基于p阶最大相关熵准则损失函数的鲁棒极限学习机

当前对于短期负荷预测的研究主要针对影响因素的分析以及模型的改进,很少有对模型的鲁棒性进行研究.以极限学习机(extreme learning machine,ELM)作为研究对象,针对ELM模型的鲁棒性问题进行了深入的研究,并将其应用到短期负荷预测问题中.ELM模型的鲁棒性受损失函数的影响,当前ELM模型在处理含异常点样本时,鲁棒性差、预测精度较低.针对该问题,提出了一种基于p阶最大相关熵准则的损失函数,并将该损失函数应用到ELM模型中,以提高其在短期负荷预测问题中的鲁棒性.提出了一种估计实际样本中异常点百分比的计算方法,在建立短期负荷预测模型之前,估计出实际负荷样本中的异常点百分比.仿真结果表明,在异常点超过12％的样本中,提出的算法模型具有更好的鲁棒性以及预测精度.

Robust extreme learning machine based on p order maximum correntropy criterion loss function

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 the load forecasting model was established. The simulation results show that the proposed algorithm model has better robustness and prediction accuracy when the outlier is more than 12%.