  Performing analytic of household load curves (LCs) has significant value in
predicting individual electricity consumption patterns, and hence facilitate
developing demand-response strategy, and finally achieve energy efficiency
improvement and emission reduction. LC clustering is a widely used analytic
technology, which discovers electricity consumption patterns by grouping
similar LCs into same sub-groups. However, previous clustering methods treat
each LC in the data set as an individual time series, ignoring the inherent
relationship among different LCs, restraining the performance of the clustering
methods. What's more, due to the significant volatility and uncertainty of LCs,
the previous LC clustering approaches inevitably result in either lager number
of clusters or huge variances within a cluster, which is unacceptable for
actual application needs. In this paper, we proposed an integrated approach to
address this issue. First, we converted the LC clustering task into the
community detection task in network. Second, we proposed a clustering approach
incorporated with community detection to improve the performance of LC
clustering, which includes network construction, community detection and
typical load profile extraction. The method convert the data set into a network
in which the inherent relationship among LCs is represented by the edges of the
network. Third, we construct a multi-layer typical load profile directory to
make the trade-off between variances within a cluster and the number of the
clusters, enabling the researchers to assess the LCs and the customers in
different layers according to practical application requirements. The
experiments demonstrate that our integrated approach outperform the
state-of-the-art methods.
