Impact Load Forecasting Model Based on Chaotic Multi-objective Antlion Optimization Algorithm and Kernel Extreme Learning Machine

In allusion to the forecasting of impact load, an impact load forecasting model based on chaotic multi-objective antlion optimization algorithm (abbr. CMOALO) and kernel extreme learning machine (abbr. KELM) was proposed. Firstly, to decrease the difficulty of forecasting the ensemble empirical mode decomposition (abbr. EEMD) was utilized to decompose the original impact load into a series of smoother subseries. Secondly, to simultaneously improve the forecasting accuracy and stability of the proposed model, a multi-objective ant lion optimization algorithm (abbr. MOALO) was proposed. Thirdly, to further improve the solution search ability of the algorithm, the MOALO was integrated with chaotic operation to put forward CMOALO algorithm and applying the latter to optimize KELM. Finally, the put forward EEMD-CMOALO-KELM model was verified by true-collected impact load data in a certain region. It can be know by case study that the proposed impact load forecasting model possesses the best performance in both aspects of forecasting accuracy and stability of predicted results.