| Title | A Hybrid Method for Short-term Load Forecasting in Power System |
| Authors | 朱祥和 齐欢 |
| Issue Date | 2013 |
| Publisher | Intelligent Control and Automation (WCICA), 2012 10th World Congress on |
| Keywords | short-term load forecasting hybrid method ensemble empirical mode decomposition (EEMD) least square-support vector machine (LS-SVM) BP neural network |
| metadata.dc.description.sponsorship | 无 |
| Citation | Intelligent Control and Automation (WCICA), 2012 10th World Congress on,2012. |
| Abstract | In order to improve the accuracy of power load forecasting, this paper proposes a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD), least square-support vector machine (SVM) and BP nature network as a short-term load forecasting model. At first, the actual power load series is decomposed into different new series based on EEMD. Then the right parameters and kernel functions are chosen to build different LS-SVM model respectively, to forecast each intrinsic mode functions, due to the change regulation of each of all resulted intrinsic mode functions. Finally, we use the BP network to reconstruct the forecasted signals of the components and obtain the ultimate forecasting results. Simulation results show that the proposed forecasting method possesses accuracy. |
| Appears in Collections: | 数学教研室 |
Original Search
