基于灰关联加权组合模型的电力负荷预测研究

A Novel Load Forecasting Method Based on Grey Relational Weighing Combination Model

针对灰色系统理论中的预测模型(简称GM(1,1)模型)不太适于中长期负荷预测的不足,以及由历史负荷数据的不同时段建模形成预测灰区间的特点,提出了灰关联加权组合修正方法。从历史负荷与其拟合数值的灰关联度挖掘出负荷发展的“远、近”趋势,对灰区间值进行加权组合,大大提高了GM(1,1)模型的预测精度。使用该方法对某一地区未来几年的负荷预测得到了较为理想的结果,说明该方法对中长期负荷预测非常有效,弥补了GM(1,1)模型在该领域内使用的缺陷,具有一定的理论价值和实际应用价值。

For the grey model is not fit for mid-long term power load forecasting as well as the prediction grey area caused by different periods of historical data, this paper proposes a novel prediction model using the synthesizing grey relational degree as the weights to modify the results from GM （1,1）. Thus the developing trend of power load will be extracted from the grey relational degree. The effectiveness of the proposed model is demonstrated by a test in a certain area. The results show that the method is useful both in theory and in practice.