Simulation Analytics for Social and Behavioral Modeling

Swarup, Samarth; Marathe, Achla; Marathe, Madhav V.; Barrett, Christopher L.

doi:10.1002/9781119485001.ch26

“…We begin by collecting the data for our analysis as in Figure 2. Here, we use the digital twin of the US population from the Network Systems Science and Advanced Computing (NSSAC) synthetic population [8]. The base population of the digital twin is developed using a 5% sample of complete records from Public Use Microdata Sample (PUMS) [11].…”

Section: A Data Extractionmentioning

confidence: 99%

“…The base population of the digital twin is developed using a 5% sample of complete records from Public Use Microdata Sample (PUMS) [11]. In this work [8], each person P from household H in a residential location RA is assigned an activity sequence A from the list of activities, where each activity has a type TA, start time s, duration d and activity location LA using two-stage fitted value method [5]. The activity model is constructed based on the National Household Travel Survey (NHTS) [12], the Multinational Time Use Study (MTUS) [6], and the American Time Use Survey (ATUS) [10].…”

Section: A Data Extractionmentioning

confidence: 99%

Modeling Social Equity in Energy Consumption Using Digital Twins

Kishore,

Li,

Atweh

2023

Preprint

0

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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.

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“…Next is the enrichment step for energy information, where we map the attributes from the Residential Energy consumption survey (RECS) [9] to the synthetic population. This survey consists of real samples representing the US population for household characteristics and attributes, appliance information, and other energy-related information.…”

Section: A Data Extractionmentioning

confidence: 99%

“…Many works have stressed the importance of retrofitting building stock to mitigate climate change, address energy poverty and inequity, and reach net-zero emissions [15]. Based on U.S. Energy Information Administration (EIA) [9], 1/3rd of the U.S. lives in energy poverty, and 11% keep home in unhealthy conditions just because they cannot afford to pay their electricity bills. Although we have federal policies and incentives for retrofitting, these numbers look scary.…”

Section: Introductionmentioning

confidence: 99%

Modeling Social Equity in Energy Consumption Using Digital Twins

Kishore¹,

Li²,

Atweh³

2023

Preprint

0

View full text Add to dashboard Cite

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.

show abstract