TY - JOUR
T1 - An ensemble forecasting model of wind power outputs based on improved statistical approaches
AU - Kim, Yeojin
AU - Hur, Jin
N1 - Funding Information:
Acknowledgments: This work was supported by the Korea Electric Power Corporation (No.R18XA06-55).
Publisher Copyright:
© 2020 by the authors.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Autoregressive integrated moving average with exogenous variable
KW - Ensemble method
KW - Power curve modeling
KW - Support vector regression
KW - Wind power forecasting
UR - http://www.scopus.com/inward/record.url?scp=85081635757&partnerID=8YFLogxK
U2 - 10.3390/en13051071
DO - 10.3390/en13051071
M3 - Article
AN - SCOPUS:85081635757
SN - 1996-1073
VL - 13
JO - Energies
JF - Energies
IS - 5
M1 - 1071
ER -