A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction
AbstractNumerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Hu, M.; Hu, Z.; Yue, J.; Zhang, M.; Hu, M. A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction. Energies 2017, 10, 419.
Hu M, Hu Z, Yue J, Zhang M, Hu M. A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction. Energies. 2017; 10(4):419.Chicago/Turabian Style
Hu, Mengyue; Hu, Zhijian; Yue, Jingpeng; Zhang, Menglin; Hu, Meiyu. 2017. "A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction." Energies 10, no. 4: 419.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.