3. Gneiting, Tilmann, and Adrian E. Raftery. "Weather forecasting with ensemble methods," Science, vol. 310, no. 5746, pp. 248-249, Oct. 2005. https://doi.org/10.1126/science.1115255
  4. Athanasios Sfetsos, "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable energy, vol. 21, no. 1, pp. 23-35, Sep. 2000. https://doi.org/10.1016/S0960-1481(99)00125-1
  5. Landry, Mark, Thomas P. Erlinger, David Patschke, and Craig Varrichio. "Probabilistic gradient boosting machines for GEFCom 2014 wind forecasting," International Journal of Forecasting, vol. 32, no. 3, pp. 1061-1066, Sep. 2016. https://doi.org/10.1016/j.ijforecast.2016.02.002
  6. 14th international Conference on the European Energy Market, 6-9, June 2017, Dresden, Germany, http://eem2017.com/program/forecast-competition
  7. Pierre Pinson, Christophe Chevallier, and George N. Kariniotakis. "Trading wind generation from shortterm probabilistic forecasts of wind power," IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1148-1156, Aug. 2007. https://doi.org/10.1109/TPWRS.2007.901117
  8. Friedman Jerome, Trevor Hastie, Robert Tibshirani, The elements of statistical learning, New York: Springer series in statistics, 2001.
  9. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An introduction to statistical learning, New York: springer, 2013.
  10. Jerome H. Friedman, "Greedy function approximation: a gradient boosting machine," Annals of statistics, pp. 1189-1232, Oct. 2001.
  11. Box, George EP, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons, 2015.
  12. Akaike, Hirotugu. "Factor analysis and AIC," Psychometrika, vol. 52, no. 3, pp. 317-332, Sep. 1987 https://doi.org/10.1007/BF02294359
  13. William Wu-Shyong Wei, Time series analysis, Addison-Wesley publication, 1994.
  14. System Identification Toolbox, MATLAB 2015b, MathWorks.
  15. Reinsel, Gregory C., Elements of multivariate time series analysis, Springer Science & Business Media, 2003.
  16. European Energy Market Competition Folder, https://cloudstore.zih.tu-dresden.de/index.php/s/DYt48551xFvUEqM, 2017.