| Title |
Developing Wind Power Forecasting Algorithm Based on the Support Vector Machine and Gradient Boosting Machine for the KPX Wind Power Forecasting Competition |
| Authors |
박은총(Eunchong Park) ; 김수연(Sooyeon Kim) ; 손승우(Seungwoo Son) ; 박서영(Seoyoung Park) ; 이두희(Duehee Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.12.1694 |
| Keywords |
Gradient boosting machine; Support vector machine; Short-term forecasting; Ensemble; Wind power forecasting |
| Abstract |
We propose a new method that improves the prediction accuracy of wind power generation by using two machine learning algorithms, support vector machine (SVM) and gradient boosting machine (GBM). We participate in the wind power forecasting competition held by KPX to verify the performance. First, we construct individual models in parallel using the data only at the corresponding target time since the data quality of weather data decreases as the target time increases. Second, we use the ensemble method by using two machine learning algorithms, SVM and GBM. Third, we extend the wind power generation data by interpolation to reduce the variation and estimate actual wind power generation. Fourth, we reconstruct the extended wind power generation data to prevent from converging to the average value and. We describe characteristics of stepwise model and present each result with normalized mean absolute error. |