离群数据挖掘方法在电力负荷预测中的应用

Outlier data mining application in power load forecasting

根据负荷预测的理论,通过历史数据为基础进行电力负荷数据预测。由于实际运行过程中,采集数据存在错误,使得获得到的负荷预测曲线包含较大的锯齿状。提出一种新的离群数据挖掘方法,即求二直线的夹角方法寻找尖锐点,离群数据为尖锐点处对应电力负荷有功值,然后使用曲线平滑的方法对这些离群数据进行了处理。实验证明,运用提出的这一新的离群数据挖掘方法处理负荷预测曲线,预测结果明显改进。

According to the theory of power load forecasting,data mining based on historical data of power load data is used in load predicting.For practical operation process,there is an error in data collection,so load forecasting curve contains bigger saw tooth.This paper presents a new outlier data mining approach.It finds the sharp angle points between two straight,which correspond to outliers of power load value,and smoothes the curve at same time outliers are treated.Experiments show that after the new outlier mining approach is applied,load forecast results have improved significantly.