Name
Title | ||
|---|---|---|
Evolutionary Ensemble Learning Using Multimodal Multi-objective Optimization Algorithm Based on Grid for Wind Speed Forecasting |
Abstract | ||
|---|---|---|
Improving the accuracy of wind speed forecasting is essential for the usage of wind energy. This paper proposes an evolutionary ensemble learning (EEL) method, which consists of ensemble empirical mode decomposition (EEMD), random vector functional link network (RVFL) based ensemble learning, and grid-based multimodal multi-objective evolutionary algorithm (MMOG). Based on MMOG, the proposed ensemble learning model is improved in terms of accuracy. Several benchmark forecast methods are compared with the proposed EEL model on 12 wind speed forecasting datasets. The experiment results validate the superiority of the proposed EEL model in wind speed forecasting. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CEC45853.2021.9504754 | 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) |
Keywords | DocType | Citations |
Wind Speed Forecasting, Evolutionary Ensemble Learning, Ensemble Empirical Mode Decomposition, Random Vector Functional Link Network, Multimodal Multi-objective Evolutionary Algorithm | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Hu | 1 | 10 | 2.13 |
| Jing J. Liang | 2 | 2073 | 107.92 |
| Boyang Qu | 3 | 0 | 0.34 |
| Jie Wang | 4 | 0 | 0.34 |
| Yanli Wang | 5 | 6 | 1.75 |
| Panpan Wei | 6 | 0 | 0.68 |