基于 p 阶最大相关熵准则损失函数的鲁棒极限学习机.

At present, researches on short-term load forecasting mainly focus on the analysis of influencing factors and the optimization of the model, and there are few researches on the robustness of the model. This paper took the extreme learning machine as the research object, conducted in-depth research on the robustness of the model, and applied it to the short-term load forecasting problem. The robustness of the ELM model was affected by the loss function. The current ELM model had poor robustness and low prediction accuracy when dealing with the samples containing outliers. To solve this problem, this paper proposed a new loss function based on p-order maximum correntropy criterion. It applied the loss function to the ELM model to improve its robustness in regression prediction. It proposed a method to estimate the percentage of noise in the actual sample, and gave the degree of noise pollution in the sample before established the load forecasting model. The simulation results show that the proposed algorithm model has better robustness and prediction accuracy when the outlier is more than 12%. [ABSTRACT FROM AUTHOR]