Power consumption data play a crucial role in demand management and abnormality detection in smart grids. Despite its management benefits, analyzing power consumption data leads to profiling consumers and opens privacy issues. To demonstrate this, we present a power profiling model for smart grid consumers based on real-time load data acquired from smart meters. It profiles consumers’ power consumption behavior by applying the daily load factor and the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features can be extracted. By this model, two load types are defined and the related load patterns are extracted for classifying consumption behavior by DTW. The classification methodology is discussed in detail. To evaluate the performance of the proposed model for profiling, we analyze the time-series load data measured by a smart meter in a real case. The results demonstrate the effectiveness of the proposed profiling method, achieving an F-score of $0.8372$ for load type clustering in the best case and an overall accuracy of $77.17\%$ for power profiling.