Research on Short-Term Wind Power Forecasting Based on RMADE-SEN-IRFR
- 10.2991/iceeecs-16.2016.158How to use a DOI?
- Wind Power Forecasting; Random Forest Regression; M5P Model Tree; Differential Evolution; Selective Ensemble
To solve the problem of slowly convergence rate,weakly generalization ability, complicated model structure and parameter determination method of traditional machine learning model, the RFR model is introduced to forcast the short-term wind power. The wind power prediction model of RFR has the advantages of generalized error control, fast convergence rate, few adjustment parameters, strong interpretability, but its prediction accuracy is low and the computational complexity is high. So this paper constructs an RFR model using the M5P model tree and improves the accuracy of wind power prediction and reduces the computational complexity by combining the selective ensemble method (SEN) based on improved differential evolution algorithm (RMADE). Experimental results show that the proposed method has higher prediction accuracy and better generalization performance than SVM improved by GA (GA-SVM), OS-ELM and traditional RFR.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Baoyi Wang AU - Hui Wang AU - Shaomin Zhang PY - 2016/12 DA - 2016/12 TI - Research on Short-Term Wind Power Forecasting Based on RMADE-SEN-IRFR BT - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SP - 806 EP - 811 SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.158 DO - 10.2991/iceeecs-16.2016.158 ID - Wang2016/12 ER -