负荷预测中多模型的自动筛选方法

预测模型的多样性一直是负荷预测中特别强调的一个问题.然而,对于多种预测模型,如何在舍弃效果较差的模型的同时选择出比较有效的模型,从而获得更加准确的预测结果,始终是一个难点.文中提出了一种新颖的多模型自动筛选算法,它应用odds-matrix方法来定量确定每种单一模型的权重,每种方法的权重代表了该方法的优劣性,通过权重分布函数判断各个预测模型的显著性.算例表明这种思路可以得到令人满意的预测结果.

MULTI-MODEL AUTOMATIC SIFTING METHODOLOGY IN LOAD FORECASTING

The diversity of models is an important issue in load forecasting. To improve the precision for forecasting, it is necessary to distinguish between better models and bad ones. But this task is very difficult. This paper proposes a novel multi-model automatic sifting methodology to solve this problem. The odds-matrix method of the new algorithm is used to calculate the weight of each model, which reflects the 'optimality' of an individual forecasting model. Thus, the efficiency of each model can be differentiated via evaluating probability distribution function of the weights. Numerical studies show that this method is satisfactory in improving forecasting precision.