Load profiles clustering is of great significance for the load forecasting

power grid planning and demand response

but massive historical load profiles introduce great challenges for the data storage and computation efficiency. To solve this problem

a dimension-reduced clustering method for daily load profiles is proposed based on singular value decomposition. Firstly

daily load profiles are transformed into a new coordinate system by singular value decomposition

and the singular values reflect the importance of different axes. Then

the dimension-reduced index is defined as the projected values of load profiles on axes to represent the main features of load profiles. The number of dimension-reduced indices is determined by the decreasing trend of singular values. Finally

the singular values are selected as the weight of corresponding axes and the weighted K-means algorithm is adopted to classify daily load profiles. Cases studies verify that the proposed method has the advantages of short running time

good robustness and better classification results.