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1. Unveiling hidden energy poverty using the energy equity gap

   https://doi.org/10.1038/s41467-022-30146-5  

   Cong, Shuchen; Nock, Destenie; Qiu, Yueming Lucy; Xing, Bo (May 2022, Nature Communications)

   Abstract Income-based energy poverty metrics ignore people’s behavior patterns, particularly reducing energy consumption to limit financial stress. We investigate energy-limiting behavior in low-income households using a residential electricity consumption dataset. We first determine the outdoor temperature at which households start using cooling systems, the inflection temperature. Our relative energy poverty metric, theenergy equity gap, is defined as the difference in the inflection temperatures between low and high-income groups. In our study region, we estimate the energy equity gap to be between 4.7–7.5 °F (2.6–4.2 °C). Within a sample of 4577 households, we found 86 energy-poor and 214 energy-insecure households. In contrast, the income-based energy poverty metric, energy burden (10% threshold), identified 141 households as energy-insecure. Only three households overlap between our energy equity gap and the income-based measure. Thus, the energy equity gap reveals a hidden but complementary aspect of energy poverty and insecurity. 

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2. Residents’ Perceptions of Behavioral Reference Groups for Personalized Normative Messaging Interventions

   Song, K. (November 2020, Construction Research Congress)

   Reporting normative feedback to residential energy consumers has been found effective at reducing residential energy consumption. Upon receiving normative feedback households tend to modify their use to become in line with group norms. The effect of normative messages is partially moderated by how personally relevant normative reference groups are to the individual. Advanced energy metering technologies capture households’ energy use patterns, making it possible to generate highly similar and relevant normative reference groups in a non-invasive manner. Unfortunately, it is not well understood how similar individuals are to other group members. It also remains unknown how much individuals identify with behavioral reference groups. Therefore, this research aims to investigate how households perceive behavioral reference groups used in normative comparisons. Survey questionnaires are collected from 2,008 participants using Amazon Mechanical Turk. It is found that while households’ behaviors are more similar when grouped based on energy use profiles than based on geographic proximity, they identify more closely with proximity-based groups. Also, members’ group identification increases as individuals have higher similarity in energy use behaviors with other group members. This implies that enhancing the identity of profile-based behavioral reference groups will lead to an increase in norm adherence, and in turn reductions in household energy use. 

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3. JUE: Insights: Powering work from home

   https://doi.org/10.1016/j.jue.2022.103474  

   Cicala, Steve (July 2022, Journal of urban economics)

   This paper documents a shift in energy consumption toward residential usage during the COVID-19 pandemic in the United States. Focusing on electricity, I find a 7.9% increase in residential consumption, and a 6.9% and 8.0% reduction in commercial and industrial usage, respectively, from a monthly panel of electric utilities. Natural gas consumption also shifted toward residential use, so that aggregate electricity and gas expenditure only fell by 1% on net during a period in which GDP fell by 5%. Hourly smart meter data from Texas reveal how daily routines changed during the pandemic, with residential electricity usage during weekdays closely resembling those of weekends. In total, residential energy expenditures were an estimated $13B higher during Q2-Q4 2020, with the largest increases occurring in areas with a greater propensity to work from home. I find that transportation fuel consumption declined about 16%, so that total energy consumption in the U.S. economy fell by 8%. 

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4. Sensitivity of Building Energy Model Predictions to Spatial Variations in Climate under Different Levels of Urbanization

   Jahani, Elham; Vanage, Soham; Cetin, Kristen; Gallus, William; Jahn, David (January 2020, ASHRAE Annual Virtual Conference 2020)

   null (Ed.)

   In the United States, approximately 40% of the primary energy use and 72% of the electricity use belong to the building sector. This shows the significance of studying the potential for reducing the building energy consumption and buildings’ sustainability for ensuring a sustainable development. Therefore, many different efforts focus on reducing the energy consumption of residential buildings. Data-validated building energy modeling methods are among the studies for such an effort, particularly, by enabling the identification of the potential savings associated with different potential retrofit strategies. However, there are many uncertainties that can impact the accuracy of such energy model results, one of which is the weather input data. In this study, to investigate the impact of spatial temperature variation on building energy consumption, six weather stations in an urban area with various urban density were selected. A validated energy model was developed using energy audit data and high-frequency electricity consumption of a residential building in Austin, TX. The energy consumption of the modeled building was compared using the selected six weather datasets. The results show that energy use of a building in an urban area can be impacted by up to 12% due to differences in urban density. This indicates the importance of weather data in predicting energy consumption of the building. The methodology and results of this study can be used by planners and decision makers to reduce uncertainties in estimating the building energy use in urban scale. 

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5. The Impact of Urban Heat Island on Calibrated Building Energy Model Predictions

   Jahani, Elham; Vanage, Soham; Cetin, Kristen (January 2020, ASCE Construction Research Congress)

   Among various elements of urban infrastructure, there is significant opportunity to improve existing buildings’ sustainability, considering that approximately 40% of the total primary energy consumption and 72% of electricity consumption in United States is consumed by the building sector. Many different efforts focus on reducing the energy consumption of residential buildings. Data-validated building energy modeling methods serve the role of supporting this effort, by enabling the identification of the potential savings associated with different potential retrofit strategies. However there are many uncertainties that can impact the accuracy of energy model results, one of which is the weather input data. Measured weather data inputs located at each building can help address this concern, however, weather station data collection for each building is also costly and typically not feasible. Some weather station data is already collected, however, these are generally located at airports rather than near buildings, and thus do not capture local, spatially-varying weather conditions which are documented to occur, particularly in urban areas. In this study we address the impact of spatial temperature differences on residential building energy use. An energy model was developed in EnergyPlus for a residential building located in Mueller neighborhood of Austin, TX, and was validated using actual hourly measured electricity consumption. Using the validated model, the impact of measured spatial temperature differences on building energy consumption were investigated using multiple weather stations located throughout the urban area with different urban fractions. The results indicate that energy consumption of a residential building in a city with a 10% higher urban fraction would increase by approximately 10%. This variation in energy consumption is likely due to the impact of UHI effects occurring in urban areas with high densities. 

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