Version 1
: Received: 24 June 2023 / Approved: 27 June 2023 / Online: 28 June 2023 (05:40:20 CEST)
Version 2
: Received: 13 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:06:15 CEST)
How to cite:
Kishore, A.; Li, B.; Atweh, J. Modeling Social Equity in Energy Consumption Using Digital Twins. Preprints2023, 2023061890. https://doi.org/10.20944/preprints202306.1890.v1
Kishore, A.; Li, B.; Atweh, J. Modeling Social Equity in Energy Consumption Using Digital Twins. Preprints 2023, 2023061890. https://doi.org/10.20944/preprints202306.1890.v1
Kishore, A.; Li, B.; Atweh, J. Modeling Social Equity in Energy Consumption Using Digital Twins. Preprints2023, 2023061890. https://doi.org/10.20944/preprints202306.1890.v1
APA Style
Kishore, A., Li, B., & Atweh, J. (2023). Modeling Social Equity in Energy Consumption Using Digital Twins. Preprints. https://doi.org/10.20944/preprints202306.1890.v1
Chicago/Turabian Style
Kishore, A., Beatrice Li and Jad Atweh. 2023 "Modeling Social Equity in Energy Consumption Using Digital Twins" Preprints. https://doi.org/10.20944/preprints202306.1890.v1
Abstract
This research examines the impact of social equity on energy consumption. We constructed a digital twin for residential energy consumption by enriching the synthetic population with real-world surveys and feeding them with other environmental and appliance data to the energy modeling framework. We analyzed household hourly energy consumption data from Albemarle County and Charlottesville City in Virginia, USA, for the year 2019. We used clustering analysis to identify patterns in social equity and energy consumption. The results demonstrated the impact of different residential attributes on energy poverty. Statistical analyses, including ANOVA and Chi-Squared tests, were conducted to test for significant differences between racial groups in quantitative and categorical variables. The study found that race is significant in determining the location and quality of housing. People of color often live in areas with higher pollution and less access to green spaces. Additionally, income levels and the age of the house are influential factors in determining energy efficiency. Future work should focus on collecting and analyzing data at the country level and using qualitative data collection methods to gain a more comprehensive understanding of social equity issues concerning energy consumption. Overall, this study provides valuable insights into the relationship between different residential attributes and energy consumption, which can inform policy development to promote more equitable and sustainable communities.
Keywords
Social Equity; Residential Energy Modeling; Digital Twins; Engineering
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
