# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 8 Conclusions  
In this article, we have proposed a power profiling model based on power consumption data available in smart grids. This model consists of four stages, namely data extraction, load data analysis, load data clustering, and power profile assignment. Power consumption data are obtained from smart meters and sampled, processed, and stored in real time. Power consumption patterns are determined by analyzing the similarities between the consumer's power usage time series. The consumer's power consumption is clustered into two power load types, namely workday and weekend. By recognizing the power consumption patterns and matching them to a load type, a power profile is assigned to the consumer, enabling the identification of any demand changes or abnormalities. To train and evaluate the introduced power profiling model, we used a real-world dataset of electrical consumption from AMPds2, which includes real load data of various electrical appliances. The power consumption of the refrigerator in this dataset was extracted and analyzed. Power consumption is profiled by comparing the time series of the measured power load data and observing the consumption patterns in both short-term (one-day) and long-term observations. Any new power consumption data can be clustered based on the load factor and the similarities between its data series and the power load types measured by the DTW system. Our evaluation shows 77.17% clustering correctness, leading to accurate power profiles. In the future, we will improve the accuracy of the profiling by introducing more granular categories (e.g., holidays and other significant time-based distinctions) and increasing the size of the household, the sampling rate, and including more electric appliances. Future work will include testing with datasets from diverse regions to assess generalizability. The results will be generalized to other time-series datasets, where precise behavior profiling is necessary.  
Conceptualization, M.K., M.D.F. and H.K.; methodology, M.K.; software, M.D.F.; validation, M.K., M.D.F. and H.K.; formal analysis, M.K. and M.D.F.; investigation, M.K. and M.D.F.; resources, M.K.; data curation, M.D.F.; writing--original draft preparation, M.K. and M.D.F.; writing--review and editing, M.K., M.D.F., H.K. and M.E.-H.; visualization, M.K. and M.D.F.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00451909), and by the MSIT under the Global Research Support Program in the Digital Field (RS-2024-00419073), supervised by the IITP.  
The data used and analyzed in this study are openly available in AMPds dataset at [[https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/MXB7](https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/MXB7)]([https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/MXB7](https://dataverse.harvard.edu/file.xhtml?persistentId=doi:10.7910/DVN/MXB7)) VO/CBHFN9&version=1.1 accessed on 20 January 2025.  
The authors declare no conflicts of interest.