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Office of Scientific and Technical Information
 

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

Conference ·
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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. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
2229750
Report Number(s):
NREL/CP-5D00-88286; MainId:89061; UUID:20c4e393-a5e4-4826-bcd2-6d613e862cd9; MainAdminID:71286
Resource Relation:
Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida; Related Information: 84634
Country of Publication:
United States
Language:
English

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Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
advanced metering infrastructure (AMI)
energy consumption
load profile cluster
load shape