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.
Highlights
In the United States (US), the residential sector consumes approximately 21% of total energy and generates 19% of all carbon dioxide (CO2 ) emissions [1]
A clustering clustering analysis analysis is is performed performed using using the the collected collected data data in in conjunction conjunction with with the the proposed proposed categorizationframework framework investigate the effect data granularity and aggregation on the categorization to to investigate the effect of dataofgranularity and aggregation on the behavioral behavioral reference group categorization
The creation of meaningful personalized reference groups based on similar behavioral patterns of households can lead to increases in social norm adherence and improvements in normative messaging intervention effectiveness
Summary
In the United States (US), the residential sector consumes approximately 21% of total energy and generates 19% of all carbon dioxide (CO2 ) emissions [1]. Residential energy consumption is significantly affected by occupant behaviors within their homes [2,3]. Numerous behavioral intervention methods (e.g., education campaigns, goal setting interventions, and energy-saving incentives) aimed at improving occupant energy use behavior have been studied. An increasing amount of research has investigated normative messaging interventions, as these interventions have been found to reduce overall household energy consumption and are very cost-effective given its implementation cost (3.3 cents per kWh of electricity saved) [4,5,6,7]. Normative messaging interventions typically provide households with information about their own energy consumption as well as information about the mean or median energy use of other similar households.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
