Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1735669 | Energy | 2010 | 8 Pages |
Abstract
Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising.
Related Topics
Physical Sciences and Engineering
Energy
Energy (General)
Authors
Jianzhou Wang, Suling Zhu, Wenyu Zhang, Haiyan Lu,