Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data

Kwonsik Song, Kyle Anderson, SangHyun Lee, Kaitlin T. Raimi and P. Sol Hart
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Kwonsik Song: Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Kyle Anderson: Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
SangHyun Lee: Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Kaitlin T. Raimi: Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor, MI 48109, USA
P. Sol Hart: Department of Communication and Media, Program in the Environment, University of Michigan, Ann Arbor, MI 48109, USA

Energies, 2020, vol. 13, issue 14, 1-21

Abstract: Within residences, normative messaging interventions have been gaining interest as a cost-effective way to promote energy-saving behaviors. Behavioral reference groups are one important factor in determining the effectiveness of normative messages. More personally relevant and meaningful groups are likely to promote behavior change. Using readily available energy-use profiles in a non-invasive manner permits the creation of highly personalized reference groups. Unfortunately, how data granularity (e.g., minute and hour) and aggregation (e.g., one week and one month) affect the performance of energy profile-based reference group categorization is not well understood. This research evaluates reference group categorization performance across different levels of data granularity and aggregation. We conduct a clustering analysis using one-year of energy use data from 2248 households in Holland, Michigan USA. The clustering analysis reveals that using six-hour intervals results in more personalized energy profile-based reference groups compared to using more granular data (e.g., 15 min). This also minimizes computational burdens. Further, aggregating energy-use data over all days of twelve weeks increases the group similarity compared to less aggregated data (e.g., weekdays of twelve weeks). The proposed categorization framework enables interveners to create personalized and scalable normative feedback messages.

Keywords: household energy consumption; behavior change; normative feedback; behavioral reference group; smart meter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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