Research Article
An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis
@INPROCEEDINGS{10.1007/978-3-030-64214-3_10, author={Zhongqin Bi and Yabin Leng and Zhe Liu and Yongbin Li and Stelios Fuentes}, title={An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={Cluster analysis Time series Fast-DTW Spectral clustering}, doi={10.1007/978-3-030-64214-3_10} }- Zhongqin Bi
Yabin Leng
Zhe Liu
Yongbin Li
Stelios Fuentes
Year: 2020
An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_10
Abstract
Cluster analysis of power loads can not only accurately extract the commonalities and characteristics of the loads, but also help to understand the users’ habits and patterns of electricity consumption, so as to optimize the power dispatching and regulate the operation of the entire power grid. Based on the traditional clustering methods, this paper proposes a clustering algorithm that can automatically determine the optimal cluster number. Firstly, Fast-DTW algorithm is used as the similarity measuring function to calculate the similar matrix between two time series, and then Spectral Clustering and Affinity Propagation (AP) algorithm are used for clustering. It is combined with Euclidean distance, DTW and Fast-DTW algorithms to evaluate the algorithm effect. By analyzing the actual power data, our results show that the improved external performance evaluation index ARI, AMI and internal performance evaluation index SSE are significantly improved and have better time series similarity and accuracy. Applying the algorithm to more than six thousands of users, twelve kinds of typical power load patterns can be obtained. For any other load curve, it can be mapped to a standard load by feature extraction. The corresponding prediction model is adopted, which is of great significance to reduce the peak power consumption, adjust the electricity price appropriately and solve the problem of system balance.
