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


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.


Supported by : Korea Institute of Energy Technology Evaluation and Planning (KETEP)


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