基于智能相似日识别及偏差校正的短期负荷预测方法

A short-term load forecasting method based on intelligent similar day recognition and deviation correction

在传统负荷预测理论的基础上,提出了基于智能相似日识别及偏差校正的新型短期负荷预测方法。首先构建地市相关因素特征矩阵,通过判断矩阵相关性智能选取负荷相似日,从而实现负荷曲线的一次预测。在此基础上,建立了实时气象偏差校正策略,采用XGBoost算法进行负荷曲线的二次偏差校正,达到短期负荷预测的目标。算例研究表明,该策略能够有效提升短期负荷预测精度,而且具有较好的自适应特性,可以应用于电力系统短期负荷预测实践。

Based on the traditional load forecasting theory,this paper proposes a new short-term load forecasting method based on intelligent similar day recognition and deviation correction.Firstly,the characteristic matrix of prefecture-city and correlation factors is constructed to select the most similar day of load curve through calculating matrix correlation coefficient.On this basis,the real-time meteorological deviation correction strategy which adopts the XGBoost algorithm is established to carry out the secondary deviation correction of the load curve,so as to achieve the goal of short-term load prediction.An example study shows that this strategy can effectively improve accuracy of short-term load forecasting,and also has good adaptive characteristics.Therefore,this method can be applied to the short-term power load forecasting practice.