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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]

• 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]

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]

• 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]

• 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]

• 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]

[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]