A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile

Research output: NRELPresentation


Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation and the results have proved the effectiveness of this method.
Original languageAmerican English
Number of pages7
StatePublished - 2023

Publication series

NamePresented at the 2023 IEEE Power & Energy Society General Meeting, 16-20 July 2023, Orlando, Florida

NREL Publication Number

  • NREL/PR-5D00-86914


  • AMI
  • energy consumption
  • load profile cluster
  • load shape


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