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Inequality in the availability of residential air conditioning across 115 US metropolitan areas
Continued climate change is increasing the frequency, severity, and duration of populations’ high temperature exposures. Indoor cooling is a key adaptation, especially in urban areas, where heat extremes are intensified—the urban heat island effect (UHI)—making residential air conditioning (AC) availability critical to protecting human health. In the United States, the differences in residential AC prevalence from one metropolitan area to another is well understood, but its intra-urban variation is poorly characterized, obscuring neighborhood-scale variability in populations’ heat vulnerability and adaptive capacity. We address this gap by constructing empirically derived probabilities of residential AC for 45,995 census tracts across 115 metropolitan areas. Within cities, AC is unequally distributed, with census tracts in the urban “core” exhibiting systematically lower prevalence than their suburban counterparts. Moreover, this disparity correlates strongly with multiple indicators of social vulnerability and summer daytime surface UHI intensity, highlighting the challenges that vulnerable urban populations face in adapting to climate-change driven heat stress amplification.
Automated fault detection of residential air-conditioning systems using thermostat drive cycles
Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.
Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems
The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.
Development of a solar-powered residential air conditioner
The initial objective of the program was the optimization (in terms of cost and performance) of a Rankine cycle mechanical refrigeration system which utilizes thermal energy from a flat solar collector for air conditioning residential buildings. However, feasibility investigations of the adsorption process revealed that a dessicant-type air conditioner offers many significant advantages. As a result, limited efforts were expended toward the optimization of such a system.
Emergency department visits in California associated with wildfire PM 2.5 : differing risk across individuals and communities
The threats to human health from wildfires and wildfire smoke (WFS) in the United States (US) are increasing due to continued climate change. A growing body of literature has documented important adverse health effects of WFS exposure, but there is insufficient evidence regarding how risk related to WFS exposure varies across individual or community level characteristics. To address this evidence gap, we utilized a large nationwide database of healthcare utilization claims for emergency department (ED) visits in California across multiple wildfire seasons (May through November, 2012–2019) and quantified the health impacts of fine particulate matter <2.5 μm (PM 2.5 ) air pollution attributable to WFS, overall and among subgroups of the population. We aggregated daily counts of ED visits to the level of the Zip Code Tabulation Area (ZCTA) and used a time-stratified case-crossover design and distributed lag non-linear models to estimate the association between WFS and relative risk of ED visits. We further assessed how the association with WFS varied across subgroups defined by age, race, social vulnerability, and residential air conditioning (AC) prevalence. Over a 7 day period, PM 2.5 from WFS was associated with elevated risk of ED visits for all causes (1.04% (0.32%, 1.71%)), non-accidental causes (2.93% (2.16%, 3.70%)), and respiratory disease (15.17% (12.86%, 17.52%)), but not with ED visits for cardiovascular diseases (1.06% (–1.88%, 4.08%)). Analysis across subgroups revealed potential differences in susceptibility by age, race, and AC prevalence, but not across subgroups defined by ZCTA-level Social Vulnerability Index scores. These results suggest that PM 2.5 from WFS is associated with higher rates of all cause, non-accidental, and respiratory ED visits with important heterogeneity across certain subgroups. Notably, lower availability of residential AC was associated with higher health risks related to wildfire activity.
Smart Vent System (SYSM 562 Final Project_Guest and Fleurantin)
Current residential air conditioning (AC) and heating units do a decent job at providing overall cooling/heating to a household. However, they usually fall short on accurately controlling which rooms to be heated and cooled. Most homes also require you to manually open or close a vent which can be very inconvenient if in a hard-to-reach area. There is a need for a system capable of automatically measuring and maintaining the thermal environment of each room throughout a house to meet a user’s preferences.
Life and stability testing of packaged low-cost energy storage materials
Thermal cycling and performance tests, performed to verify the package integrity, life, and stability of the chub packaged materials system for storage coolness with application to residential air conditioning, are described. The moisture vapor retention characteristics of the laminate film for long term chub performance was determined. The stability, mechanical integrity, and thermal performance of chubs following mechanical shock, vibration, and temperature extremes is reported.
Air-conditioning adoption and electricity demand highlight climate change mitigation–adaptation tradeoffs
Abstract We elucidate mid-century climate change impacts on electricity demand accounting for endogenous adoption of residential air-conditioning (AC) in affluent, cooler countries in Europe, and in poorer, hotter states in India. By 2050, in a high-warming scenario (SSP585) AC prevalence grows twofold in Europe and fourfold in India, reaching around 40% in both regions. We document a mitigation-adaptation tradeoff: AC expansion reduces daily heat exposures by 150 million and 3.8 billion person degree-days (PDDs), but increases annual electricity demand by 34 TWh and 168 TWh in Europe and India, respectively (corresponding to 2% and 15% of today’s consumption). The increase in adoption and use of AC would result in an additional 130 MMTCO2, of which 120 MMTCO2 in India alone, if the additional electricity generated were produced with today’s power mix. The tradeoff varies geographically and across income groups: a one PDD reduction in heat exposure in Europe versus India necessitates five times more electricity (0.53 kWh vs 0.1 kWh) and two times more emissions (0.16 kgCO $$_2$$ 2 vs 0.09 kgCO $$_2$$ 2 ), on average. The decomposition of demand drivers offers important insights on how such tradeoff can be moderated through policies promoting technology-based and behavioral-based adaptation strategies.
Refrigerants for a Sustainable Future
Worldwide use of high global warming potential (GWP) hydrofluorocarbon (HFC) refrigerants for space conditioning and food storage results in significant equivalent greenhouse gas (GHG) emissions. This is further exacerbated in developed countries by the current transition from hydrochlorofluorocarbon (HCFC) refrigerants to HFC refrigerants. Under the Kigali amendment to the Montreal Protocol, the proposed phase-out of currently used HFC and HCFC refrigerants has initiated a re-evaluation of some pre-existing refrigerants as well as the development and evaluation of new refrigerants. Making the ideal refrigerant selections for heating, ventilation, air-conditioning, and refrigeration (HVAC&R) applications is thereby difficult in an already overabundant refrigerants market. In this paper, a study of key parameters required of a good refrigerant is conducted, followed by the analysis of refrigerants desired and refrigerants used in two major sectors of the HVAC&R industry, namely commercial refrigeration and residential air-conditioning and heat pumps. Finally, keeping in consideration the global environmental regulations and safety standards, a recommendation of the most suitable refrigerants in both sectors has been made.
A Real-time Agent Based Optimization and Control Approach for Residential Building Heating Ventilation and Air Conditioning Systems
The prevalence of the loT (Internet of Things) is fostering the development of new control options and decision making that was not previously available. This is creating a wealth of opportunities for real-time control approaches and systems that can optimize for a common goal. This paper presents a smart residential neighborhood with optimization at the residential level utilizing a system of agents. The optimization utilizes information and modeling to optimize variable speed HVAC real-time operation in actual residential buildings. Data is presented showing the performance of the optimization and conclusions are drawn on next steps for development.
Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control
This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.
Evaluation of hybrid evaporative-vapor compression air conditioners for different global climates
Currently, a majority of global residential air-conditioning requirements are met using cooling systems based on the vapor compression cycle (VCC). To meet the future demand for space cooling and to reduce the corresponding environmental impact, there is a need for alternative cooling systems which require less resources and exhibit a higher coefficient of performance (COP). One of proposed alternatives is called the hybrid evaporative-vapor compression (HEVC) cycle, which utilizes adiabatic latent cooling in combination with the VCC. In this paper, we conducted a feasibility analysis of a HEVC system by comparing its performance to that of VCC for various global climates. To accomplish this, we developed and validated models to simulate the performance of residential VCC and HEVC systems. ASHRAE weather data and design conditions were then used to compare the performance of the two systems. In general, HEVC systems are best suited for hot arid climates with energy reduction greater than 20%. Conversely, humid climates are not suitable for HEVC adoption due to degraded performance of evaporative coolers in these climates. In addition to comparing the energy savings from HEVC systems, it is also critical to analyze their water consumption. Generally, climates that benefit the most from HEVC technology tend to also experience water scarcity issues. Thus, both the energy savings and water consumption of HEVC systems must be analyzed to guide the discussion on their adoption.
Bulk Electric Power System Risks From Coordinated Edge Devices
Not Available
FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration
Grid-forming inverters must optimally transfer power from dc-coupled photovoltaic arrays and batteries into an ac grid. Further, they must be able to restore single-phase induction motors (SPIMs) and withstand fault-induced delayed voltage-recovery (FIDVR) events. These resilience and reliability challenges are addressed here by: (i) engineering a controller to optimally operate dc-coupled hybrid resources; (ii) modeling residential air-conditioning compressors for restoration/FIDVR studies; and (iii) analyzing SPIM thermal-relay performance under limited inverter currents and designing an electronic protection for stalled SPIMs. Furthermore, these contributions are demonstrated via electromagnetic-transient simulations and can be helpful to understand recommendations by the North American Electric Reliability Corporation.
Development of a solar-powered residential air conditioner: Economic analysis
The results of investigations aimed at the development of cost models to be used in the economic assessment of Rankine-powered air conditioning systems for residential application are summarized. The rationale used in the development of the cost model was to: (1) collect cost data on complete systems and on the major equipment used in these systems; (2) reduce these data and establish relationships between cost and other engineering parameters such as weight, size, power level, etc; and (3) derive simple correlations from which cost-to-the-user can be calculated from performance requirements. The equipment considered in the survey included heat exchangers, fans, motors, and turbocompressors. This kind of hardware represents more than 2/3 of the total cost of conventional air conditioners.
Heat pipe systems using new working fluids
The performance of a heat pipe system is greatly improved by the use of a dilute aqueous solution of about 0.0005 and about 0.005 moles per liter of a long chain alcohol as the working fluid. The surface tension-temperature gradient of the long-chain alcohol solutions turns positive as the temperature exceeds a certain value, for example about 40.degree. C. for n-heptanol solutions. Consequently, the Marangoni effect does not impede, but rather aids in bubble departure from the heating surface. Thus, the bubble size at departure is substantially reduced at higher frequencies and, therefore, increases the boiling limit of heat pipes. This feature is useful in microgravity conditions. In addition to microgravity applications, the heat pipe system may be used for commercial, residential and vehicular air conditioning systems, micro heat pipes for electronic devices, refrigeration and heat exchangers, and chemistry and cryogenics.