Residential Air Conditioning Research Articles

Air conditioning and global inequality

As global temperatures go up and incomes rise, air conditioner sales are poised to increase dramatically. Recent studies explore the potential economic and environmental impacts of this growth, but relatively little attention has been paid to the implications for inequality. In this paper we use household-level microdata from 16 countries to characterize empirically the relationship between climate, income, and residential air conditioning. We show that both current and future air conditioner usage is concentrated among high-income households. Not only do richer countries have much more air conditioning than poorer countries, but within countries adoption is highly concentrated among high-income households. The pattern of adoption is particularly stark in relatively low-income countries such as Pakistan, where we show that the vast majority of adoption between now and 2050 will be concentrated among the upper income tercile. We use our model to forecast future adoption, show how patterns vary across countries and income levels, and discuss what these patterns mean for health, productivity, and educational inequality.

Evidence-based assessment of energy performance of two large centrifugal chillers over nine cooling seasons

The change over time of energy performance of large chillers used in commercial and institutional buildings is seldom presented in the literature. Most studies are based on measurements of small capacities of residential air-conditioners and heat pumps. Authors found in the literature only three papers on this topic. This paper presents the evidence-based assessment of long-term performance of two identical large capacity chillers that serve the cooling system of a university campus. The paper presents the estimation of uncertainty of direct and derived measurements by using as example measurements recorded at 15 min time intervals by the building automation system during the summer of 2016. First, the paper verifies the hypothesis that weekly values of coefficient of performance (COP) of these large chillers do not change significantly over the summer of 2016. Second, the t-test also verifies the hypothesis of no significant change of seasonal COP values from 2009 to 2017. Third, a linear correlation-based model shows a variation of COP values by 1-2% per year between 2009 and 2017. The results show that the seasonal variation of COP is not statistically significant.

Residential Air Conditioning System Integrated with Packed Bed Cool Storage Unit for Promoting Rooftop Solar PV Power Generation

The increase in the share of renewable-based power in the gross power generation in most countries causes significant concerns over the addition of renewable power with the grid, results in stability issues in most developed nations. Energy storage is an emerging technology that is considered the ultimate solution in developing microgrids with distributed renewable power generation. The cool thermal storage plays a vital role in economically promoting renewable power among different storage units. The major objective of the research work is to demonstrate the integration of residential air-conditioning systems with packed bed cool storage units to promote rooftop solar power generation for residential space cooling applications. In order to achieve the said objective, an experimental investigation was made to study the charging/discharging characteristics of a packed bed cool-storage unit combined with a chiller and a cooling coil unit suitable for small capacity air-conditioning applications. The system consists of encapsulated spherical capsules filled with a phase change material blended with distilled water and pseudomonas (nucleating agent) and the heat transfer fluid as a combination of distilled water and Mono-ethylene glycol. A cooling coil unit was connected to the cool-storage tank to transfer cool energy from the storage tank to the space to be cooled when there is a demand. The important parameters, such as instantaneous and cumulative heat transfer during the charging/discharging processes, are presented. The average COP values of the chiller during the charging operation were estimated as 1, 0.93, and 0.89 when the HTF setpoint temperatures were -6°C, -9°C, and -12°C, which shows a decrease in performance as the setpoint temperature decreases. During the discharging process, a cooling load of 2.25 kW is obtained during the first cycle of operation and gradually reduces to 0.3 kW during the sixth cycle of operation. The increase in the HTF temperature during each cycle of operation indicates that the Phase Change Material (PCM) in the balls cannot release the heat as per the demand after a certain period of discharging. Hence, decreasing the internal thermal resistance by suitable measures is essential to achieve uniform heat flux and to operate the system successfully

Experimental evaluation on a centralized air conditioning system embedded with a split-type air conditioner as a dedicated subcooler

Dedicated subcooling (DS) is one of the alternative methods to gain significant performance improvement in the air conditioning (A/C) system. However, DS increases the investment cost and the performance improvement depends on the capacity ratio between the subcooler and the main A/C system (RSM). As compared to previous studies, the present study investigates experimentally the performance of the A/C system equipped with DS at smaller RSM, and justify the economic advantage of the proposed system. The experimental study was conducted at constant outdoor, evaporation, and condensation temperatures of 30, 5, and 40°C, respectively. The experimental facility consists of a main A/C system with a compressor capacity of 11.25 kW and R22 as a working fluid. The sub-system is a split-type A/C system with compressor capacity of 0.75 kW and R290 as a working fluid. Four parameters; the degree of subcooling, cooling capacity, input power, and coefficient of performance (COP) were investigated. The results showed that the DS decreased the condenser outlet temperature , thus led to an average increase in degree of subcooling by 3.4°C. Consequently, cooling capacity of the main system increased by 1.4 kW, and the input power of the entire system (power consumed by the main and subcooler systems) decreased by 0.4 kW. As a result, the COP raised by 7.8%, as compared to system without DS. In the economic point of view, the analysis showed that the system with DS is economically viable and has a huge potential due to short payback period of just 11.81 months. • Residential air conditioner as a sub-system in producing dedicated subcooling. • The ratio on capacities of subcooler to main system determine the degree of subcooling of the main system. • The dedicated subcooling increases the cooling capacity and decreases the input power of the main system. • The COP increases when the power used by both the main and subcooler systems are considered. • Simple payback period of 11.81 months is expected for system with DS as compared to system without DS.

Design and analysis of waste heat recovery from residential air conditioning units for cooling and pure water production

Abstract: The present article reports on the analysis of waste heat and condensate recovery from residential air conditioning (AC) units in different climatic regions in Saudi Arabia. Simulation results obtained using a transient dynamic model show that significant amounts of waste heat and water condensate are rejected to the environment in hot and humid climate regions. A multi-generation system for simultaneous cooling and pure water production using condensate and waste heat recovery is designed and implemented in TRNSYS environment. A refrigerant to water de-superheater is introduced after the compressor to extract energy from the high temperature superheated refrigerant gas for heating saline water that is used as a feed to drive an air gap membrane desalination unit. The production of pure water is analyzed for both batch and continuous cooling modes of the membrane desalination unit. The amount of pure water production shows the potential of proposed system to supply pure water requirements in hot and humid climates in Saudi Arabia. The monthly average coefficient of performance of multi-generation residential AC units with continuous cooling of desalination unit is shown to be improved by ~6.61%. The monthly average gained output ratio of membrane desalination unit is higher than 1.14.

A thermal comfort-driven model predictive controller for residential split air conditioner

In the wake of rising cooling electricity demand in the residential building sector, there is a need to contemplate how the residential air conditioners (ACs) are operated. This study explores the residential split AC's capacity regarding its ability to provide thermal comfort to the occupants instead of just cooling through the implementation of thermal comfort-driven model predictive control (MPC). The proposed control technique is illustrated by implementing it in a real residential house. To integrate thermal comfort feedback into the controller while optimizing AC's set-point, a real-time co-simulation methodology has been developed, using EnergyPlus and Simulink. A data-driven model is used in the MPC framework to predict the set-point of AC, accounting for the environmental variables as measured disturbances and thermal comfort index, i.e., predictive mean vote (PMV) as a constraint on output. A Raspberry Pi-Arduino-based embedded system is developed to actuate the AC's set-point during real-time co-simulation. Experimental data is used to develop the numerical model of the system. The simulation study has been performed to observe the proposed control technique's value addition over the base cases. Simulation data shows that AC operated with the proposed control technique consumes (8.8–17.5%) less cooling energy compared to the base cases.

Analysis of decision-making for air conditioning users based on the discrete choice model

Because of the thermal inertia of air conditioning load, electric power companies can guide users to manage it effectively and reduce the peak-valley difference of power grid through the electricity price mechanism on the premise of less affecting the comfort of users. Although the load adjustable potential of a single air conditioner user is small, its aggregation potential cannot be ignored due to a large number of residential air conditioners. In order to accurately describe users' differential response decision behavior, an analysis method of air conditioning users' response decision behavior considering uncertainty and demand diversity response behavior is proposed in this paper. Firstly, according to the change in the indoor and outdoor environmental factors of air conditioning, the cooling load characteristics of residential buildings were modeled, the difference in the comfort temperature of the users was considered, the cooling load characteristics of various types of residential users were identified, and the daily load curves of the cooling demand of the users were obtained. Secondly, the uncertainty of the user response under the electricity price mechanism was analyzed, and the user preference for economy and comfort was comprehensively considered. Finally, the decision-making behavior of the users was analyzed with the discrete choice model, and the probability model of the differential responses of the users was simulated by the multi-objective optimization method. An example is given to analyze the actual response data of users in a residential district in Changzhou City, Jiangsu Province, and get the response of users to air conditioning load under the time-of-use electricity price and different incentive degrees. It provides more comprehensive user response decision information for the grid side, and enables the power company to improve the implementation effect of air conditioning load participating in demand response.

Life Cycle Optimization of Residential Air Conditioner Replacement

Life Cycle Optimization of Residential Air Conditioner Replacement

Life Cycle Assessment of Residential Air Conditioners Considering the Benefits of Their Use: A Case Study in Indonesia

The global demand for air conditioners (ACs) has more than tripled since 1990, with 1.6 billion units currently in use. With the rapid economic and population growth of countries located in the hottest parts in the world, this trend is likely to continue in the future. The aim of this study was to show the benefits of introducing air conditioners with different materials or different technologies such as inverters with high energy-saving performance on the environment and human health in Indonesia. To evaluate the environmental impacts of the different technologies, a cradle-to-grave life cycle assessment (LCA) of air conditioners was conducted using the life-cycle impact assessment method based on endpoint modeling (LIME3). As expected, the use stage has the largest global warming potential (GWP), representing more than 90% of the impact, whereas copper and nickel have the most important impact in terms of resource consumption (about 50%). We found that the impacts are heavily dependent on the country’s energy mix, but reduction can be achieved by introducing better technologies. The integration factors from LIME3 were then applied to estimate the external cost of each model; the results showed that the use stage also has the most influence. Even though the impact of climate change is important, air pollution impact must be seriously considered as its impact was found to be the highest (about 60% of the total impact). The external cost was finally compared to the possible benefits produced by the introduction of air conditioning technologies during their 10-year life cycle. We found that the impacts are twice that of the benefit for the best model (USD 2003 vs. 1064); however, the novelty of this study is that the benefit was also considered. In the future, developing countries should promote AC models with inverters, refrigerants with low global warming impact such as natural refrigerants, and encourage the recycling of units as soon as possible. The energy mix for electricity production is also a key parameter to consider.

Bulk Electric Power System Risks From Coordinated Edge Devices

As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a time-series deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time short-term voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on short-term voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

A Novel Approach for Direct Load Control of Residential Air Conditioners in Developing Regions

A Novel Approach for Direct Load Control of Residential Air Conditioners in Developing Regions

Optimal design of home energy management strategy based on refined load model

Based on the physical model of load and the control model of expressing user’s subjective use intention, refined modeling methods are proposed for three typical high-power controllable loads on residential side, namely, heating-ventilation and air conditioning (HVAC) system, electric water heater and charging load of electric vehicle (EV) and the refined models are carried out. First of all, a custom family example is constructed, including the user’s electricity habits, structural characteristics of the house and the load, and the simulation is carried out. The refined definition of technical parameters and the influence of typical and atypical electricity consumption process on load electricity consumption effect are analyzed in detail. Then, an optimization model of energy management strategy aiming at optimizing electricity consumption cost is established based on traditional saving families and modern comfortable families. Simulation results show that the user personalized electricity consumption habits and environment have a significant impact on the control strategy of home energy management system (HEMS).

Peak shaving potential analysis of distributed load virtual power plants

As an important demand-side management method, distributed load participation in demand response provides an important means for auxiliary services such as peak-clipping and calming fluctuations of renewable power generation. Air conditioning load is usually viewed as a typical thermostatically related load, and is widely distributed in residential areas. Residential air conditioning load is a kind of demand response resource with broad application prospect. During the peak load period in summer, air conditioning load accounts for more than 40% of urban electricity consumption. Moreover, air conditioning load has certain flexibility and controllability. Reasonable evaluation of its peak clipping potential can effectively promote the safety and economic efficiency of the power grid. This paper proposes an analysis method of air conditioning load demand response potential combined with electricity consumption data and physical model. Subsequently, the switching machine and working state of air-conditioning load are identified, model parameters are calculated, and the dynamic adjustment potential of air-conditioning load in the future period is analyzed. In this paper, the data of electricity consumption in a province of China is used in the case study, and the algorithm is verified by examples.

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. The applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

Assessing the impact of filtration systems in indoor environments with effectiveness

Effectiveness is commonly used to evaluate the particle removal performance of residential filtration systems. Previous investigations have used various effectiveness metrics to assess system performance through modeling efforts. However, the model assumptions often lead to an overestimation of effectiveness. Further, none of the studies considered the impact of realistic residential heating, ventilation, and air conditioning (HVAC) system operation patterns. In this investigation, we evaluated the strength and limitations of various effectiveness metrics. We identified exposure reduction as the most appropriate metric for modeling analyses because it does not rely on any steady-state assumptions. A time-varying mass balance model that accounts for system operation parameters including runtimes and cycle on-times, and the impact of indoor emission sources is used to simulate the changes in indoor particulate matter smaller than 2.5 μm (PM2.5) concentration with and without system operation. The effectiveness results show that runtime has the biggest impact on exposure reduction. At a fixed runtime, short cycle on-times and the coincidence of system operation with emission events can further improve the removal performance of the system. Thus, the particle removal performance of a filtration system can be optimized by changing its operation pattern while still maintaining similar fan energy use.

Performance analysis of a solid desiccant cooling system for a residential air conditioning system

Desiccant cooling systems have been regarded as alternative residential air conditioning systems, owing to their potential for considerably reducing electricity power consumption. In particular, when they are combined with distributed power generations, the overall efficiency of the system can be significantly enhanced by utilizing the system exhaust heat for the adsorption of water from the solid desiccant. However, desiccant cooling systems have a limited cooling capacity and consume an extremely high amount of thermal energy. Hybrid desiccant cooling (HDC) systems can extend their cooling capacity to satisfy the cooling load on the hottest day in the summer season by combination with an electric heat pump (EHP). In this study, one-dimensional steady state models of desiccant cooling systems were developed using MATLAB-Simulink®. Three types of desiccant cooling system models, direct evaporative cooling (DEC), indirect evaporative cooling (IEC), and HDC systems, have been simulated, and their cooling performance under various temperatures ranges from 25 °C to 50 °C and various humidity conditions ranges from 4% to 98%, which represent the weather of summer seasons worldwide, have been compared. DEC system has enough cooling performance to satisfy the target cooling load only when the outdoor temperatures becomes lower than 35 °C. When the outdoor temperature becomes exceeds 40 °C, the total COP of the HDC system is significantly increased and becomes higher than that of the IEC system.

Thermodynamic analysis of low-GWP blends to replace R410A for residential building air conditioning applications.

The Kyoto Protocol has stipulated array of national policies to combat the climate change. To tackle the global warming, governments embraced Paris Agreement and Kigali Amendment which deal with the reduction of greenhouse gas emission. For example, the European F-gas regulation and the Japan METI now enforce refrigerants below 150 GWP for automobile industry and below 750 GWP for the residential air-conditioning applications. To invent a perfect refrigerant that meets performance requirement, environmental requirements, and safety standards is considered extremely difficult. On the other hand, some existing refrigerants exhibit excellent performance with safe operation but record high-GWP while refrigerants such as R1234yf and R744 possess almost 0 GWP. Thus, these refrigerant blends might serve as urgent solutions with minimum performance compromise. This paper evaluates the performance of binary and ternary blends using several promising refrigerants. Exploiting the excellent performance of R32 as the base refrigerant, R1123, R1234yf, R1234ze(E), and R744 are utilized in the blends. The performance indicators employed here are (i) GWP, (ii) temperature glide, (iii) volumetric capacity, and (iv) coefficient of performance. The advantages to reduce the irreversible heat loss by glide matching and energy saving potential for the blends are also discussed. Results showed that some refrigerant blends considering GWP 200 and 300 could successfully replace the widely used R410A in a residential air conditioner. Thermodynamic cycle and performance of zeotropic blend.

Local Pollution as a Determinant of Residential Electricity Demand

This study finds that a significant and hitherto ignored determinant of home energy demand is ambient particle pollution. I access longitudinal data for Singapore, a newly affluent Asian city-nation and arguably a harbinger of what is to come in the urbanizing tropics. Singapore today combines high (yet unequal) defensive capital stocks, such as residential air conditioning, with widely varying particle pollution. Overall, residential electricity demand grows by 1.1% when PM2.5 rises by 10 µg/m3. I compare the pollution-electricity response to the well-known heat-electricity response, and show how it varies over the socioeconomic distribution. Local pollution control has the cobenefit of reducing electricity generation, via lower household demand, and thus mitigating carbon emissions. The observed inequality in defensive expenditure may also exacerbate health inequalities, as suggested by an exchange between epidemiologists and government.

Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The analysis shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.