Normative messaging interventions have proven to be a cost-effective strategy for promoting pro-environmental behaviors. The effectiveness of normative messages is partially determined by how personally relevant the comparison groups are as well as the lag of feedback. Using readily available energy use data has created opportunities to generate highly personalized reference groups based on households’ behavioral patterns. Unfortunately, it is not well understood how data granularity (e.g., minute, hour) affects the performance of behavioral reference group categorization. This is important because different levels of data granularity can produce conflicting results in terms of group similarity and vary in computational time. Therefore, this research aims to evaluate the performance of clustering methods across different levels of temporal granularity of energy use data. A clustering analysis is conducted using one-year of energy use data from 3,000 households in Holland, Michigan. The clustering results show that behavioral reference groups become the most similar when representing households’ energy use behaviors at a six-hour interval. Computationally, less granular data (i.e., six and twelve hours) takes less time than highly granular data which increases exponentially with more households. Considering the enormous scale that normative messaging interventions need to be applied at, using less granular data (six-hour intervals) will permit interveners to maximize the effectiveness of highly personalized normative feedback messages while minimizing computation burdens.
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Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data
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.
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- Award ID(s):
- 1705273
- NSF-PAR ID:
- 10175382
- Date Published:
- Journal Name:
- Energies
- Volume:
- 13
- Issue:
- 14
- ISSN:
- 1996-1073
- Page Range / eLocation ID:
- 3678
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/en13143678
