The dimension reduction and classification of massive load data are the premise of critical load information extraction and deep data mining. According to the shape characteristics of load curves

this paper proposes a shape clustering algorithm of load curves based on the adaptive piecewise aggregate approximation method with variable temporal resolution. The information of the ramp events and slope-extracted edge point is collected to quantify the shape characteristics and fluctuation level of load curves. Then the users' daily load data sets with variable temporal resolution are reconstructed by the adaptive piecewise aggregate approximation algorithm. Further

the k-shape algorithm is applied in the clustering of daily load curves according to the shape characteristics of load curves. In the k-shape algorithm

the distance measurement method based on the similarity of curve shape is regard as the similarity criterion

and the cluster center is calculated based on the Steiner's sequence optimization method. The practicality and effectiveness of the proposed algorithm in data dimension reduction and load clustering are verified by simulation data and measured data.