中文摘要：

针对目前短期电力负荷预测方法未充分利用电力用户用电特征,以及预测精度不高等问题,提出了＂分层-汇集＂模型。首先,对电力用户按用电特征＂分层＂,得到表征不同类型电力用户用电特征的层负荷特性曲线,并将层负荷特性曲线作为构造总负荷曲线的属性因子;之后,＂汇集＂不同日的层负荷特性曲线,结合实时负荷训练模型;最后,进行回归预测。以某区域实际电力负荷数据为算例,基于所提出的预测方法进行负荷预测。结果显示,基于＂分层-汇集＂模型的短期电力负荷预测在平均百分误差（mean absolute percentage error,MAPE）、均方根误差（root-mean-square error,RM SE）以及Pearson（皮尔逊）相关系数3项评价指标上均优于一般的回归预测方法,验证了模型的有效性;在＂分层＂和＂汇集＂阶段采用不同算法组合,＂分层-汇集＂模型均具有较好的预测效果,验证了模型的鲁棒性。使用＂分层-汇集＂模型可以提高负荷预测的精度,为短期电力负荷预测提供了一种新思路。

英文摘要：

In view of problems that the electrical characteristics of power consumers have not been fully shown in shortterm load forecasting and the accuracy of load forecasting is not enough,this paper proposes a new‘layered-confluence＇model. Firstly,we layer the power consumers based on electrical characteristics,obtain the load characteristic curve of each layer with different electrical characteristics of power consumers,and take the load characteristic curve of layer as attribute factor to construct total load curve. Then,we train the model according to real time load data and load characteristic curve of each layer confluence on different days. Finally,we implement the regression prediction. Taking the actual power load data of a region as an example,we forecast load based on the proposed prediction method. The results show that,the short-term power load forecasting method based on‘layered-confluence＇model is superior to general regression forecasting method in 3evaluation indices of mean absolute percentage error（ MAPE）,root-mean-square error（ RMSE） and Pearson＇s correlation coefficient,which verifies the validity of the model. The‘layered-confluence＇model has a good forecast effect through using different algorithm combination in‘layered＇period and‘confluence＇period,which verifies the robustness of the model. ‘Layered-confluence＇mode can improve the precision of load forecasting,which can provide a new idea for the short-term power load forecasting.