Electricity information big data based load curve clustering

Clustering analysis of load curves on basis of electricity information big data is an important basis of load characterisitic and electricity consumption ha bits analysis of large users. In view of the slow speed of traditional K-means clustering algorithm in the background of big data, a parallel K-means clustering algorithm is proposed to speed up the clustering procedure. Firstly, all the load curves are de-noised by wavelet decomposing in order to reduce the influence of small fluctuations. Secondly, a multi-core parallel technology based K-means clustering algorithm is applied to load curve clustering. Thirdly, more than 40,000 load curves are clustered by the multi-core parallel technology based K-means clustering algorithm. Test results show that the proposed parallel K-means clustering algorithm can speed up clustering procedure effectively.

[1]  Yong Xu,et al.  K-Means Clustering Algorithm with Refined Initial Center , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[2]  Juan Mora-Flórez,et al.  k-means algorithm and mixture distributions for locating faults in power systems , 2009 .

[3]  Yuanbiao Zhang,et al.  A K-means Clustering Algorithm with Meliorated Initial Centers and its Application to Partition of Diet Structures , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[4]  M. P. Sebastian,et al.  Improving the Accuracy and Efficiency of the k-means Clustering Algorithm , 2009 .

[5]  Jitendra Kumar,et al.  Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets , 2011, ICCS.

[6]  Saint John Walker.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2014 .

[7]  K. Shanti Swarup,et al.  Particle swarm optimization based K-means clustering approach for security assessment in power systems , 2011, Expert Syst. Appl..

[8]  Yanchi Liu,et al.  An Application on Time Series Clustering Based on Wavelet Decomposition and Denoising , 2008, 2008 Fourth International Conference on Natural Computation.

[9]  M. M. Rahman,et al.  Improvement of K-means clustering algorithm with better initial centroids based on weighted average , 2012, 2012 7th International Conference on Electrical and Computer Engineering.

[10]  Jim Z. C. Lai,et al.  A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement , 2009, J. Inf. Sci. Eng..