English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Residential customers have traditionally not been treated as individualentities due to the high volatility in residential consumption patterns as wellas a historic focus on aggregated loads from the utility and system feederperspective. Large-scale deployment of smart meters has motivated increasingstudies to explore disaggregated daily load patterns, which can revealimportant heterogeneity across different time scales, weather conditions, aswell as within and across individual households. This paper aims to shed lighton the mechanisms by which electricity consumption patterns exhibit variabilityand the different constraints that may affect demand-response (DR) flexibility.We systematically evaluate the relationship between daily time-of-use patternsand their variability to external and internal influencing factors, includingtime scales of interest, meteorological conditions, and householdcharacteristics by application of an improved version of the adaptive K-meansclustering method to profile "household-days" of a summer peaking utility. Wefind that for this summer-peaking utility, outdoor temperature is the mostimportant external driver of the load shape variability relative to seasonalityand day-of-week. The top three consumption patterns represent approximately 50%of usage on the highest temperature days. The variability in summer load shapesacross customers can be explained by the responsiveness of the households tooutside temperature. Our results suggest that depending on the influencingfactors, not all the consumption variability can be readily translated toconsumption flexibility. Such information needs to be further explored insegmenting customers for better program targeting and tailoring to meet theneeds of the rapidly evolving electricity grid.

Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data