Short-term power load forecasting using combination model with improved particle swarm optimization

The power load forecasting has always been important for the secure and economically beneficial operation of a power system. Generally, the power load series always presents complex phenomenon because of the influence of many complicated facts, various forecasting results can be obtained by using different models for a given electric power utility. The combination model is recognized as an appreciative method. In the process of modeling short-time load combination model, the key is to confirm the most optimized weight coefficient. The paper introduces an improved particle swarm optimization (PSO) for power load combination forecasting model weight optimization. The new method applies a self-adaptive weight scale operator and chaos mutation operator to avoid being trapped in the local optimum in conventional particle swarm optimization. The proposed method has been examined and tested on a practical power system. The test results show that the improved PSO has better convergence and faster calculation speed than the basic PSO, and the presented combination forecast model has improved the accuracy.