Investigating Underlying Drivers of Variability in Residential Energy
Usage Patterns with Daily Load Shape Clustering of Smart Meter Data
- URL: http://arxiv.org/abs/2102.11027v1
- Date: Tue, 16 Feb 2021 16:56:27 GMT
- Title: Investigating Underlying Drivers of Variability in Residential Energy
Usage Patterns with Daily Load Shape Clustering of Smart Meter Data
- Authors: Ling Jin, C. Anna Spurlock, Sam Borgeson, Alina Lazar, Daniel Fredman,
Annika Todd, Alexander Sim, Kesheng Wu
- Abstract summary: Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns.
This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
- Score: 53.51471969978107
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Residential customers have traditionally not been treated as individual
entities due to the high volatility in residential consumption patterns as well
as a historic focus on aggregated loads from the utility and system feeder
perspective. Large-scale deployment of smart meters has motivated increasing
studies to explore disaggregated daily load patterns, which can reveal
important heterogeneity across different time scales, weather conditions, as
well as within and across individual households. This paper aims to shed light
on the mechanisms by which electricity consumption patterns exhibit variability
and the different constraints that may affect demand-response (DR) flexibility.
We systematically evaluate the relationship between daily time-of-use patterns
and their variability to external and internal influencing factors, including
time scales of interest, meteorological conditions, and household
characteristics by application of an improved version of the adaptive K-means
clustering method to profile "household-days" of a summer peaking utility. We
find that for this summer-peaking utility, outdoor temperature is the most
important external driver of the load shape variability relative to seasonality
and day-of-week. The top three consumption patterns represent approximately 50%
of usage on the highest temperature days. The variability in summer load shapes
across customers can be explained by the responsiveness of the households to
outside temperature. Our results suggest that depending on the influencing
factors, not all the consumption variability can be readily translated to
consumption flexibility. Such information needs to be further explored in
segmenting customers for better program targeting and tailoring to meet the
needs of the rapidly evolving electricity grid.
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