动态相似与静态相似相结合的短期负荷预测方法

Short-term load forecasting based on the combination of dynamic similarity and static similarity

气温对负荷的影响存在累积效应,这导致气象条件与日类型相同两天的负荷仍然可能有较大的差异。因此,现有的以当天气象条件和日类型为特征参量的相似日选取方法不能保证预测的准确率。针对目前累积效应带来的影响,提出了一种动态相似的思路,并且将其与现有的静态相似方法相结合得到一种新的短期负荷预测方法。运用解耦模型分别对待预测日的日平均负荷和负荷曲线形状进行预测。采用动态相似的思路进行日平均负荷预测,采用静态相似日的思路进行负荷曲线形状的预测。算例中,分别以对节假日的负荷预测以及连续高温日期的负荷预测为例,通过对北京某地区的实例计算,结果表明,该预测方法可以提高短期负荷预测的准确率。

The influence of temperature on the load has a cumulative effect, which leads to a big difference on the load even the weather conditions and the day type of the two days are the same. The current similarity data selection methods which take weather conditions and the day type as the characteristic parameters cannot guarantee the accuracy of prediction. Thereby, a dynamic similar method is proposed, and a new short-term load forecasting method is proposed by combining it with the existing methods. The daily average load and the load curve of candidate prediction day are forecasted respectively by using decoupling model. The daily average load is predicted by the dynamic similar method and the static similar method is used for the load curve prediction. In the case study, the first day of holiday and the day of continuous high temperature are taken for the load forecast as an example. Through instance simulation and calculation of a district in Beijing, it is shown that the proposed method can improve the accuracy of the holiday load forecast.