| 引用本文: | 魏 勇,李学军,李万伟,等.基于空间密度聚类和K-shape算法的城市综合体
负荷模式聚类方法[J].电力系统保护与控制,2021,49(14):37-44.[点击复制] |
| WEI Yong,LI Xuejun,LI Wanwei,et al.Load pattern clustering method of an urban complex based on DBSCAN and K-shape algorithm[J].Power System Protection and Control,2021,49(14):37-44[点击复制] |
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| 摘要: |
| 城市综合体作为新兴负荷种类，其负荷预测的精度直接影响电网的规划与安全运行。然而城市综合体负荷模式易受外部环境影响而出现异常变化，对其直接进行预测的精度不能满足实际运行的要求，需要对城市综合体负荷进行聚类以提取不同的负荷模式来提高预测的精度，因此提出了一种基于空间密度聚类和K-shape算法的城市综合体负荷模式聚类方法。首先利用自适应空间密度算法(DBSCAN)根据不同区域的密度大小来提取不同季节下综合体负荷的典型日负荷曲线。然后利用K-shape聚类算法在不同季节下对不同综合体的典型日负荷曲线进行聚类分析。最后将仿真结果与K-means、K-medoids的聚类结果进行对比。仿真结果表明，与其他两种方法相比，提出的DBSCAN-K-shape两阶段负荷模式聚类方法对城市综合体负荷进行聚类在不同的聚类指标下均具有较高的精度。 |
| 关键词: 城市综合体 负荷预测 日负荷模式 空间密度聚类 K-shape聚类 |
| DOI：DOI: 10.19783/j.cnki.pspc.201256 |
| 投稿时间：2020-10-17修订日期：2020-12-24 |
| 基金项目:国家电网有限公司科技项目资助(SGGSJY00PSJS 1900123) |
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| Load pattern clustering method of an urban complex based on DBSCAN and K-shape algorithm |
| WEI Yong1,LI Xuejun2,LI Wanwei3,LIU Jiaming4,WANG Yuxi4,LI Zhenghui4,WANG Fei4 |
| (1. Economic Technology Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China;
2. State Grid Gansu Electric Power Company, Lanzhou 730000, China; 3. State Grid Wuwei Power Supply Company,
Wuwei 733000, China; 4. North China Electric Power University, Baoding 071003, China) |
| Abstract: |
| As an emerging load category of an urban complex, load forecasting accuracy directly affects the planning and safe operation of the power grid. However, the load pattern of the urban complex is prone to change abnormally because of the influence of the external environment, and the accuracy of direct prediction cannot meet the requirements of actual operation. Therefore, it is necessary to cluster the load of the urban complex to extract different load patterns to improve the accuracy of prediction. This paper proposes a load pattern clustering method based on density-based spatial clustering and K-shape. First, the DBSCAN algorithm is used to extract the typical daily load curve of the complex load in different seasons according to the density of other regions. Then a K-shape clustering algorithm is used to cluster the typical daily load curves of different complexes in different seasons. Finally, the simulation results are compared with the clustering results of K-means and K-medoids. The results show that, compared with the other two methods, the two-stage load pattern clustering method of DBSCAN-K-shape proposed in this paper has high precision under different clustering indexes for urban complex load clustering.
This work is supported by the Science and Technology Project of State Grid Corporation of China (No. SGGSJY00PSJS1900123). |
| Key words: urban complex load forecasting daily load pattern density-based spatial clustering K-shape clustering |
