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[Display omitted] • The LCA methods and findings of 41 papers on air-conditioning systems are examined. • Retrieved studies presented discrepancies in the LCA goals and scope definition. • The global warming potential (GWP) was the most evaluated indicator across papers. • Grid-connected electrical heat pumps generally had a higher life cycle GWP. • Improvements related to AC energy sources and other life cycle aspects are proposed. Air conditioning (AC) has become the fastest-growing energy end-use in buildings worldwide, and its adoption is expected to increase further due to various socioeconomic factors and climate change. Despite this, the decarbonization of space cooling is progressing slowly; hence, the emissions burden at each stage should be examined in detail. Given this context, it is crucial to assess the environmental performance of AC systems by considering a comprehensive approach like the Life Cycle Assessment (LCA). While several studies have already targeted the LCA of AC systems, a clear overview of those studies' methodologies, assumptions, and results is still lacking. Therefore, this systematic review examined the methods and key findings of 41 peer-reviewed articles. Although the results revealed discrepancies among the goals and scope, impact assessment methods, and indicators, most articles predominantly focused on the global warming potential (GWP) impact, and its reported quantification was further analyzed. It was observed that grid-connected vapor-compression heat pumps/chillers (conventional systems) presented higher GWP impacts than other AC systems powered by renewables, which in contrast, exhibited higher impacts in some toxicity and resource depletion-related indicators. However, the share of GWP showed different distributions, as the pre-operation emissions of solar-powered AC systems (either PV or thermal) were higher than those of conventional systems, mainly driven by reduced energy consumption usage. This review concludes with some recommendations for each stage of the life cycle. Since the operation phase generally showed the highest impact, more efforts should be devoted to accelerating the transition to a 100% clean electricity power system at a national scale. In contrast, the need to power AC systems with on-site renewables should be carefully assessed based on the context. [ABSTRACT FROM AUTHOR]

This paper describes a method for residential water use modeling predicated on metered energy data. Actual measured hot water volumes for major indoor consumption are used to verify and adjust the outputs in gallons of hot water consumption based on climate variables, water heater technical features, and set-point and intake temperatures. Three independent datasets for residential energy (RECS 2009), water heater efficiency (Air-conditioning, Heating and Refrigeration Institute-AHRI), and end-use domestic water (Residential End Uses of Water, Version 2-REU II) are applied to identify specific demographic, built environment and geographic factors that relate patterns of energy demand to water consumption. The proposed model acts within the broader Integrated Urban Metabolism Analysis Tool (IUMAT), a system-based analytical framework for evaluating the environmental performance of the built environment. The method described in this paper offers an alternative approach to residential water consumption modeling by implementing volume of hot water consumption as a proxy for indoor water use. It provides utilities with the potential to parse and prioritize energy and water conservation measures. [ABSTRACT FROM AUTHOR]

This paper summarises the results of a study to identify and quantify the potential impact of measures and policies to reduce the energy consumption of air conditioning in European countries over a 10-year period. The market penetration of air conditioning in Europe has increased steadily over several decades. On a “business as usual” basis, its energy consumption would increase by over 50% by 2020. Measures to restrict this increase are therefore important, but the savings potential straddles several overlapping areas which are subject to different constraints including those imposed by the replacement rates of systems and products, and the refurbishment rates of existing buildings. This work reported in this paper is based on the use of information from a variety of sources in a highly disaggregated model to assess the “realisable” savings over a 10-year period, taking into account relevant constraints. The paper summarises results at a European level, focussing on those cases that generate the greatest realisable savings. Savings are possible in three areas: improved equipment and system efficiency, reduced cooling loads, and more effective operation. The cases that offer the largest savings provide the basis for recommendations for policy measures, often using existing policy instruments. In addition, areas where further work is needed are identified. More detailed results and information about the study and additional recommendations can be found at http://www.bre.co.uk/searchresults.jsp?category=5&q=energy+management [1] . [ABSTRACT FROM AUTHOR]

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

• An improved system is proposed based on multi-fans with frequency conversion. • The improved system meets pressure control requirement in non-working modes. • The energy saving and cleanliness can be achieved by switching operation modes. Pressure control for cleanrooms in the pharmaceutical industry is a necessary means to avoid the cross-contamination. The pressure control of traditional air conditioning can only handle the working stage. In the non-working stage, the system cannot reduce the air change rate per hour (ACH) in order to alleviate the problem of high energy consumption. This paper proposes an improved air conditioning system, which focuses on the ability to control pressure gradient of the system in working and non-working mode. By theoretical derivation of the combined fan frequency conversion, when the impedance and volume of the infiltration air satisfy S i G Z , i = S j G Z , j = S k G Z , k = c o n s t a n t , the pressure gradient in multi-zone cleanrooms can remain stable. A mathematical model of the system has been established, which includes five modules: air pipe network, fan characteristic curve, fan operating point solution, variable volume damper characteristic curve and multi-zone network pressure gradient solution. The correctness of the model is verified by field measurements. Taking a clean workshop as case study, the room pressure can be stabilized in the range of ± 3 Pa by switching the working and non-working mode, which can save 39.8% of the energy consumption in non-working mode, while ensuring cleanliness. This paper demonstrates the process of the air pipe network's influence on cleanrooms pressure and proves the effectiveness of improved purification air conditioning system in pressure gradient control and energy saving potential. [ABSTRACT FROM AUTHOR]

The American Society for Heating, Refrigeration, and Air Conditioning Engineers (ASHARE) prescribes fixed ventilation rates for the operation of heating, ventilation, and air conditioning (HVAC) systems, which is sub-optimal in terms of controlling indoor air pollutants and energy consumption. This paper relies on a physics-based model of a residential house to predict the concentrations of indoor pollutants such as ozone, formaldehyde (HCHO), and particulate matter (PM) as function of time, outdoor conditions, and indoor emissions. The model captures real-world scenarios such as pollutants entering the house from ambient air through an air handling unit (AHU) and pollutants emitted in the house. A dynamic optimization problem is then formulated and solved to calculate the optimal ventilation rate at each time instant, that minimizes the total energy consumption, while maintaining the indoor pollutant concentrations below or as close as possible to their respective acceptable thresholds. For the considered example day, operation under optimized conditions reduces the peak pollutant concentration by 31%, time of exposure to undesirable concentrations by 48%, and energy consumption of the AHU by 17.7%. [ABSTRACT FROM AUTHOR]

Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form. [ABSTRACT FROM AUTHOR]

The increased demand of energy in domestic applications necessitates the development of innovative engineering solutions in building heating, ventilating, and air conditioning (HVAC) systems. As the largest energy intensive sector is domestic buildings, more focus is currently directed to reduce air conditioning energy consumption. Double-pipe heat exchangers are considered one of the practical solutions in today’s HVAC industry. Nevertheless, a few studies focus on using double-pipe heat exchangers in air conditioning applications. This paper experimentally investigates the usage of double-pipe condenser and evaporator in an air conditioning system serving a 45 m 3 balanced calorimeter of 2.24 kW heat load. Deionized water (DIW) was used as the secondary heat transfer working fluid for both the evaporator and condenser units, and R-22 was used as the AC system refrigerant. Experimental results of the double-pipe heat evaporator/condenser setup showed a promising reduction in the compressor work and an increase in the system coefficient of performance (COP). The collected data showed that the system efficiency depends more on the evaporator DIW flowrate than on the condenser DIW flowrate. By increasing the DIW flowrate in the evaporator, the compressor work was shown to decrease, while the COP was shown to increase. In comparison with a standard rated air conditioning unit, using a double-pipe evaporator and condenser units with the maximum DIW flowrates resulted in a decrease of about 53% in the compressor work and a similar percentage of increase in the system COP. [ABSTRACT FROM AUTHOR]

The purpose of air-conditioning systems is to provide a suitable indoor environment with respect to temperature, humidity and fresh air. Liquid desiccant dehumidification constitutes an effective method for extracting moisture from humid air with less energy consumption. Current studies mainly focus on dehumidification system under certain heat and mass transfer ability (number of mass transfer units NTU m ). This paper will investigate NTU m of counter-flow packing tower and its influence on system performance. An experiment including a counter-flow packing tower was conducted. When the height of tower was 0.7 m, the volumetric mass transfer coefficient was between 1–4 kg/(m 3 ·s) and NTU m presented a range between 0.9–1.4, when air flow rate changes from 1 kg/(m 2 s) to 2.5 kg/(m 2 s). When the height changed to 2.1 m, the NTU m of the tower presented a variation from 3.3 to 4.3. Furthermore, a simulation model is developed, which is validated by experimental data and past research results. The influence of NTU m on total circulation flow path is mainly caused by heat and cold offset and an NTU m value approximately of 4 constitutes a suitable value of increasing heat and mass transfer ability. Comparing three typical flow paths, when NTU m is lower than 3.5, inter-stage circulation presents improved performance in system COP. When NTU m is large, total circulation is efficient. The system COP cross points of flow paths are caused by heat and cold offset and concentration difference of the solution circulation between the dehumidifier and the regenerator, which can be quantified by loss coefficient ε and χ respectively. The heat and mass transfer ability of the system should be considered in the designing process of flow path configuration to obtain an efficient performance for different range of NTU m . [ABSTRACT FROM AUTHOR]

With the increased complexity of air conditioning systems, the computational load and complexity of real-time optimization appear to be challenging for applications. To overcome the challenge, multiplexed optimization has been proposed in which multiple set-points are sequentially optimized and updated one by one. In such a way, it can achieve equivalently-good energy performance but with exponentially reduced computational load if compared with those traditional methods that optimize all set-points simultaneously. In fact, the time instant for each set-point optimization and the set-point optimization sequence have impacts on the overall performance of the multiplexed optimization. Considering such impacts, this paper proposes a new multiplexed optimization method for enhanced performance. In the proposed method, optimization is activated as the system energy efficiency is low. The set-point optimization sequence is selected according to their impacts on the system energy performance. The one with the most energy saving potential will be firstly optimized. The case study results demonstrate the improved performance of the proposed method in terms of energy, computation load and system stability when compared with the conventional multiplexed optimization. The proposed method can be applied in complex air conditioning systems for energy saving, computation load reduction and system stability improvement. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to suggest a virtual water flowmeter applicable in hydraulic systems with low-cost sensors. Practical procedures for realizing virtual water flowmeters for constant flow pumps (i.e., Cases 1 and 2) and variable flow pumps (i.e., Cases 3 and 4) are proposed in this paper. In Case 1, for measuring the water flow rate through a constant flow pump, the pump head was measured and the water flow rate was estimated using the pump characteristic curve obtained from the manufacturer or by deriving an empirical model based on the field measurement data. In Case 2, the water flow rate through a valve with a constant flow pump was estimated by measuring the pressure head between the valve and the valve opening position. The valve characteristic curve can be obtained by deriving an empirical model based on the initial field test results. The water flow rate through the variable flow pump was calculated, using the pump affinity laws and an empirical model, by measuring the frequency (i.e., Case 3) or input power (i.e., Case 4). The initial setup and test procedures for virtual flowmeters are also proposed in this research for practical use in the field. To verify the proposed procedures, experiments were conducted using constant and variable flow pumps in a pilot system. It was found that the virtual water flowmeters showed 2.9% to 6.7% of root mean square error from Case 1 to Case 4, compared with an ultrasonic water flowmeter, through long-term experimental verification. [ABSTRACT FROM AUTHOR]

Accurate modelling and prediction of energy consumption of the air conditioning system is crucial for improving decision making. A method for predicting the energy consumption of air-conditioning systems is proposed in this paper. Based on the same weather type (sunny, cloudy, overcast, or rainy) and day type (workdays or holidays), the similarity errors using the combined weight method and the baseline errors of similar working conditions are calculated with this method. These conditions include outdoor temperature and lighting and plug power, then, similar days are determined within a certain similar error range. In addition, the air-conditioning energy consumption in these similar days is regarded as that in the predicted days. The similarity errors in selecting similar days are acquired by efficiently combining subjective weights, objective entropy weights, and correlation coefficients. To verify the accuracies of the predicted energy consumption using similar days method based on combined weights, a simulation was performed by eQUEST. According to the simulation example of measured data in an office building, it is proved that the proposed prediction method with high forecast accuracy can select similar days with a high degree of similarity under non-catastrophic weather conditions, and offer promise for wider engineering application. [ABSTRACT FROM AUTHOR]

Industry is stuck in reactive mode regarding the maintenance of their heating, ventilation and air conditioning (HVAC) systems, which leads to inefficient operation. To avoid wasteful use of natural resources a proactive approach is needed. To enable this transition combined knowledge-based and data-driven approaches such as the Industrial Data Analysis Improvement Cycle (IDAIC) framework have been proposed, but there is a lack of practical implementation in the literature. In this paper, we implement the IDAIC framework on an Air Handling Unit (AHU) in a real industrial facility to address the lack of practical and reproducible real world analysis. The lack of domain knowledge-driven data mining solutions is addressed primarily in two parts. Firstly, data and control issues are addressed to enable the application of knowledge-based techniques to detect faults that are easily identifiable. Secondly, data-mining based techniques are applied which focus on the detection of more subtle faults which are indicative of component degradation. This approach enabled the development of data-driven analysis that visualises the effect of the different modes of operation on the energy consumption of the AHU, which allow the onsite facilities team to make more informed decisions regarding the maintenance of this AHU. [ABSTRACT FROM AUTHOR]

Real-time optimal control of an air-conditioning system is a very efficient tool to improve its energy efficient operation, which optimizes and resets on a regular time basis the set points of the local control loops that are significant for the system energy performance. Conventionally, step or rate-limited change is used to reset the set points. However, the step-change reset will always introduce significant disturbances into the control loops; while the rate-limited can reduce the disturbances but lacks systematic study as the rate is always set by a rule of thumb. As the disturbances introduced by the set point reset will not only influence the control loops that experience the reset but also be propagated to other interacted local control loops, they may severely deteriorate the stability of the whole system. This paper therefore presents a stability enhancement study for the real-time optimal control of air-conditioning systems, and proposes a degree of freedom (DOF) based set-point reset instead of the conventional step-change or rate-limited set-point reset. As the DOF based set-point reset needs disturbance models to describe the transient behaviors of the control loops activated by resetting set-points, a subspace identification method with canonical variant analysis is adopted for model identification because it has the ability to model a complex system without structural parameterization. Case studies will show that in comparison with the conventional step reset and rate-limited reset, the DOF-based set-point reset is able to enhance the stability of the whole system significantly. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

This paper presents a detailed investigation and analysis of the energy consumption characteristics of three institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. Identification models are developed to predict the real daily energy consumption data. Developed models involve three specific functions to represent the variability of the daily occupancy, the additional occupancy due to visitors and the variation of outdoor air temperature. The performance of the developed identification model is very satisfactory and fits very well with the real energy consumption data. Based on the identification model, key factors which are influencing the energy consumption in institutional buildings are identified as the variability of the daily occupancy, hence a methodology is developed to calculate the occupancy in the building. The identified parameters are used as inputs into deterministic energy simulation programs, like Energy Plus, to perform detailed energy analysis. The whole methodology developed has improved significantly the accuracy of the energy simulation modeling of institutional buildings and has permitted to understand and evaluate the major energy characteristics of these buildings. [ABSTRACT FROM AUTHOR]

Temperature and humidity independent control (THIC) systems demonstrate promising energy-saving potential compared with the conventional air conditioning systems. One type of THIC systems is proposed to use dual cooling sources with different temperatures since they could be produced easily by traditional vapor compression refrigeration chillers. This paper investigates the applicable condition of the THIC system based on dual cooling sources (DCSTHIC system). It is found that the DCSTHIC system has limited conditions (such as scopes of the heat moisture ratio and the fresh air rate) when applied, otherwise the indoor air temperature and humidity can’t be fulfilled, and further available scope of the heat moisture ratio realized by the DCSTHIC system is disclosed for air conditioned spaces. The minimum realized heat moisture ratio depends on the temperature of low-temperature cooling sources ( t LCS ). A parametric study is also conducted to analyze effects on the energy efficiency of DCSTHIC systems. These parameters include the heat moisture ratio ( ε ) and temperatures of dual cooling sources. Results show that DCSTHIC systems show more significant energy-saving potential in the air conditioned spaces with higher ε . The system COP improvement ranges from 3.1% to 17.5% compared with conventional air conditioning systems when the heat moisture ratio changes from 8000 kJ/kg to 15,000 kJ/kg. This study helps to guide the design and optimization of DCSTHIC systems. [ABSTRACT FROM AUTHOR]

In this paper we explore the influence of personal control over space conditioning systems on occupants’ thermal comfort perception. It aims to identify the psychological effects of perceived control as well as the physical impacts when the controllable approaches are utilized. A chamber experiment was conducted, the results from which indicate that subjects with perceived control tended to report more positive comfort perception. The severer the thermal conditions were, the more subjects wanted to use personal control approaches, thus corresponding to stronger psychological effects. When personal control approaches were utilized appropriately, even a slight improvement in thermal conditions could significantly alleviate subjects’ thermal discomfort complaints. These findings provide support to the adaptive comfort theory and can serve as reference for the design of personal environmental control systems. [ABSTRACT FROM AUTHOR]

This paper shows development of a simple, cheap “Building Energy Management System” (BEMS) to monitor energy consumption, and an application to measure energy consumption, including electricity and gas consumption in buildings. The system uses current transformers to measure electric consumption at each distribution box. Web cameras monitor lighting in each room, as well as air conditioning status by recognizing the movement of a ribbon at the outlet. Web cameras can also monitor gas consumption at the meter through character recognition. The data is sent via network to a computer for monitoring and analysis. As an example, key components of this BEMS were tested to measure building energy consumption of Osaka City University Sugimoto Campus in Japan for a period of 2 years (2013, 2014). The electric consumption of campus as well as the electric and gas consumption for air conditioning use was measured. Web cameras were installed in several rooms in one section of the campus to record lighting and air conditioning use data. The data was analyzed to reveal a correlation between hourly outdoor temperature and hourly electric consumption, with a stronger correlation between hourly outdoor temperature and hourly electric consumption for air conditioning. [ABSTRACT FROM AUTHOR]

• Addressed a real-world challenge for load-shifting multiple ON/OFF staged units. • Presented a unique MPC that shifts load while eliminating undesired peak. • Presented comprehensive 2-month experimental MPC demonstration results. • Achieved significant load shifting with over 30% demand savings. • Discussed experimental charging and discharging processes of building thermal mass. Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented. [ABSTRACT FROM AUTHOR]

Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment's operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization. [ABSTRACT FROM AUTHOR]

Due to the complexity and diversity of energy conversions in HVAC systems, this paper is focused on the central air conditioning systems with water-cooled chiller and its operational energy consumption. Through analyzing the energy flows of the five consecutive loops which extract energy from the conditioned spaces and rejects it to the environment, the global energy intensity index is first to be put forwarded and then the decomposition method which analyzes the influence of the specific consumption (e) and delivered fluid ratios (p) on global energy intensity is proposed. The important conclusion drawn from this method is that it is necessary to decrease both the specific consumption and delivered fluid ratios of the main energy users in order to decrease the global energy intensity of HVAC system. Finally, the global energy intensity of central air conditioning system of a twenty six-story office building in Guangzhou, China in design and real condition is compared and analyzed by e–p decomposition method as a case study. [ABSTRACT FROM AUTHOR]

China has been the world's largest producer and consumer of air conditioners, and more and more RACs 1 1 The RACs is short for room air conditioners. would be owned by China's households along with the rapid economic development. Air Conditioner is also considered as one of the largest potential contributors to energy reduction among home appliances because of the huge energy consumption. Therefore, the national energy efficiency standards were issued to promote the use and production of high-efficient RACs. According to China's energy efficiency standards, this paper investigated the electricity savings and CO 2 emission reductions from RACs over the period of 2005–2025. The results indicate that the rural RAC market which develops more slowly than the urban one still has great potential, and government has to revise subsidy policies to make the standards more effective, especially for rural areas. In 2025, the total electricity consumption of RACs is projected to be 598–674 TW h, while the amount without energy efficiency standards is 753 TW h. From 2005 to 2025, the energy efficiency standards for RACs can save 1430–2540 TW h electricity and reduce 908.3–1610. 1 Mt CO 2 emissions in different scenarios. Finally, we suggest that the standards should be revised every 4 or 5 years with higher revision pace of 8% to 10% [ABSTRACT FROM AUTHOR]

[Display omitted] • Distributed real-time optimal control was developed for typical HVAC systems. • Physical meaning in decomposition was enhanced in the proposed scheme. • Computation load was significantly distributed from BEMS to local computation unit. • Global convergence of the distributed optimal control was investigated. • Distributed and centralized optimal control led to similar energy performance. Real-time optimal control (RtOpt) is an efficient tool to improve the energy efficiency of heating, ventilating and air-conditioning (HVAC) systems. RtOpt is always centralized and solved entirely in building energy management systems (BEMSs). However, centralized optimization is not be an efficient solution because real-time operation data in local loops need to be sent to the central computation unit in BEMS for optimization. This paper, therefore, proposes a distributed RtOpt scheme for typical centralized HVAC systems, which uses a dual decomposition mechanism to break the centralized problem into one mater problem and several sub-problems that can be solved in parallel and independently. The mater problem is converted to a simple mean value calculation using a projected sub-gradient method, thus distributing the computation load from the central computation unit into local computation units. Unlike available decomposition schemes in literature, the proposed method considered the functionality and spatial distribution of the major components of HVAC systems, which could enhance the physical meaning and facilitate the application of the decomposition scheme. The proposed distributed RtOpt scheme was tested in case studies, with numerical analysis being used to demonstrate its potential benefits. [ABSTRACT FROM AUTHOR]

[Display omitted] • Residential fault detection with room thermostat, and local weather station data. • 3R2C modeling using extended Kalman filter when air-conditioning system cycled off. • Indoor air-flow, refrigerant undercharge, and duct-leak fault identification algorithm. • Modeling and fault detection algorithm verified with EnergyPlus simulations. 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. [ABSTRACT FROM AUTHOR]

A novel dynamic overhang system is evaluated in this paper when applied to control solar heat gains through windows of US residential buildings. In particular, the energy performance of the dynamic overhangs is compared to static systems using various operation scenarios. The main objective for the control strategies evaluated in the analysis is to minimize annual heating and cooling energy consumption when dynamic instead of static overhangs are applied to US residential buildings. The operation of the dynamic overhangs is adjusted to account for the range of positions allowed as well as the setting frequencies. In particular, the dynamic overhangs can be set on hourly, daily, and monthly basis depending on the window orientation and the time of the year. The analysis has indicated that the performance of the dynamic overhangs depend on their design specifications as well as on the window glazing type. However, the energy saving potential for the dynamic overhangs compared to static devices depend largely on the window size and the climate with significant reduction in cooling energy use achieved in hot and mild US climatic zones. Indeed, the dynamic overhangs can save over 45% of the air conditioning needs for US homes with window-to-wall ratio of 30% located in Phoenix, AZ, and San Francisco, CA, when automatically operated on hourly basis. [ABSTRACT FROM AUTHOR]