• Energy Research

Abstract Inverse modeling techniques are often used to predict the performance and energy use of buildings. Residential energy use is generally highly dependent on occupant behavior; this can limit a model's accuracy due to the presence of outliers. There has been limited data available to determine the cause of and evaluate the impact of such outliers on model performance, and thus limited guidance on how best to address this in model development. Thus the main objective of this work is to link the use of outlier detection methods to the causes of anomalies in energy use data, and to the determination of whether or not to remove an identified outlier to improve an inverse model's performance. A dataset of 128 U.S. residential buildings with highly-granular, disaggregated energy data is investigated. Using monthly data, change-point modeling was determined to be the best method to predict consumption. Three methods then are used to identify outliers in the data, and the cause and impact of these outliers is evaluated. Approximately 19% of the homes had an outlier. Using the disaggregate data, the causes were found to mostly be due to variations in occupant-dependent use of large appliances, lighting, and electronics. In 20% of homes with outliers, the removal of the outlier improved model performance, in particular all outliers identified with both the standard deviation and quartile methods, or all three methods. These two combinations of outlier detection methods are thus recommended for use in improving the prediction capabilities of inverse change point models.

Abstract Inverse modeling techniques are often used to predict the performance and energy use of buildings. Residential energy use is generally highly dependent on occupant behavior; this can limit a model's accuracy due to the presence of outliers. There has been limited data available to determine the cause of and evaluate the impact of such outliers on model performance, and thus limited guidance on how best to address this in model development. Thus the main objective of this work is to link the use of outlier detection methods to the causes of anomalies in energy use data, and to the determination of whether or not to remove an identified outlier to improve an inverse model's performance. A dataset of 128 U.S. residential buildings with highly-granular, disaggregated energy data is investigated. Using monthly data, change-point modeling was determined to be the best method to predict consumption. Three methods then are used to identify outliers in the data, and the cause and impact of these outliers is evaluated. Approximately 19% of the homes had an outlier. Using the disaggregate data, the causes were found to mostly be due to variations in occupant-dependent use of large appliances, lighting, and electronics. In 20% of homes with outliers, the removal of the outlier improved model performance, in particular all outliers identified with both the standard deviation and quartile methods, or all three methods. These two combinations of outlier detection methods are thus recommended for use in improving the prediction capabilities of inverse change point models.

Abstract Transportation infrastructure and operations are greatly impacted by ice and snow, adding enormous costs to the American economy. Because of their sustainability benefits, heated-pavement systems (HPS) continue to gain attention as a potential alternative to conventional snow removal operations, and the main goal of this paper is to assess the economic feasibility of hydronically-heated pavements systems (HHPS), one type of heated pavements, for use at apron areas of commercial airports. Both benefits and expenses associated with use of HHPS for snow and ice removal were identified and quantified in monetary terms using a stochastic economic analysis method, and a sensitivity analysis approach was used to determine particular variables that significantly influence overall economic viability of HHPS. The findings suggest that, despite high capital costs, HHPS use at airports might be economically feasible. The results from the sensitivity analysis indicate that airport size, in the context of number of aircraft operations, strongly affects the benefit-cost ratio of HHPS use.

Abstract Transportation infrastructure and operations are greatly impacted by ice and snow, adding enormous costs to the American economy. Because of their sustainability benefits, heated-pavement systems (HPS) continue to gain attention as a potential alternative to conventional snow removal operations, and the main goal of this paper is to assess the economic feasibility of hydronically-heated pavements systems (HHPS), one type of heated pavements, for use at apron areas of commercial airports. Both benefits and expenses associated with use of HHPS for snow and ice removal were identified and quantified in monetary terms using a stochastic economic analysis method, and a sensitivity analysis approach was used to determine particular variables that significantly influence overall economic viability of HHPS. The findings suggest that, despite high capital costs, HHPS use at airports might be economically feasible. The results from the sensitivity analysis indicate that airport size, in the context of number of aircraft operations, strongly affects the benefit-cost ratio of HHPS use.

Abstract One of the largest user of electricity in the average U.S. household is appliances, which when aggregated, account for approximately 30% of electricity used in the residential building sector. As influencing the time-of-use of energy becomes increasingly important to control the stress on today's electrical grid infrastructure, understanding when appliances use energy and what causes variation in their use are of great importance. However, there is limited appliance-specific data available to understand their use patterns. This study provides daily energy use profiles of four major household appliances: refrigerator, clothes washer, clothes dryer, and dishwasher, through analyzing disaggregated energy use data collected for 40 single family homes in Austin, TX. The results show that when compared to those assumed in current energy simulation software for residential buildings, the averaged appliance load profiles have similar daily distributions. Refrigerators showed the most constant and consistent use. However, the three user-dependent appliances, appliances which depend on users to initiate use, varied more greatly between houses and by time-of-day. During peak use times, on weekends, and in homes with household members working at home, the daily use profiles of appliances were less consistent.

Abstract One of the largest user of electricity in the average U.S. household is appliances, which when aggregated, account for approximately 30% of electricity used in the residential building sector. As influencing the time-of-use of energy becomes increasingly important to control the stress on today's electrical grid infrastructure, understanding when appliances use energy and what causes variation in their use are of great importance. However, there is limited appliance-specific data available to understand their use patterns. This study provides daily energy use profiles of four major household appliances: refrigerator, clothes washer, clothes dryer, and dishwasher, through analyzing disaggregated energy use data collected for 40 single family homes in Austin, TX. The results show that when compared to those assumed in current energy simulation software for residential buildings, the averaged appliance load profiles have similar daily distributions. Refrigerators showed the most constant and consistent use. However, the three user-dependent appliances, appliances which depend on users to initiate use, varied more greatly between houses and by time-of-day. During peak use times, on weekends, and in homes with household members working at home, the daily use profiles of appliances were less consistent.

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.