Multi-step Short-term Load Forecasting Method Based on User Group Division

被引：0

作者：

Chen C. ^{[1
]}

Ma H. ^{[1
]}

Chen L. ^{[1
]}

Ren B. ^{[1
]}

Jin C. ^{[1
]}

Zhang T. ^{[1
]}

机构：

[1] New Energy Photovoltaic Industry Research Center, Qinghai University, Xining

来源：

Gaodianya Jishu/High Voltage Engineering | 2023年 / 49卷 / 10期

基金：

中国国家自然科学基金;

关键词：

attention mechanisms; characteristic selection; clustering; multi-step-ahead forecast; short-term load forecast; user group division;

D O I：

10.13336/j.1003-6520.hve.20221841

中图分类号：

学科分类号：

摘要：

The diversity and stochastic characteristics of loads in new power systems are becoming increasingly obvious, meanwhile, it is required that single-step load forecasting should make reference to first-order lag characteristics, which cannot predict the observations of multiple time steps in advance. Therefore, it is necessary to propose a multi-step load forecasting method which is applicable to the new characteristics of loads. In this paper, a multi-step short-term load forecasting method based on user group division is proposed. First, typical characteristics are selected from the external factors affecting user load by using the maximum correlation minimum redundancy criterion, and user groups are divided based on the correlation coefficient between each user load and the typical characteristics. Then, a global attention model is constructed for each user group, and the information known at the moment of iteratively inputting on the decoding side of the model is complementarily forecast. Finally, the output of each model is summed to achieve an accurate multi-step forecast of the load of the entire user group. The results of the case study with a publicly available dataset show that the proposed method has greater advantages in forecasting accuracy and stability. © 2023 Science Press. All rights reserved.

引用

页码：4213 / 4222

页数：9

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