An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches

Yeojin Kim and Jin Hur
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Yeojin Kim: Department of Energy Grid, Sangmyung University, Seoul 03016, Korea
Jin Hur: Department of Energy Grid, Sangmyung University, Seoul 03016, Korea

Energies, 2020, vol. 13, issue 5, 1-11

Abstract: The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method.

Keywords: wind power forecasting; ensemble method; autoregressive integrated moving average with exogenous variable; support vector regression; power curve modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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