@article{34719,
  keywords = {Building energy modeling, Keywords: Building electrical load profile, Smart meter, Time-domain analysis, Frequency-domain analysis, Load variability},
  author = {Han Li and Zhe Wang and Tianzhen Hong and Andrew Parker and Monica Neukomm},
  title = {Characterizing patterns and variability of building electric load profiles in time and frequency domains},
  abstract = {<p>The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.</p>},
  year = {2021},
  journal = {Applied Energy},
  volume = {291},
  pages = {116721},
  month = {06/2021},
  issn = {03062619},
  doi = {10.1016/j.apenergy.2021.116721},
  language = {eng},
}