Abstract A review of the heating, ventilation and air-conditioning control problem for buildings is presented with particular emphasis on its distinguishing features. Next, we not only examine how data-driven algorithms have been exploited to tackle the main challenges present in this area, but also point to promising future investigations both from theoretical and from practical viewpoints. Rule based control, reinforcement learning, model predictive control (MPC), and learning MPC techniques are compared on the basis of four attributes that we expect an ideal solution to possess. Finally, on-line learning MPC with guarantees is recognized as an approach with high potential that needs to be further investigated by researchers. Such a solution is likely to be accepted by practitioners since it meets the industry expectations of reduced deployment time and costs.
Stirling cryocoolers have the advantages of fewer moving parts, oil-free, single-phase heat transfer, a large refrigeration temperature range, convenient and adjustable refrigeration capacity, and high refrigeration efficiency. Therefore, they have broad application prospects in commercial and domestic refrigeration systems. To meet the demands of low-temperature refrigerators, this study developed a multi-temperature zone refrigerator with a vapor compression refrigeration system and a Stirling cryocooler. The performance of the Stirling cryocooler was tested. The designs of the heat dissipation and cooling conduction structures were optimized, and the refrigerator’s overall performance was examined. Moreover, the influences of input power and ambient temperature on the performance of the Stirling cryocooler were investigated. The results showed that the cooling output and coefficient of performance (COP) of the Stirling cryocooler increased with an increase in the cold-end temperature, and increasing the input power provided a higher cooling capacity but decreased the COP. Under an ambient temperature of 43 °C, the cooling capacity of the Stirling cryocooler was 28.97 W with a COP of 0.37 when the cold-end temperature was -60 °C. Heat transfer structures at the cold and hot ends of the Stirling cryocooler based on heat-pipe technology were also proposed, and the simulation showed that the proposed structures for heat dissipation and cooling conduction met the system design requirements. Finally, an experimental test of refrigerator performance was conducted. A no-load experiment at an ambient temperature of 32 °C showed that the pull-down time of the low-temperature room was 28% shorter than that of the freezer room. Under an ambient temperature of 43 °C, the low-temperature room reached an average temperature of -64.5 °C, and the power consumptions of the Stirling cryocooler and refrigerator were 2.33 kW?h/d and 4.13 kW?h/d, respectively.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
https://doi.org/10.1016/j.enbuild.2022.112165 0378-7788/ 2022 Elsevier B.V. All rights reserved. Abbreviations: AEC, Architectural Engineering, and Construction; AC, Air conditioning; ACH, Air change per hour; BPS, Building Performance Simulation; CEN, E Committee for Standardization; CDD, cooling degree days; DBT, Dry Bulb Temperature; DHW, Domestic Hot Water; EE, Eastern European; EPBD, Energy Perform Building Directive; EPC, Energy Performance Certificate; EUI, Energy Use Intensity; EU, European Union; ETICS, External Thermal Insulation Composite Systems; FIT Tariff; GHG, Greenhouse Gas; HDD, heating degree days; HP, Heat Pump; HRV, Heat recovery ventilation; HVAC, Heating, Ventilation and Air Conditioning; IEA, Inte Energy Agency; IEE, Intelligent Energy Europe; IEQ, Indoor Environmental Quality, LCA, Life Cycle Assessment; MS, Member States; MVHR, Mechanical Ventilation w Recovery; nZEB, nearly Zero Energy Buildings; NZEB, Net Zero Energy Buildings; OT, Operative Temperature; PE, Primary Energy; PEF, Primary Energy Factor; PH House; PMV, Predicted Mean Vote; PV, Photovoltaic; RES, Renewable Energy Systems; SCOP, Seasonal Coefficient of Performance; SEER, Seasonal Energy Efficiency R Smart Readiness Indicator; SHW, Solar Hot Water; SME, Small and Middle Enterprise; VRF, Variable refrigerant flow; WWR, Window to Wall Ratio. ⇑ Corresponding author. E-mail address: shady.attia@uliege.be (S. Attia). Shady Attia a,⇑, Jarek Kurnitski , Piotr Kosiński , Anatolijs Borodin ecs , Zsofia Deme Belafi , Kistelegdi István , Hrvoje Krstić , Macedon Moldovan , Ion Visa , Nicolay Mihailov , Boris Evstatiev , Karolis Banionis , Miroslav Čekon , Silvia Vilčeková, Karel Struhala , Roman Brzoň , Oriane Laurent a,o
L. Carnieletto, Martina Ferrando, Lorenzo Teso
et al.
Abstract Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.
The measurement of CO2 concentration is a relevant indicator for defining the occupation of indoor spaces. The real-time knowledge of occupation of such spaces is relevant both for maintaining indoor air quality standards and for energy efficiency purposes connected with the operation of heating, ventilation, and air-conditioning (HVAC) systems. The exact knowledge of occupation allows for rapid feedback from and the regulation of an HVAC system and the ventilation rate. Interesting applications include educational buildings and other buildings of the civil sector (e.g., shopping centres and hospitals). This paper provides the results of an experimental analysis in different classrooms of a university campus under real operating conditions, in different periods of the year, and with different kinds of activities. The correlation between the CO2 concentration and occupancy profiles of the spaces is then analysed. Some graphical trends of the CO2 concentrations in these indoor spaces are provided to determine the most important variables affecting such concentrations. The basic elements of the mathematical models for estimating the occupation of classrooms in relation to increases in CO2 concentration are also discussed and analysed.
O. Hjelkrem, Karl Yngve Lervåg, Sahar Babri
et al.
Abstract In this paper, an energy model for battery electric buses (Ebus) is proposed. The model is developed based on established models for longitudinal dynamics, using event-based low-frequency data. Since the energy model is able to provide relatively accurate estimation of Ebus energy consumption with limited input requirements, it can be easily applied for future bus route planning. In addition, we have introduced a comprehensive model of the auxiliary systems, which contributes significantly to the total energy consumption ofa bus. The model for auxiliary systems includes heating, ventilation, air conditioning, and other electrical components. To evaluate the model, data was collected from 3266 trips with Ebuses operated in China and Norway. The results show that the model is able to predict the energy consumption on a trip level comparison.
Abstract The occupant-centric control (OCC) is receiving an increasing attention since it could reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CPBM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis, which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones, was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in the majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.
Abstract Predicting thermal comfort in an automotive vehicle cabin's highly asymmetric and dynamic thermal environment is critical for developing energy efficient heating, ventilation and air conditioning (HVAC) systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and machine learning algorithms to predict vehicle occupant thermal comfort for any combination of glazing properties for any window surface, environmental conditions and HVAC settings (flow-rate and discharge air temperature). A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training data that spanned the entire range of boundary conditions, which impact occupant thermal comfort. Three machine learning algorithms: linear regression with stochastic gradient descent, random forests and artificial neural networks (ANN) were applied to the simulation data to predict the Equivalent Homogeneous Temperature (EHT) for each passenger and the volume averaged cabin air temperature. The trained machine learning models were tested on unseen data also generated by the CFD model. Our best machine learning model was able to achieve a test error of less than 5% in predicting EHT and cabin air temperature. Predicted EHT can also yield thermal comfort metrics such as Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), which can account for different passenger profiles (metabolic rates and clothing levels). Machine learning models developed in this work enable predictions of thermal comfort for any combination of boundary conditions in real-time without having to rely on computationally expensive CFD simulations.
Commuter buses have a high passenger density relative to the interior cabin volume, and it is difficult to maintain a physical/social distance in terms of airborne transmission control. Therefore, it is important to quantitatively investigate the impact of ventilation and air-conditioning in the cabin on the airborne transmission risk for passengers. In this study, comprehensive coupled numerical simulations using computational fluid and particle dynamics (CFPD) and computer-simulated persons (CSPs) were performed to investigate the heterogeneous spatial distribution of the airborne transmission risk in a commuter bus environment under two types of layouts of the ventilation system and two types of passenger densities. Through a series of particle transmission analysis and infection risk assessment in this study, it was revealed that the layout of the supply inlet/exhaust outlet openings of a heating, ventilation, and air-conditioning (HVAC) system has a significant impact on the particle dispersion characteristics inside the bus cabin, and higher infection risks were observed near the single exhaust outlet in the case of higher passenger density. The integrated analysis of CFPD and CSPs in a commuter bus cabin revealed that the airborne transmission risk formed significant heterogeneous spatial distributions, and the changes in air-conditioning conditions had a certain impact on the risk.
Abstract As a common physical phenomenon, frost deposition is inevitable and always has significant negative effects on several industry fields, such as aerospace, aviation, and heating, ventilation, air conditioning, and refrigeration. To accurately predict and control a frosting–defrosting cycle, there is a need to understand the interrelated heat, mass, and momentum transport phenomena within the frost and at the air–frost interface, which is a moving boundary condition. Consequently, during the past several decades, there has been a continuous effort to advance the understanding and modeling of frost formation on cold surfaces on the basis of experimental, semi-empirical, theoretical, and numerical approaches. To provide an overview of the analytical tools for scholars, researchers, product developers, and policy designers, a review and a comparative analysis of the available literature on frosting characteristics, correlations, and mathematical models are presented in this study. The mechanisms of the frost formation process and its influence will be first introduced, followed by the presentation of methods for the measurement of the frost layer thickness and the frosting rate. Then, the frost characteristics, including the accumulation, the density, the thermal conductivity and morphology, and the heat and mass transfer coefficients, will be summarized. The existing gaps in the research works on frost will be identified, and recommendations will be offered as per the viewpoint of the present authors. Finally, the conclusions of this study will be given.
Sokratis Papadopoulos, Constantine E. Kontokosta, A. Vlachokostas
et al.
Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for a significant share of the energy consumed in commercial buildings. While energy system retrofits have been found to reduce buildings' carbon footprint substantially, these measures are often hindered by financial, regulatory or design constraints. Recent research sheds light on energy management approaches to energy conservation such as energy-efficient settings of HVAC temperature setpoints. While existing case studies confirm the significant energy saving potential of efficient HVAC operation, there is scarcity of studies quantifying energy savings from optimal HVAC temperature setpoints comprehensively, while controlling for important factors, such as guaranteeing tenant thermal comfort levels and the impact of different climate conditions on the results. In this work, we apply simulation-based multi-objective optimization to fine-tune heating and cooling setpoints of large “typical” office buildings with respect to energy consumption and occupant thermal comfort. We apply the framework in seven climate zones across the US in an effort to examine spatial variations in the energy savings potential due to different climate conditions and propose targeted energy-saving strategies and policies. We show that locations with mild climates, such as San Francisco, CA, can realize up to 60% of annual HVAC-related energy savings without compromising the occupants' thermal comfort. This untapped potential to simultaneously improve building performance and occupants’ comfort drives the discussion on revisiting HVAC setpoint configuration standards in commercial buildings, either as part of individual building retrofit planning or as part of energy policy regulations.
With the development of smart grid technologies, residential and commercial loads have large potentialities to participate in demand response (DR) programs. This makes the data dimension reduction techniques and classification processing critical for the success of DR development. A novel load profile clustering method is proposed for load data classification which is based on the information entropy, piecewise aggregate approximation, and spectral clustering (SC). The variable temporal resolution technique is presented to model typical daily load datasets, and then an improved SC based on multi-scale similarities of distance and shape characteristics is proposed for clustering to obtain reasonable load classification. A case study with one hundred of commercial heating, ventilation, and air conditioning data analysis illustrates the approach. The results prove that the proposed method is feasible in terms of data dimension reduction, reasonable profile selection and classification, and the operation stability.
Based on a three-dimensional distributed parameter model, a simulation model of a small-diameter heat exchanger in the indoor unit of a split-household air-conditioner was established, and the performance metrics were computed, including the total heat load, sensible heat load, latent heat load, refrigerant-side pressure drop, and air-side pressure drop. The effects of the tube length, refrigerant mass flow rate, air volumetric flow rate, air inlet temperature, and air inlet relative humidity on the heat exchanger performance metrics were determined under different working conditions. For the 5 mm diameter heat exchanger considered in this study, the corresponding tube length range was 0.6~0.7 m, achieving good heat transfer with a small pressure drop. Owing to the comprehensive influence of the heat transfer coefficient and the effective mass transfer time, when the volumetric flow of air was in the range of 600–700 m3/h, the latent heat load reached a maximum of 426 W. With an increase in the air inlet temperature, the sensible heat load first increased and then decreased.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
In this paper, an aggregate game is adopted for the modeling and analysis of energy consumption control in smart grid. Since the electricity users’ cost functions depend on the aggregate energy consumption, which is unknown to the end users, an average consensus protocol is employed to estimate it. By neighboring communication among the users about their estimations on the aggregate energy consumption, Nash seeking strategies are developed. Convergence properties are explored for the proposed Nash seeking strategies. For energy consumption game that may have multiple isolated Nash equilibria, a local convergence result is derived. The convergence is established by utilizing singular perturbation analysis and Lyapunov stability analysis. Energy consumption control for a network of heating, ventilation, and air conditioning systems is investigated. Based on the uniqueness of the Nash equilibrium, it is shown that the players’ actions converge to a neighborhood of the unique Nash equilibrium nonlocally. More specially, if the unique Nash equilibrium is an inner Nash equilibrium, an exponential convergence result is obtained. Energy consumption game with stubborn players is studied. In this case, the actions of the rational players can be driven to a neighborhood of their best response strategies by using the proposed method. Numerical examples are presented to verify the effectiveness of the proposed methods.
Realizing the dynamic redundancy of sensors is of great significance to ensure the energy saving and normal operation of the heating, ventilation, and air-conditioning (HVAC) system. Building a virtual sensor model is an effective method of redundancy and fault tolerance for hardware sensors. In this paper, a virtual sensor modeling method combining the maximum information coefficient (MIC) and the spatial–temporal attention long short-term memory (STA-LSTM) is proposed, which is named MIC-STALSTM, to achieve the dynamic and nonlinear modeling of the supply and return water temperature at both ends of the chiller. First, MIC can extract the influencing factors highly related to the target variables. Then, the extracted impact factors via MIC are used as the input variables of the STA-LSTM algorithm in order to construct an accurate virtual sensor model. The STA-LSTM algorithm not only makes full use of the LSTM algorithm’s advantages in handling historical data series information, but also achieves adaptive estimation of different input variable feature weights and different hidden layer temporal correlations through the attention mechanism. Finally, the effectiveness and feasibility of the proposed method are verified by establishing two virtual sensors for different temperature variables in the HVAC system.