Estimation of residential space conditioning parameters using smart electricity meter data

Changes in climate and energy technologies motivate a greater understanding of how electricity is accessed and used in the event of extreme weather conditions. Consumption patterns from a testbed of smart electricity meter data were characterized in the context of identifying demand for space conditioning to evaluate vulnerability and livability in cities. In particular, methods in existing literature for inferring the presence of air conditioning or space heating from residential electricity consumption and weather data were reviewed for their applicability to a new urban dataset. This exploration reveals challenges in adapting methodologies to datasets with different scopes and locations. At the same time, these issues can serve to inform future data collection strategies and present opportunities for further study towards a unification of metering infrastructure analytics incorporating factors such as geography, demographics, and building characteristics. A metric suitable for the residential classification task is proposed,which summarizes a household's consumption profile by describing one aspect of its relation between electricity demand and temperature. The proportion of electricity consumed within a selected range of low temperatures is found to be a productive feature for identifying electric primary space heating in a smart meter dataset of single-family Chicago residences. Although the limitations of the dataset restrict possible approaches, this basic experiment suggests that this feature has advantages that can be adapted to study other datasets beyond the identification of space heating. The possible benefits of analysis using the full extent of the available temporal resolution rather than the established method of aggregating daily measurements are investigated. An initial comparison does not reveal a definitive advantage in classification performance but suggests that further work is required to characterize the contribution of smart meter resolution for specific methodologies and algorithms.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Lee, Christopher Seunghwan

Contributors dc:contributor

• Zhao, Zhizhen
• Stillwell, Ashlynn S

• Residential electricity consumption
• Smart meter data
• Space conditioning
• Classification
• Regression

Statement dc:rights

• Copyright 2022 Christopher Lee

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/116286

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F116286