面向云边协同的配变短期负荷集群预测

配电变压器是配电网中连接用户的重要设备，研究其负荷变化规律是十分重要的。随着物联网技术在电力系统中的推广，配电网中监测的配电变压器将越来越多，但对众多设备逐一分析建模会导致工作低效。因此，提出面向云边协同的配变负荷预测框架，并着重研究云端的集群预测模型。首先，集群预测模型对配变进行日负荷曲线聚类，提取日负荷模式，并分析各配变日负荷模式变化规律，采用聚类方法划分具有相似用电行为的配变。然后，将同类别配变负荷数据整合训练，利用STL-LSTMs-XGBoost预测模型实现配变的短期负荷集群预测。最后，通过使用某市配变的负荷数据作为算例进行分析，实验结果验证了所提方法的可行性和有效性。

Short-term load cluster forecast of distribution transformers for cloud edge collaboration

The distribution transformer (DT) is an important piece of equipment connecting users in a distribution network, and it is very important to study the law of load changes. With the promotion of IoT technology applied in a power system, more and more DTs are monitored in the distribution network, but analyzing and modeling for many devices one by one will be inefficient. Therefore, this paper proposes a technical framework of distribution transformer load forecast for cloud edge collaboration, focusing on a cluster forecast model in the cloud. First, it performs daily load curve clustering on DTs, extracts daily load patterns, analyzes the changes in daily load patterns of DTs, and puts DTs with similar power consumption behavior into one category. Then, it integrates the same type of DT load data for training, and uses the STL-LSTMs-XGBoost forecasting model to realize short-term load cluster prediction of the DT. By using the load data of a city''s DTs as an example for analysis, the experimental results verify the feasibility and effectiveness of the proposed method.This work is supported by the National Key Research and Development Program of China (No. 2020YFB2009405).