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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]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

In this paper the results of a wide investigation on a geothermal-integrated air HVAC system are reported: in the first part, the energy performances of an Earth to Air Heat eXchanger (EAHX) operating in Pomezia (a locality of the area of Rome, Italy) are carried out through a comprehensive numerical two-dimensional model based on the finite element method and the system is forced under real time-changing weather conditions over 1 year. The model is able to reproduce the time- and space- dependent thermo-fluid-dynamic behavior of the EAHX system, the humid air flux and the surrounding soil, as well as to account for the mass of condensed water in the tubes. In the second part of the investigation, the EAHX is coupled to an air handling unit of a company building for office use and tested while operating both in cooling and heating mode. The hourly results, in terms of power savings and energy consumption were reported for representative days of all the months belonging to the cooling or the heating working seasons, monthly and seasonal assessment. The convenience in terms of seasonal energy savings is +40.8% in cooling mode and +44.1% in heating mode. Then, the economic impact in terms of payback time and net present value was investigated through a cost analysis to examine the return of the economic investment under two possible soil morphologies and different prices of electrical energy in Italy. The simple payback with the real price of energy in Italy (0.50 €/kWh) is 3.3 years for the tufa-soil and 5.8 years for soft rock-soil; the economic return in terms of net present value is around 500 k€ for both the morphologies (560 k€ for the tufa; 446 k€ for the soft rock), per effect of the energy saving and, consequently, for a reduced expense of electrical energy. Therefore, in all the considered cases the installation of EAHX systems in support of traditional systems would generate both energy and economic advantages. [ABSTRACT FROM AUTHOR]

• The effect of the inaccurate load label on prediction models is analyzed. • The method of extracting load from cooling capacity is proposed. • Practical cases are used for validation. • The accuracy of ultra-short-term load prediction models is improved. Data-driven modeling achieves excellent performance in building load prediction by mining the actual load characteristic from operation data. When predicting room air-conditioning load, however, the measured data of cooling capacity cannot be used as the load label in the modeling as customary. The reason lies in the unstable automatic control of the air-conditioner, which makes the cooling capacity oscillate at the actual load. The mismatch of cooling capacity and actual load will affect prediction modeling. This paper first sets up a simulation experiment to quantitatively analyze this effect. The results indicate that the more the cooling capacity deviates from the load, the greater the error of the prediction model. To solve this problem, a load extraction method based on the Fourier decomposition is proposed, which involves transforming the actual cooling capacity data into the frequency domain and extracting the low-frequency components as load data, which is further used for modeling. The experiments show that the accuracy between extracted data and real load exceeds 90%. Additionally, the load extraction method improves the prediction accuracy by up to 17.55% compared to the model using cooling capacity data directly. Finally, a practical case demonstrates the effectiveness of the proposed load extraction method. [ABSTRACT FROM AUTHOR]

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S. Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control. [ABSTRACT FROM AUTHOR]

• A gray-box energy model for residential ACs is developed, calibrated, and validated. • State variables (evaporation and condensation temperatures) are keys in the model. • Heat transfer at condenser and evaporator correlate state variables to model inputs. • Seven performance properties functions are identified for model calibration. • The cooling capacity and electrical power input can be accurately predicted. Energy models for vapor compression refrigeration in residential air conditioners have been developed through white-box, gray-box, and black-box methods in decades. However, existing white-box and gray-box models require complicated equations with detailed geometries while black-box models require substantial experimental data. This paper aims to develop and validate a simple gray-box steady-state energy model without the need for detailed geometries, which can accurately predict the cooling capacity and electrical power input based on outdoor and indoor air conditions, and supply airflow rates. First three state variables, including the evaporation and condensation temperatures, and refrigerant mass flowrate, are applied to develop the energy model and are solved by three physical equations, including the energy conservations at the evaporator and condenser, and the refrigerant volume-mass flow correlation of the compressor. Secondly, seven performance property functions related to three state variables and three physical equations are identified and calibrated by simple temperature and power measurements. Finally, field experiments are conducted on a residential air conditioner to calibrate these performance property functions and validate the developed model. The validated results reveal the model can accurately predict the cooling capacity and electrical power input, with the normalized root mean square errors of 2.3% and 0.87% respectively. [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]

[Display omitted] • Desiccant air conditioning system integrated with PV/T units was simulated. • Optimum area ratio of PV module to PV/T unit is 0.2 and number of the units is 441. • Seasonal thermal and electrical solar fractions are 1 and 1.16, respectively. • Seasonal thermal, electrical and total COP are 0.46, 4.09 and 0.42, respectively. • Proposed air-conditioning system is a self-energizing sustainable system. This paper proposed and simulated a solid desiccant air-conditioning system integrated with a direct evaporative cooler, a dew-point indirect evaporative cooler and heat recovery units during a complete cooling season for a building located in Adana, Turkey. The thermal and electrical energy required for the proposed system are provided by water-cooled PV/T units. The generated thermal energy is used to regenerate the desiccant wheel, while the electrical energy generated is used to power air fans, water pumps and other equipment of the air-conditioning system. The area ratio of PV module to PV/T unit, the number of PV/T units required and the optimum connection strategy of PV/T units are investigated in detail. Five different area ratios are tested to determine optimum total PV/T unit number considering the seasonal thermal and electrical solar fractions during cooling season. The optimum area ratio, total PV/T unit number, seasonal thermal and electrical solar fractions are found to be 0.2, 441, 1 and 1.16, respectively. Seasonal thermal, electrical and total coefficient of performances are calculated as 0.46, 4.09 and 0.42, respectively. The net seasonal primary energy consumption is negative at −102.82 kWh, which demonstrates that the proposed air-conditioning system is a self-energizing sustainable system, and can be considered as an alternative air-conditioning solution for office buildings in the Mediterranean region. [ABSTRACT FROM AUTHOR]

Emission reduction in buildings is essential to combat climate change. However, current strategies failed to balance development and emissions reduction goals. This paper selects a public building in China and simulates its initial energy consumption and carbon emissions. Then it establishes a multi-objective optimization model to explore cost-effective and emission-reduction strategies, considering photovoltaic and ground-source-heat-pump (GSHP) systems. Finally, NSGA-Ⅱ is used to solve, and sensitivity analysis is carried out. Results show that: (1) Energy consumed by the air conditioning system is the largest in January for heating and in July for cooling, validating the accuracy of the energy simulation model. (2) Economic efficiency and emission reduction effect are better when photovoltaic and GSHP systems are implemented simultaneously. (3) Electricity price, initial investment cost, annual solar peak hours, and the proportion of shallow geothermal energy significantly impact carbon emissions and cost. (4) The photovoltaic and power grid substitution ratio will exceed 8% every five years, and all substitutions will be completed in 2050. After adopting the GSHP system, the complete replacement will be advanced to 2045. Setting subsidies significantly shortens the payback period of investment and ensures firms' economic benefits. In summary, using photovoltaic and shallow geothermal energy is conducive to reducing emissions and saving costs in the long term. [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]

• Developed and applied a feature selection algorithm to unitary equipment at part load conditions. • Feature relevancy analysis of the air and refrigerant side is performed. • Feature selection algorithm verified using Artificial Neural Network. • A subset of 33% of features are identified with the same level of accuracy as a complete dataset. • Developed guidelines for the formulation of future models using algorithms. A variable-speed air-conditioning system provides more control than single-or multi-stage units, resulting in increased energy efficiency and more precise temperature control. Current commercial and residential direct expansion (DX) equipment testing standards require the embedded control placed on variable-speed equipment to be deactivated during performance testing and instead the fan and compressor speeds are fixed. For this reason, the test and field performances differ, creating a performance as well as an energy consumption gap. This paper presents a machine learning-based approach to investigate critical features from the air and refrigerant side influencing the cooling capacity of unitary air conditioning equipment. High-fidelity experimental data obtained from 4 different state-of-the-art high-efficiency units, 1 fixed-speed 35.2(10) kW(tons) commercial and 3 variable-speed residential unitary equipment of 12.3(3.5), 14(4), and 17.6(5) kW(tons) of cooling capacity have been analyzed by utilizing a feature selection methodology, Elastic Net (EN). Influential features with higher relevance scores corresponding to the total cooling capacity are fed into a supervised Artificial Neural Network (ANN), formulated as a predictive model to evaluate the validity of features selected. Results show that the proposed method of feature selection when applied, ANN is able to predict the equipment cooling capacity with a mean absolute percent error of less than 2% for all tested units, thus approving the proposed input features. The findings may be used as recommendations for the development of a semi-empirical model for better cooling capacity and COP predictions. [ABSTRACT FROM AUTHOR]

• Selected the outdoor design conditions based on simultaneous occurrence probability. • Analyzed the dependence relation between three weather elements using vine copula. • Modeled the multivariate joint distributions of three weather elements. • The indoor thermal risk levels of outdoor design conditions were evaluated. In building design, the commonly used outdoor weather conditions recommended by ASHRAE are generated by the annual cumulative frequency of occurrence method. This may result in air-conditioning equipment capacity being overestimated. This is because the air temperature, humidity and solar radiation that have the greatest impact on indoor peak cooling loads are selected independently. It means that the probability of these three weather parameters occurring at the same time is extremely small. This paper proposes an approach for selecting the design weather data on the basis of the exact joint distribution with specific simultaneous occurrence probabilities of these three weather parameters. For this purpose, a novel statistical method vine copula is applied to construct the theoretical multivariate joint distributions of these three weather parameters for 24-hour data and combined the design parameters of each hour with a specific simultaneous occurrence probability into a design day. The hourly meteorological records over 10 years in Tokyo are used to generate the outdoor design conditions. The results indicate that the vine copula method can create an effective theoretical model and all the design parameters selected by the simultaneous occurrence probability are significantly below that of the same conventional exceeding probability. For simultaneous occurrence probabilities of 0.4 %, 1 % and 2 %, the corresponding indoor thermal risk levels are 0.46 %, 0.52 % and 0.84 %, respectively. Therefore, the proposed method can be used as a reference for designers to determine a more reasonable capacity of air-conditioning systems. [ABSTRACT FROM AUTHOR]

• Models of the impact of indoor thermal environment on MDPC thermal comfort were established. • The effect of activity levels on thermal comfort in MDPC was quantitatively analyzed. • The relationship among temperature and humidity in different parts and working time and intensity was established. • The acceptable regions inside MDPC were proposed by the enthalpy-humidity chart. With the outbreak of infectious diseases such as Corona Virus Disease 2019, medical staff work intensively in isolated plots, medical disposable protective clothing (MDPC) has poor air condition and humidity permeability, which seriously reduces the thermal comfort of medical staff. In this paper, the effect of indoor thermal environment and activity levels on thermal comfort inside MDPC was studied by experiment. Five parts of the body were measured inside MDPC and the appropriate movements were chosen to simulate different levels of labor intensity. Meanwhile, physiological parameters and subjective thermal sensation were statistically analyzed. The results showed the influence range of different indoor temperatures on the temperature and humidity inside MDPC was about 1 °C and 10 %, respectively; it indicated that the environment inside MDPC could be improved by reducing indoor temperature, that is, a cross intelligent adjustment mode was proposed. The effect of labor intensity on the temperature inside MDPC was significantly less than that of humidity. Within 20 min, the humidity changes under moderate and heavy labor intensity were even more than 10 %, and the subjective discomfort threshold of the subjects increased by nearly 50 %. Furthermore, the maximum benefit could be obtained by concentrating cooling on back, forehead, chest and upper arm. Theoretical models of working time, labor intensity, and temperature and humidity inside MDPC under different indoor temperatures and different parts were given. In addition, acceptable regions inside MDPC which were approximately parallelogram in the enthalpy-humidity chart. These conclusions could be a reference for future thermal comfort inside MDPC research. [ABSTRACT FROM AUTHOR]

Vapor Compression Systems (VCS) are the most common air conditioning technology. VCS cool the air to its dew point temperature (overcooling) to remove water vapor in the air through condensation and then reheats the air back to the comfort temperature for direct use. The VCS process is inefficient due to overcooling and reheating. Liquid Desiccant Dehumidification (LDD) is a potentially energy-efficient air conditioning. LDD removes water vapor in the process air using liquid desiccant's high-water affinity. It hybrids with sensible cooling to control temperature and humidity separately. The LD in the LDD becomes weak after dehumidification. The LDD needs additional heating to regenerate the weak Liquid Desiccant (LD) to a high concentration for dehumidification. Earlier versions of the LDD systems use highly concentrated liquid desiccant (large water removal capability) to dehumidify the air by only dealing with latent load. It leads to highly elevated temperatures above 60 °C of heat sources (combustion or electric resistance-based heating) for regeneration. The energy needed for the elevated temperature heat resource significantly reduces or demolishes the benefit of LDD systems. In the recent two decades, researchers have investigated a new configured LDD system that couples an LDD with a heat pump at both dehumidification and regeneration sides for better efficiency. The heat pump provides cooling (from the evaporator) for both dehumidification and sensible cooling and simultaneous heating from the condenser for regeneration. The highly integrated system (HP-LDD) with improved efficiency enables the LDD to operate at lower concentrations and temperatures in dehumidification and regeneration. This paper depicts the working principle behind HP-LDD and its heating and cooling requirements. It reviews the comparison between the HP-LDD systems and the conventional LDD systems regarding system configurations, component design, energy efficiency, and dehumidification performance characteristics. The main findings from the review include the preferred use of packed bed over membrane-based dehumidifiers, the use of internally cooled dehumidifiers enabled by the HP cooling capacity, the high dispersion of HP operation conditions, and the dependence of dehumidification performance on various dehumidifiers. An outlook for future research on HP-LDD strategies is presented based on the reviewed works and their limitations. [ABSTRACT FROM AUTHOR]

• The influences of various common faults on the VRFs under different fault levels and load rates was analyzed. • A faults experienced database for VRFs was provided. • The fault mechanism was clarified through sensitivity analysis between the physical parameters and different faults. • A reference basis for selecting various fault features and key fault decoupling variables was provided when a multiple fault diagnosis model for VRFs is established. Due to improper installation, operation, and maintenance, many common faults inevitably occur in the air conditioning systems. However, the same fault could demonstrate different characteristics under different load rates and fault levels; different faults may present similar fault phenomena. This paper reveals the quantitative impact of faults on system performance of VRF system with common faults imposed in heating mode through fault experiments. The system parameters sensitivity study has been carried out to understand the internal impact mechanism related to system parameters. This provides a reference for selecting characteristic parameters and fault decoupling variables and developing interpretable, professional, and reliable fault detection and diagnosis models. The results show that the outdoor unit fouling fault can cause the heating capacity to drop by 73.2%, and the indoor unit fouling can cause the system power to drop by 65.5%. The coefficient of performance drop is as high as 48.6%. The sensitivity study result shows that the system load rate is an influential parameter; it needs to be considered when characteristic parameters and fault decoupling variables are selected. [ABSTRACT FROM AUTHOR]

The Dynamic Pressure Regain Method, a new design method for uniform air supply, was proposed in this paper, based on the principle of total pressure balance. It can achieve uniform air supply by keeping equal dynamic pressure in adjacent sections of the main duct that opens up a new field for the design of uniform air supply duct system. Research was carried out through numerical simulation and full-scale experiment. First, the design steps of this method were proposed according to the flow characteristics in the duct. Then the recommended range of air velocity ratio was determined by numerical simulation calculation, and the resistance calculation formulas of the duct under this method were fitted. Finally, the full-scale experiment was carried out for the working conditions of inlet air velocity of 4 m/s and 5 m/s to verify the results of numerical simulation, including the total pressure balance, the uniformity of air supply system, the resistance calculation formulas of duct and the verticality of outlet airflow. The results showed that with a constant air velocity of the main duct sections, the variation of the total pressure loss was within 5 %, thus satisfying the total pressure balance; With a 0.6–1.0 air velocity ratio of the branch to main duct, the air volume imbalance rate of the branch duct was lower than 10 %; The error of local resistance calculation formulas of the duct system was no more than 3 %. The airflow deflection angle travelling out along the branch duct walls was within 20°. This complete design method of uniform air supply satisfies the total pressure balance and features simplicity, which decreases the air volume imbalance rate by 84 % compared with the static pressure regain method. Besides, when the size of the inlet duct remains constant, that of the design model will not change with the inlet air velocity. In addition, this study proposed the resistance calculation formulas that conform to the characteristics of its design model. [ABSTRACT FROM AUTHOR]

[Display omitted] • The motivations to implement pre-cooling are discussed. • Recent pre-cooling and solar pre-cooling studies are reviewed. • Pre-cooling simulation methods are briefly introduced. • Method, aims, and findings of each study are summarized. • Current gaps in the field are identified and future works are suggested. Ever increasing numbers of air-conditioners and, in many places, more extreme weather and urban overheating are placing additional stresses on electricity networks globally, reflecting not only their high power draw but also highly correlated operation driven by weather conditions. The resulting peak demands can be considerably greater than average network flows requiring additional electricity generation and network capabilities for what might only be infrequent periods. Energy users must of course pay for this additional investment, including potentially via peak demand and time-of-use charges. Pre-cooling of buildings to spread air conditioning demand is one approach to reduce these peaks. Its potential contribution depends on the thermal performance of the building stock, particularly how long it can retain pre-cooled air, and patterns of weather on warm days. The use of solar energy, including particularly rooftop solar photovoltaics presents another possible option. Photovoltaics generates during daylight hours, which is well-matched with higher temperatures. However, photovoltaics generation may often be declining while heat loads in buildings and hence air conditioning are still climbing in the late afternoon. This suggests the potential of solar pre-cooling when solar photovoltaic electricity is used to run air conditioners in the pre-cooling phase. Literature reviews to date have tended to focus on only aspects of pre-cooling and solar pre-cooling, for example, the effect of thermal mass on the energy performance of buildings but not specifically on peak demand shifting and cost reduction. There is a particular gap with review papers that investigate pre-cooling and solar pre-cooling together with detailed characterization of their potential in different circumstances. The review presented here covers a wide range of state-of-the-art studies in the field of pre-cooling and solar pre-cooling covering the period 2014 to 2021. It also presents, for the first time, the necessary steps to design and implement pre-cooling and solar pre-cooling strategies. It is intended to support the growing research studies and practical implementations of pre-cooling and solar pre-cooling within the electricity sector. [ABSTRACT FROM AUTHOR]

Central air conditioning accounts for more than 40 % of the energy consumption of social buildings. Ensuring the efficient operation of central air conditioning at full working conditions has a positive impact on reducing the total energy consumption of society. In this paper, the historical operation data of central air conditioning in a commercial building in Shenzhen is used as the source and the energy-saving strategy of this central air conditioning system is studied using 2019 data as the main research data. Firstly, the measured parameters are extracted as the Cooling factor, Delivery factor, and Load factor based on factor analysis, and the K-Means algorithm is used as input parameters for pattern recognition of the central air conditioning system. Then, according to the results of pattern recognition, C5.0 and the CHAID decision tree algorithm are used to obtain the energy-saving strategy model. Finally, the 2020 operational data of the building's central air conditioning system was used as the validation data set for energy efficiency verification. The energy-saving strategy based on the C5.0 decision tree algorithm is calculated to save 78,183 kW·h, which is 32.4 % of the total energy consumption, and the energy-saving strategy based on the CHDIA decision tree algorithm is calculated to save 73,182 kW·h, which is 30.3 % of the total energy consumption. [ABSTRACT FROM AUTHOR]

The vehicle HVAC (heating, ventilating, and air conditioning) system aims to keep passengers thermal comfort and reduce energy consumption. One effective measure is to use temperature prediction to optimize HVAC systems. This paper aimed to comprehensively investigate the potential of machine learning methods applied to cockpit temperature prediction and proposed a deep learning method considering the Spatio-temporal correlation of the temperature field. Specifically, this study constructs a topological description of the temperature field inspired by the spatial and temporal correlations revealed by the Navier-Stokes equations. The Graph Convolutional Network (GCN) is used to capture the topological structure to obtain spatial features, and the Gated Recurrent Unit (GRU) is used to capture the dynamic change of node attribute to obtain temporal features. Finally, the GCGRU model can extract Spatio-temporal features of the temperature field. The experimental results show that the prediction method using Spatio-temporal features for the temperature field is feasible. The prediction performance is better than all the baseline methods and has the robustness to the data noise. This work is enlightening and may have a further reference to the feasibility study of the vehicle cabin air temperature prediction model. [ABSTRACT FROM AUTHOR]

[Display omitted] Pre-cooling is a method to shift peak air conditioning demand to off-peak and shoulder hours by utilizing energy storage in the thermal mass of buildings. This paper evaluates the pre-cooling potential of a range of residential buildings broadly representative of the Australian housing stock. Pre-cooling in 2-, 6-, and 8-star buildings with light, medium, and heavy construction weights in the cities of Adelaide, Brisbane, Melbourne, and Sydney is simulated, and its impacts on household bills, thermal comfort and peak air-conditioning demand reduction are assessed. Star rating is the method for rating Australian buildings based on their annual heating and cooling demand. The housing assessment by both star rating and construction weight highlights the varied potential of pre-cooling. Pre-cooling sensitivity to air conditioning size is also evaluated by simulating 5 k W th , 15 k W th , and 25 k W th air conditioning sizes representing typical small, medium, and large units in the Australian air conditioning market. Using an adaptive thermal comfort model, the impact of people's thermal comfort expectations on pre-cooling is also considered. The results highlight that compared with 2-star buildings, the percentage of daily cost savings from pre-cooling is considerably higher in 6-star and 8-star buildings. Moreover, with a small air conditioner, 2-star buildings are prone to negative seasonal cost savings as the air conditioner is not able to achieve thermostat temperature settings and hence runs longer. However, pre-cooling does offer considerable discomfort reduction to household members. A maximum summer seasonal cost savings of around $260 is observed in a 6-star light-weight building in Adelaide, and a 2-star light-weight building in Brisbane. Households that accept wider temperature variations with pre-cooling may not necessarily see cost savings. These households do provide greater peak air conditioning demand reductions. In conclusion, the value of pre-cooling for households and network peak demand reduction depends significantly on the housing type and location but can be highly valuable in many cases. [ABSTRACT FROM AUTHOR]

• A new design-day hourly variation coefficient generation method. • Three-parameter coupled coefficient joint matrix and K-Means clustering are used to build representative coincident coefficients. • Evaluation of reasonableness based on different load scenarios. • Suitable for different climate zones and typical buildings. The design day is the basis for calculating the cooling load, and a reasonable design cooling load is essential for balancing economics and reliability. The coincidence among weather parameters of design day is a key means to ensure the accuracy, while the coincident design-day hourly variation coefficients are less considered in existing studies. For this reason, this paper proposes a method to establish the coincident hourly variation coefficient for design days. The method first extracts the normalized hourly variation coefficients from 30 years of historical weather data. Based on this, a three-parameter coupled coefficient joint matrix is established and K-Means clustering is used to select representative coincident coefficients. Finally, a "typical line" method based on the weighting idea is proposed, and the final coincident design-day hourly variation coefficients are selected from the actual weather. To verify the rationality of the new method, different load scenarios were constructed with three design days and five typical buildings, and the rationality index was established by the ratio of indoor to outdoor risk levels. The results show that the rationality of the new method reaches 0.81, while the original method (Chinese and ASHRAE standard) is only 0.03 and 0.25 in Tianjin; further study shows that the new method is applicable in different climate zones and between different typical buildings, which provides a basis for the improvement of the existing design day generation method. [ABSTRACT FROM AUTHOR]

• Nonlinear hybrid MPC for buildings energy systems based on Modelica. • Time-variant linearization of the nonlinear Modelica optimization problem. • Automated creation of the linearized optimization problem in every MPC iteration. • Integration of on/ off control, discrete operation states and operation modes. This paper presents a nonlinear hybrid Model Predictive Control (MPC) approach for building energy systems based on Modelica. The MPC approach takes into account two characteristics that are very common for building energy systems: nonlinearities (inherent in the building envelope and Heating, Ventilation and Air Conditioning (HVAC) systems) and discontinuities (in the form of on/ off operation, discrete operation states and operation modes). The hybrid MPC approach integrates both continuous and discrete optimization variables into the control concept and thus is capable of controlling building energy systems with binary or integer decision variables, switching dynamics or logic if-then-else constraints. By employing a time-variant linearization approach, nonlinear Modelica optimization problems are approximated with high accuracy and transformed into a linearized state-space representation. Based on the linearization output, a linearized optimization problem is generated automatically in every MPC iteration, which is extensible by various integer characteristics and is accessible for a wide range of mixed-integer solvers. A simulation study on a nonlinear Modelica building energy system demonstrates the control quality of the proposed toolchain revealing a small linearization error and successful integration of multiple integer characteristics. The benefits of the approach are manifested by comparing its performance with different reference control strategies. [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]

In practice, monthly temperature records play a major role calculating the heating and cooling degree-days of the air conditioner management in buildings. Classically, heating and cooling degree calculations are based on the comparison of outdoor temperature measurements with a standard level called the base temperature. Truncating the temperature series at a constant base temperature causes heating (deficits) and cooling (surpluses) degrees. Since outdoor temperature records are of random character, future predictions with probabilistic and statistical methods are convenient means for calculations. All previous methodologies in the literature are based on crisp logic with simple mathematical calculations. This paper proposes a new methodological approach by converting the actual temperature record probability distribution function (PDF) into a probabilistic standardized degree indicator (SDI) in normal (Gaussian) PDF form with zero mean and unit standard deviation. Furthermore, as a new evaluation methodology fuzzy logic inference calculations are provided. SDI is divided into seven classes, three for cooling, heating, and a single class for uncooled-unheated states. Heating and cooling degree changes according to SDI values are presented with crisp and fuzzy sets. The application of the methodology is made for Kadıköy urban area on the Asian side of Istanbul, Turkey. In the case of fuzzy set SDI air conditioned operation, it has been determined that there is cooling energy saving of approximately 25% compared to the crisp set. The same is true for the heating degree-day side. [ABSTRACT FROM AUTHOR]

• Building energy performance is negatively impacted by suboptimal operations. • Data-driven operations analytics may aid an operator's decision-making processes. • Insights were derived using data from interviewees' individual building portfolio. • Operators were hesitant in addressing deficiencies, specifically controls changes. • Possible barriers to effective utilization of data-driven insights were identified. Data-driven operation and maintenance analytics for improving building energy performance are prevalent in the literature and produce various visualizations and key performance indicators that identify hard and soft heating, ventilation, and air conditioning faults, occupancy patterns, and energy use deficiencies. However, an underlying assumption exists that the outputs of these analyses, some adopted from ASHRAE guidelines, can effectively impart actionable energy-saving insights to building operations personnel and augment the handling of faults, optimized operating controls, or building-level scheduling. This paper attempts to identify potential barriers to effective interpretation and utilization of data-driven, energy-saving insights by building operators and facility managers. Eleven operations professionals were interviewed and assessed regarding what initiatives or changes they would make in light of various outputs of multiple established data-driven approaches. Data from the interviewees' individual building portfolio were inputted into a multi-source data analytics web application, employing fault detection and diagnosis and inverse modelling techniques, and its outputs were presented to interviewees. Only two of the 11 interviewees expressed interest in making controls changes in response, revealing a possible knowledge gap of data-driven analytics, combined with the risk-adverse nature of building operations, which may inhibit operations personnel from effectively utilizing data-driven insights as presented. [ABSTRACT FROM AUTHOR]

• An air conditioning system with thermoelectric modules is analysed and optimised. • Cooling investment and operating costs are discussed. • Heat exchangers are optimised based on economic indicators. • The economically optimised air conditioning system can be significantly overloaded. • TEC air conditioning is expensive but quiet, compact and environmentally friendly. The paper presents simulations of an air conditioning system based on thermoelectric modules. The proposed system is of a very simple structure, easy to control, scalable, noiseless, and harmless to the environment. The most important parameters that were to be changed were the number of modules, the electric current, and the size of the heat exchangers. The analyses were carried out for a commercially available thermoelectric module whose parameters were determined experimentally. The air conditioning system was analysed in terms of its cooling capacity and COP. Furthermore, it was optimized with economic criteria to achieve the highest cooling capacity of 1 dollar of total cost (electricity and investment) in a set period of device lifetime (ETCC – economic total cooling capacity). For 2400 h of device operation, at a temperature difference of 5 K and the average electricity cost in the EU, the optimized ETCC totals about 0.58 W/$. With the possibility of altering the inner geometry of the thermoelectric modules, the factor increased to 0.64 W/$. The test results showed that the device optimized in terms of ETCC did not operate with maximum cooling power and could be overloaded by about 30% in the reference working conditions. [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]

• Nonlinear distributed MPC for multi-zone buildings based on Modelica. • Coupling of thermal and hydraulic systems using Nash optimization and ADMM. • Detailed modeling of the thermal coupling through doors based on data-driven models. • Distributed MPC on nonlinear HVAC models with thermal and hydraulic TABS coupling. This paper presents a distributed Model Predictive Control (MPC) approach for multi-zone building energy systems based on nonlinear Modelica controller models. The method considers both thermal and hydraulic coupling among different building zones. The iterative and parallel distributed optimization approach builds upon an uncooperative approach for thermal coupling using the Nash equilibrium approach and a cooperative approach for the hydraulic coupling using the Alternating Direction Method of Multipliers (ADMM). Apart from thermal coupling through walls, the modeling takes thermal coupling through doors into account using a data-driven approach, which calculates the inter-zone air exchanges based on temperature differences between door-coupled zones. The hydraulic coupling enables consideration of interactions between the zones introduced by a shared, central Heating, Ventilation and Air Conditioning (HVAC) system. The distributed MPC framework is structured in an easy-scalable, plug-and-play composition, where local systems are automatically assigned to the global coordination scheme. The distributed MPC method is applied to a simulative nonlinear case study, consisting of a six-room-building Modelica model considering both thermal and hydraulic interactions. The benefits of the proposed approach are demonstrated and compared against centralized and decentralized control concepts in terms of energy consumption, discomfort and computation time. [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]

• Heating and cooling setpoint temperature of thermostats were optimized in a residential building for different climates of Iran. • GMDH-type neural network and NSGA-II algorithm were used for multi-optimization. • XPS is the optimum insulation for all the climates in Iran. • PPD reduces with thickness of insulation for all the cities, except Bandar Abbas. Determination of the optimum setpoint temperature of thermostats in various climates is a problem in air conditioning of residential buildings. In this paper, a new method is developed to optimize the setpoint temperature of thermostats in different climates of Iran. Design variables in the optimization process are heating setpoint, cooling setpoint, thickness, and thermal conductivity of insulations in the building envelopes. The optimization goals are minimizing energy consumption and cost of insulations in addition to maximizing thermal comfort of occupants. Thus, the static payback period (SPP) and the predicted percentage dissatisfied (PPD) indices are selected as objective functions which should be minimized in the optimization process. The methods applied to attain these objectives are numerical modeling by EnergyPlus software, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Therefore in this process, first, EnergyPlus is used to train the neural network. Afterward, the GMDH-type neural network is applied to derive polynomials computing the objective functions from the design variables. Then, Pareto optimal points for the objective functions are obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum design point is selected for different cities. According to the results, type and thickness of insulation integrated in the building envelopes affect the static payback period and thermal comfort of occupants. For all the climates of Iran, the most appropriate insulation is XPS and the optimum heating setpoint of thermostat is 22 °C. Also, the optimum value for the cooling setpoint pertains to the type of climate, so that this value for Bandar Abbas, Yazd, Tehran, Rasht and, Tabriz is, respectively, equal to 24.5, 24.7, 25.2, 25.3, and 25.6 °C. Moreover, thermal comfort of occupants increases with thickness of insulation, except for Bandar Abbas whose PPD is almost constant. The most value of PPD reduction with insulation thickness is related to Tehran where by increasing the insulation thickness from 1 cm to 5 cm, PPD decreases up to 53%. [ABSTRACT FROM AUTHOR]

• A multi-zone building model based on RC network two-node wall structure method for room temperature is proposed. • The multi-zone building model can accurately simulate the dynamic changes of indoor temperature. • A periodic predictive control model of time-delay system at room temperature based on RBF neural network and multi-zone building model is developed. • The stability of the static pressure of the main air supply duct in the multi-zone VAV air-conditioning system is improved. The multi-zone variable air volume (VAV) air-conditioning system is a complex thermal system with large delay and nonlinearity. Due to the complex environment of multi-zone buildings and the complicated operation process of the VAV air-conditioning system, there are many difficulties in the room temperature control. This paper firstly establishes a multi-zone building model for room temperature using resistance–capacitance method. This investigation simulates and measures the dynamic response of room temperature in a three-floor building without/with air-conditioning for validation. Then a multi-zone VAV air-conditioning system room temperature predictive control model based on radial basis function (RBF) neural network (NN) is proposed. This study sets up a multi-zone VAV air-conditioning system experimental platform in the three rooms on the first floor of the building and implements the predictive control model based on the RBF neural network. The experimental results show that the predictive control model based on RBF NN is able to meet room temperature requirements. It also has strong anti-interference performance and ensures stable static pressure of the main air supply duct. The multi-zone building model can accurately simulate the temperature changes of each room when the air supply volume varies. [ABSTRACT FROM AUTHOR]

Today, Building Energy Models (BEM) have become essential in regulatory compliance calculations, the correct assessment of it's Air Conditioning (AC) systems is critical for the reduction of the performance gap between BEMs and reality and increase the accuracy of evaluating buildings energy performance and it's systems efficiency. Given that multi-split Variable Refrigerant Flow (VRF) systems have grown in the market in recent years becoming a particular trending solution to achieve building indoor comfort; the present paper focus on technical issues when modelling such VRF systems inside EnergyPlus, a white-box simulation environment, especially regarding the effects weather conditions have on the behaviour of VRF systems and it's correlation with the AC system performance curves. The study performs an empirical validation of an optimization-based calibration methodology assessing multiple levels: average interior temperature of the different building spaces and electric energy consumption from VRF outdoor unit. It is performed using fifteen minute time-step seasonal data obtained from a fully operational building located in a typical Mediterranean climate (Greece), adjusting the parameter and curve values of the VRF system using a genetic NGSA-II algorithm (Jeplus software) for both summer and winter conditions. The generated BEM captures the building's hourly performance for summer conditions using 1717 hours to fit into international standards. Complying with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2002 for hourly energy consumption, reaching an NMBE ⩽ ±10% ,Cv(RMSE) ⩽ 30% and R2 ⩾ 75% while keeping indoor temperatures on every room with a RMSE ⩽ 1 ° C. The resulting BEM proved stable during the 2077 hours of it's summer evaluation period, fitting into the new unseen weather and building operation conditions of 2020 which can be considered a step forward in the area of calibrating white box models. While for winter conditions the study demonstrates the value of the calibration methodology while presenting the importance of weather influence on VRF systems. Using a total of 802 hours the applied technology greatly improves the results from the baseline model, reaching a partially calibrated BEM model for winter. Which reinforces the fact that regardless of how good a baseline model is, building operating conditions and weather may will always generate a design/performance gap and therefore the calibration of a BEM is unavoidable. [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] The presence of sensing and actuating equipment faults in Heating, Ventilation and Air-Conditioning (HVAC) systems may result in unsatisfactory indoor thermal conditions and significant wasted energy in the building sector. The design of fault diagnosis algorithms for multi-zone buildings is a highly challenging procedure due to the overall complexity of the system. This paper proposes a distributed model-based fault diagnosis algorithm for detecting, isolating and identifying faults that can affect the actuating devices of Air Handling Units (AHUs) and the sensor devices. Based on modeling the temperature dynamics of each local AHU and its underlying zone with respect to its neighboring zones, a local fault diagnosis agent that collects local control signals and local and neighboring sensor measurements is designed. Each local diagnosis agent is designed to estimate on-line the states of each AHU. For each estimation, a residual signal is derived that corresponds to the difference between the measured and estimated value at each time step. Considering healthy conditions, the residual is bounded by its corresponding adaptive threshold that is calculated considering the effects of modeling uncertainties. Each corresponding pair of residual and adaptive threshold forms an analytical redundancy relation which indicates the possible presence of one or more faults. Exploiting the dependency of each actuator and sensor fault with each Analytical Redundancy Relation (ARR), a decision logic is designed, that can reveal under certain conditions the location and type of the fault during the operation of the system. The proposed algorithm is evaluated using a simulation example of a primary school building with 25 zones. [ABSTRACT FROM AUTHOR]

• Estimation of annual electricity consumption (EC) of more than 1,700,000 buildings. • EC models based on top-down, bottom-up, regression analysis, GRU and GIS mapping. • Contribution of building and socio-demographic variables on EC. • Effect of temperature on actual and predicted EC used for heating and cooling. • EC is spatialized on different territorial scales. This study financed by ERDF and the Wallonia Region estimates the annual electricity consumption (EC) of building stock, including the 3 building sectors namely residential, tertiary and industrial building. The estimation takes into account appliances, electrical heating, cooling, lighting, cooking and EC by m2 on a building level. The results are spatialized on different territorial scales. Using cadastral data of more than 1,700,000 Walloon buildings and annual EC data from a sample collected in 2012 from the energy reports, the paper assesses the EC of the whole building stock and tests to what extent different types of variables (building factors and socio-demographics) explain annual EC. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the research recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue. Three separate regression models were used to study the predictors of annual EC of residential, tertiary and industrial buildings. EC building factors (appliances, auxiliary and main heating, domestic hot water and cooking) explained the largest share which is 66.46% of the variability in EC for residential buildings whereas the EC usages share for tertiary buildings (lighting, heating and domestic hot water, air conditioning, cooling, etc.) is about 50.53% and 38.55% for industrial buildings. Socio-demographic variables on their own explained about 61.59%, 26.34% and 3.41% of the annual EC, respectively for residential, tertiary and industrial buildings. Hence, the building variables present the highest explanatory power for EC, presumably because heating and cooling EC are included in this study. The study highlights that when attempting to explain EC related to Walloon households, including heating and cooling EC, appliances usage has the strongest predictive power in residential buildings. On the other hand, the projected decrease in EC use for heating in existing residential buildings is −8.82% and −10% for existing tertiary buildings while the projected increase in EC use for cooling in existing tertiary buildings is + 11.94% from 2012 to 2050 on a regional scale. These trends follow the predicted regional heating degree-days (HDD) of −11.76% and cooling degree-days (CDD) of 14.04% for the same period based on the gated recurrent unit (GRU) an implemented deep learning (DL) model. In addition, the produced EC maps on different territorial scales show that the highest EC is seen in large and main cities in general. [ABSTRACT FROM AUTHOR]

• Experiments on high-efficiency heat pump with two, three, four simultaneous faults. • Four faults increased refrigerant superheat up to 18 °C. • NC faults tend to increase refrigerant subcooling. • An existing virtual refrigerant charge sensor was tested with simultaneous faults. Faults in air-cooled vapor compression air-conditioning systems are known to reduce performance, including efficiency, capacity, and lifespan. Their effects have been studied, and fault detection and diagnostic (FDD) methods have been developed as tools for field technicians to install or repair systems, or for monitoring to alert operators to the fault's presence. Most of this work has focused on faults that occur singly. It is likely that in some systems, multiple faults occur simultaneously, but it is uncertain what effects this may have on diagnostics. This paper describes a laboratory study of a split system air source heat pump in which combinations of two, three, and four simultaneous faults occur. The study includes all combinations of: improper evaporator airflow; overcharge or undercharge of refrigerant; liquid line restrictions; and non-condensable gas in the refrigerant, each at multiple fault intensities. Fault features – those characteristics that can be determined from measurements, for use in diagnostics – are analyzed, and the key fault features are presented. A robust existing method for determining refrigerant charge, the virtual refrigerant charge sensor (VRC) is tested using the multiple fault data, in order to understand how its performance is impacted by the combined faults. The VRC performs well, typically able to correctly determine whether a system is undercharged or overcharged, but the magnitude estimates are impacted. The results suggest that simple subcooling-based methods of charging a system are likely to provide unsatisfactory results when other faults are present. [ABSTRACT FROM AUTHOR]

Poor building energy performance often arises from suboptimal operations such as inappropriate control sequences or hard faults in heating, ventilation, and air-conditioning (HVAC) systems. Furthermore, such deficiencies are often left unaddressed due to a lack of accessible analytical tools that can derive insights which identify energy-saving measures using multiple data resources. This paper presents a novel multi-source, data-driven building energy management toolkit as a synthesis of established inverse energy modelling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods in the literature. The toolkit contains seven functions that input HVAC controls, energy meter, Wi-Fi-based occupancy, and work order log data to detect hard and soft faults, improve sequences of operation, and monitor energy flows, occupancy patterns, and occupant satisfaction. The toolkit's unique multifaceted analytical approach was demonstrated on a case study building as a proof of concept. Five faults pertaining to the air handling units' mode of operation and heating coil valves were identified and the generated insights were used to pinpoint operational deficiencies stemming from inappropriate zone temperature overheating thresholds and perimeter heating devices. The toolkit, along with the data from the case study, is open-source and accessible through GitHub (Markus, 2021) to initiate and facilitate future development. [ABSTRACT FROM AUTHOR]

• An office was instrumented to collect human-activity and environmental parameters. • The ability of three models to estimate the occupancy state through the sensors data was verified. • Law of Total Probability, Naïve Bayes classifier, and CART were the chosen models. • Models were performed by considering 34 different combinations of parameters. • The number and the type of the used parameters influenced the models accuracies. Occupancy modeling in office buildings is still in progress and needs to be developed by using observable data. In this paper, an office building under the Mediterranean climate was instrumental in the collection of both indoor environmental parameters (air temperature and relative humidity, CO 2 , VOC) and user action-related variables (electricity power, window, door state, and air conditioning use). Each parameter was monitored along with the occupancy state at a one-minute time step for two years. The purpose of the investigation was to evaluate the potential application of three straightforward models, such as the Law of Total Probability (LTP), Naïve Bayes classifier (NB), and Classification and Regression Tree (CART), to estimate the occupancy state using the indoor measurements. Thirty-four (34) different combinations of parameters were applied on the developed models; the true positive rate (TPR), true negative rate (TNR), and accuracy were used as evaluation metrics. The results confirmed that the performances of the models were influenced by both the number and the typology of the used parameters. In particular, the CART model was found to be the least affected by them; almost half of the parameters' combinations provided accuracies higher than 93% and TNR higher than TPR. Accuracies of the order of 90% were obtained with NB and LTP. [ABSTRACT FROM AUTHOR]

[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]

• The potential of energy saving by using adaptive setpoint temperatures. • The influence of thermostats according to their accuracy: 0.1, 0.5, and 1 °C. • The analysis of two case studies in the current scenarios, 2050 and 2100. • The 0.5 and 1 °C thermostats achieved energy savings of more than 40%. Building energy performance should be improved to reduce the impact of climate change. The energy saving potential has been recently proved with adaptive setpoint temperatures. However, the accuracy of thermostats hinders the achievement of the energy saving obtained in previous studies. For this reason, this paper studies the influence of three types of thermostats according to their configuration accuracy: 0.1, 0.5, and 1 °C. Two case studies (with and without retrofitting) were analysed in three cities in the current scenario, in 2050, and in 2100. The results showed that the implementation of adaptive setpoint temperatures in thermostats of 0.1 °C virtually obtains the same savings as the direct application of thermal comfort limits. Nevertheless, obtaining considerable energy savings in the other two thermostats depends on the type of energy consumption, climate, and the category of the thermal comfort model. The application of adaptive setpoint temperatures in air conditioning systems obtains energy savings greater than 40%, regardless of the type of thermostat and category, whereas in heating systems, only the category III obtains energy savings with old thermostats. [ABSTRACT FROM AUTHOR]

• Experiments on high-efficiency heat pump with two, three, four simultaneous faults. • Two faults reduced COP up to 24% • Four faults reduced cooling capacity up to 38% • Fault interactions can have significant synergistic or cancelling impacts. • Superposition principle for modeling fault impact has up to 25% error. Air conditioning systems are known to experience operating faults, often caused by problems in their installation. To understand the impacts of these faults on system-wide performance requires laboratory tests with faults imposed, and many such experimental results have been published. However, it is likely that multiple simultaneous faults occur, but published results from testing with two simultaneous faults are very limited. This paper presents results from a laboratory study of an air-to-air heat pump system with combinations of: refrigerant charge (CH) too high or too low, evaporator airflow (EA), non-condensable gas (NC) in the refrigerant, and liquid line restrictions (LL). It provides the first published results of combinations of three and four simultaneous faults, in addition to previously untested types of double fault combinations. The impacts on cooling capacity and coefficient of performance (COP) are analyzed. COP reductions up to 34% occur within operation safety limits, meaning that such fault combinations would go unnoticed. An additional analysis examines the extent to which the superposition principle applies to combined fault impacts. The superposition principle is shown to have up to 25% error, indicating that there are synergistic effects – and sometimes cancelling effects – in the impacts from fault combinations. [ABSTRACT FROM AUTHOR]