Heating Ventilation Air Conditioning System Research Articles

Statistical Analysis of Operational Carbon for Different Building Types in the UAE

The building sector contributes significantly to the greenhouse effect, generating significant carbon dioxide (CO2) emissions throughout the life cycle of buildings. Traditional methods for assessing emissions, such as software evaluation and site inspection, are time-consuming and do not adequately account for variability and uncertainty in emission data. This research aims to investigate and analyze the statistical characteristics of the operational carbon produced from different types of buildings in the context of the United Arab Emirates (UAE). The investigation focused on residential, commercial and educational buildings and their heating, ventilation, air conditioning (HVAC) systems, walls and window systems. All scenarios were statistically evaluated through linear-regression analysis, correlation analysis, Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs). The results of linear-regression analysis revealed an average accuracy (R2 ) of 0.958. The results of correlation analysis indicated that upgrading the HVAC system in residential and commercial buildings reduced the operational carbon, while in educational buildings, upgrading the window systems reduced the operational carbon. Finally, the PMF and CDF analyses indicated that upgrading the HVAC system in residential and commercial buildings was the optimal option, which reduced the carbon percentage by 28.56% and 28.48%, respectively. However, upgrading the window system was the optimal option for educational buildings, reducing the carbon percentage by 75.80%. Keywords: Operational carbon, Linear regression, Correlation analysis, Probability mass functions, Cumulative distribution functions.

Modeling and optimization of a passive building HVAC system based on improved PSO and H-J Algorithm

Passive buildings employ the principles of active optimization and passive priority to demonstrate their energy-saving, comfortable, and cost-effective qualities, thereby addressing energy consumption in an efficient manner. However, it cannot be ignored that the Heating Ventilation Air Conditioning (HVAC) system is still the largest energy consumer, especially when it is necessary to maintain the comfort of the indoor environment. Consequently, the control and optimization of the HVAC system is a research hotspot and a challenge in the field of building energy conservation, as it is the key to achieving ultra-low energy consumption operation in passive buildings while ensuring the thermal comfort of the indoor environment. This paper presents a comprehensive modeling, control, and optimization framework for the efficient operation of HVAC systems in a passive building located in Jinan, China. Firstly, the HVAC system is modeled using the transient system simulation tool (TRNSYS), GenOpt, Java, and other software. Secondly, the HVAC system’s year-round operation is simulated using specific configurations. In order to enhance the energy-saving effect, a strategy of increasing heat storage and return air ducts was proposed, and a significant energy-saving effect (approximately 17.31%) was realized. Lastly, the data communication interface with GenOpt is established utilizing the TRNOPT module, a method for actively optimizing the refrigerant temperature is proposed, and recalculation and optimization are carried out utilizing the swarm intelligence algorithm and the pattern search algorithm (Hooker-Jeeves algorithm, H-J algorithm). The final optimization results demonstrate a 19% reduction in energy consumption relative to the baseline heat recovery model, validating the applicability of the optimization method under this strategy. The control and optimization method of the HVAC system proposed in this paper can further exploit the energy-saving potential of the passive building HVAC system and will contribute to the advancement of building energy conservation.

Acoustic resonances in an automotive HVAC outlet

In order to better understand the noise generation mechanism of air outlets in an HVAC (Heating Ventilation Air Conditioning) system of a car, investigations were performed in a generic geometry representing a typical air outlet of an automotive car. A hybrid aeroacoustic simulation based on a Large-Eddy Simulation of the incompressible flow coupled with a solver for the acoustic perturbation equations is backed up by an experiment to study the interaction between a throttle valve (used to control the flow rate) and fins (used to guide the flow). Several sound generation mechanisms are identified, of which the so-called Parker-β mode is the most prominent in the sensible frequency range. Both the experimental and simulation results indicate a whistling noise at certain gap lengths between the throttle valve and fins at the frequency of the Parker-β mode. Two different levels of resonance are identified of which one leads to feedback from the acoustic mode to the flow field and one works without feedback. We demonstrate that the latter is established by an efficient excitation of the Parker-β mode by the vortex shedding from the throttle valve if the vortex shedding frequency is close enough to the acoustic frequency.

Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building

Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants’ behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants’ thermal comfort level with reasonable computing time. In addition, this model can improve occupants’ thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings.

Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort

Cutting-edge building energy management systems (BEMS) interact with heating, ventilation, air conditioning (HVAC) systems, which generally account for much of the energy consumption. Major attention is focused on the BEMS themselves, barring on-field equipment. In HVAC equipment, sub-optimal controller settings may lead to energy losses and user discomfort, for instance, due to oscillations of air temperature and fan speeds. The way to solve this problem could be to replace classical PID controllers with an alternative concept that does not require tuning and works optimally for a wide range of parameters. This paper compares a fuzzy logic controller (FLC) with a standard PID for a model-based simulation of an HVAC system in Simulink for different conditions using real building measurement data. The end result is the implementation of the developed methods in a newly designed universal control board for air handling units (AHU). The proposed FLC achieves better integral control quality indicators (IAE, ISE, ITAE, ITSE) by at least 27.4%, and smaller supply air temperature variation; the daily mean square error (MSE) was reduced by an average of 36%, which leads immediately to better occupant comfort and a presumed reduction in energy consumption. Compared to the untuned PID, energy consumption was 12.7% lower; this will ensure improved economy from the lowest level, and paves the way for interoperability with high-level energy management schemes.

Harmonic Distortion Characteristics Generated by Heating Ventilation Air Conditioning System Case Study in PCR Laboratory

Good power quality is required in health facilities since the increasing use of microprocessor-based equipment. Poor power quality in the electric power system can cause medical equipment in health care centers to malfunction and give incorrect medical diagnoses. Since 2020 we are facing a new type of Virus (Covid-19), this virus requires special laboratory construction with specific air conditioning systems (temperature, humidity, and pressure) to process specimens. This paper presents measurement results of harmonic distortion characteristic by Variable Frequency Drive (VFD) of Heating Ventilation Air Conditioning (HVAC) system in PCR Laboratory at Clinic A. The VFD in this system is used to adjust the rotation of the exhaust fan and outdoor unit. The measured voltage, current and power are used to assess power quality. The main power quality problems found in medical facilities are voltage flicker, neutral currents, and total harmonic distortion (THD) values. The measurement of total harmonic distortion (THD) is used to find the source of the harmonics. Potential problems can be identified within the facility. The results of this study can be used to develop, test, and validate the system that has been used.

Energy-Constrained Indoor Air Quality Optimization for HVAC System in Smart Building

The heating, ventilation, air conditioning (HVAC) system is a common ventilation system applied to an indoor environment based on Internet of Things (IoT) technology. In HVAC, providing a comfortable temperature under the constraint of energy is a major challenge. In this article, we propose an air quality optimization strategy to control the air supply and energy consumption, and build several dynamic models to capture the stochastic processes in HVAC. Besides, a system utility maximization problem is formulated. We provide a solution framework based on the Lyapunov optimization method. Based on this framework, we propose a utility-optimal air quality optimization algorithm to solve the subproblem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the upper bound of the indoor temperature is derived, and the optimality of the algorithm is analyzed. Simulation results show the impact of the system parameter on the HVAC system and the indoor temperature, and verify that the proposed strategy and methods can maintain the comfortable temperature range and supply more fresh air effectively under the constraints of energy consumption.

A New Type in TRNSYS 18 for Simulation of Borehole Heat Exchangers Affected by Different Groundwater Flow Velocities

Heating ventilating air-conditioning (HVAC) systems have been increasingly widespread in Italy: they can exploit renewable energies, are energy efficient systems, do not directly consume fossil fuels, and in the post-pandemic era, have also been subject to incentive processes by the Italian government. In South Tyrol, subject to harsh climates in both the winter and summer seasons, ground-source heat pump (GSHP) systems can be an excellent solution for the air conditioning of buildings. Unfortunately, too often, the design of HVAC systems with borehole heat exchangers (BHEs) is not adequate, and therefore, an innovative and expeditious numerical solution is proposed. A new numerical element (named Type285), written in Fortran code, was developed for TRNSYS 18 and able to implement the main features of BHEs and the surrounding aquifer. Type285 was compared with numerical models present in the literature (using hydrogeological software such as MODFLOW) and validated with the experimental data. The demonstration of the exchanged energy increase between the BHE and subsoil due to the increase in the groundwater flow velocity was carried out and evaluated. The choice to simulate BHE in TRNSYS using Type285 can be a fast and advantageous solution for HVAC system design.

Assessment of a desiccant cooling system in a traditional and innovative nanofluid HVAC system

The topic of energy saving is a constant in everyday life, and it is widespread all over the world. Space heating using solar panels is the most used renewable source of energy, but the application of solar energy for cooling the fluids used for refrigeration is growing very fast. Among the techniques used for refrigeration, this work focused on Desiccant Cooling. In particular, with the use of dynamic simulation software, it was possible to study the heat supplied and the energy consumption of a Heating Ventilation Air Conditioning (HVAC) system of a university building and to compare consumption with those of a Desiccant Cooling system applied to the same building. Four different cases were simulated: two related to the HVAC system, one of which operates with water and glycol and the other one with nanofluid, and the other ones to the Desiccant Cooling system with both types of fluids mentioned above. Keeping the same energy demand of the building in all the simulations, it was found that in summer the Desiccant Cooling system had higher performance than the traditional HVAC system and that the use of the nanofluid in both types of conditioning systems further increased the performance of 21%. Simulations were carried out using TRNSYS software.

A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates

This paper proposes a novel methodology to assess the yearly Heating Ventilation Air Conditioning (HVAC) energy costs and indoor comfort levels for indoor spaces. The methodology involves a weather clustering technique coupled with a simulation-based multi-objective optimization for HVAC systems operation control. The clustering technique is utilized to determine representative days that capture the yearly variability of outdoor air temperature, total solar radiation on horizontal surface, wind speed, and outdoor relative humidity from historical time series. The optimization framework then determines the optimal cooling operation strategies that simultaneously minimize energy consumption cost and thermal discomfort for each representative day. Such clustering-based approach, particularly, enables the assessment of the annual operation of the HVAC using representative daily weather conditions while avoiding the high computational costs of a day-by-day optimization. The numerical prospects of the proposed framework are illustrated using an office building located in Saudi Arabia, i.e., under subtropical desert conditions. The results show that the proposed methodology can achieve reductions of up to 17.6% and 19.4% in annual cooling consumption cost and thermal discomfort, respectively, compared to standard baseline policies.

Energy-Optimal Structures of HVAC System for Cleanrooms as a Function of Key Constant Parameters and External Climate

This article presents approximating relations defining energy-optimal structures of the HVAC (Heating, Ventilation, Air Conditioning) system for cleanrooms as a function of key constant parameters and energy-optimal control algorithms for various options of heat recovery and external climates. The annual unit primary energy demand of the HVAC system for thermodynamic air treatment was adopted as the objective function. Research was performed for wide representative variability ranges of key constant parameters: cleanliness class—Cs (ISO5÷ISO8), unit cooling loads—q˙j (100 ÷ 500) W/m2 and percentage of outdoor air—αo (5 ÷ 100)%. HVAC systems are described with vectors x¯ with coordinates defined by constant parameters and decision variables, and the results are presented in the form of approximating functions illustrating zones of energy-optimal structures of the HVAC system x¯* = f (Cs, q˙j, αo). In the optimization procedure, the type of heat recovery as an element of optimal structures of the HVAC system and algorithms of energy-optimal control were defined based on an objective function and simulation models. It was proven that using heat recovery is profitable only for HVAC systems without recirculation and with internal recirculation (savings of 5 ÷ 66%, depending on the type of heat recovery and the climate), while it is not profitable (or generates losses) for HVAC systems with external recirculation or external and internal recirculation at the same time.

Life cycle assessment of an experimental solar HVAC system and a conventional HVAC system

Solar heating ventilating air conditioning systems are useful tools to meet the objectives of the European Commission in terms of sustainability in buildings, since their use can reduce the environmental impact, including CO2 emissions, due to their low energy consumption. In order to quantify the improvement that in environmental terms the use of this type of system could entail, in this work it was carried out (a) a comparative life cycle assessment of a solar heating ventilating air conditioningsystem based on evaporative cooling and desiccant wheel with a conventional direct expansion system; and (b) an analysis of feasible modifications of the desiccant wheel based system and their influence on the life cycle analysis results. The experimental desiccant wheel based system showed a slightly higher environmental performance than the conventional direct expansion based system, between 2% and 10%, for the 3 impact categories evaluated: human health, ecosystem quality and resource consumption. When weight optimisation and the reuse of materials were considered, the environmental performance of the experimental based system became even up to between 22% and 50% higher than that of the conventional direct expansion based system. That involved a 60 % reduction in climate change potential indicator, which mainly was influenced by CO2 emissions.

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

A Two-Layer Real-Time Optimization Control Strategy for Integrated Battery Thermal Management and HVAC System in Connected and Automated HEVs

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework.

Numerical Evaluation of a HVAC System Based on a High-Performance Heat Transfer Fluid

Nanofluids have great potential to improve the heat transfer properties of liquids, as demonstrated by recent studies. This paper presents a novel idea of utilizing nanofluid. It analyzes the performance of a HVAC (Heating Ventilation Air Conditioning) system using a high-performance heat transfer fluid (water-glycol nanofluid with nanoparticles of Al2O3), in the university campus of Lecce, Italy. The work describes the dynamic model of the building and its heating and cooling system, realized through the simulation software TRNSYS 17. The use of heat transfer fluid inseminated by nanoparticles in a real HVAC system is an innovative application that is difficult to find in the scientific literature so far. This work focuses on comparing the efficiency of the system working with a traditional water-glycol mixture with the same system that uses Al2O3-nanofluid. The results obtained by means of the dynamic simulations have confirmed what theoretically assumed, indicating the working conditions of the HVAC system that lead to lower operating costs and higher COP and EER, guaranteeing the optimal conditions of thermo-hygrometric comfort inside the building. Finally, the results showed that the use of a nanofluid based on water-glycol mixture and alumina increases the efficiency about 10% and at the same time reduces the electrical energy consumption of the HVAC system.

Phase Change Material Integration in Building Envelopes in Different Building Types and Climates: Modeling the Benefits of Active and Passive Strategies

Among the adaptive solutions, phase change material (PCM) technology is one of the most developed, thanks to its capability to mitigate the effects of air temperature fluctuations using thermal energy storage (TES). PCMs belong to the category of passive systems that operate on heat modulation, thanks to latent heat storage (LHS) that can lead to a reduction of heating ventilation air conditioning (HVAC) consumption in traditional buildings and to an improvement of indoor thermal comfort in buildings devoid of HVAC systems. The aim of this work is to numerically analyze and compare the benefits of the implementation of PCMs on the building envelope in both active and passive strategies. To generalize the results, two different EnergyPlus calibrated reference models—the small office and the midrise apartment—were considered, and 25 different European cities in different climatic zones were selected. For these analyses, a PCM plasterboard with a 23 °C melting point was considered in four different thicknesses—12.5, 25, 37.5, and 50 mm. The results obtained highlighted a strong logarithmic correlation between PCM thickness and energy reduction in all the climatic zones, with higher benefits in office buildings and in warmer climates for both strategies.

Estimating Number and Dwell Time of Visitors in a Large-scale Indoor Space from HVAC Data and Its Evaluation in Different Seasons

The aim of this paper is to estimate the number and dwell time of visitors in a large-scale indoor space or room with a common heating-ventilation-air conditioning (HVAC) system that includes sensors for CO2 and indoor temperature in any season. Previous studies tried to estimate the number and dwell time of visitors from CO2 concentration in small rooms with or without a HVAC system. However, in a large-scale indoor space with large air-conditioning and ventilation systems, the number and dwell time of visitors are difficult to estimate for three reasons: 1) CO2 concentration changes much more slowly than the number and dwell time of visitors and with a delay, 2) the difference in changes is affected by the amount of ventilation, and 3) this difference may be affected by operation of HVAC, which is affected by seasonal climate. To solve these problems, we proposed partial modeling with a variable time window. This method can make a partial estimation model that automatically corresponds to differences in the change speed between two variables: visitors and CO2 concentration. We demonstrate the effectiveness of our proposal using measured sensor data in summer, fall, and winter to clarify its feasibility in different seasons in Japan.

A study on evaluation systems for optimized design GSHP and ES technologies used in HVAC systems

Abstract Due to the lack of suitable evaluation systems for heating, ventilation, air-conditioning (HVAC) systems with ground source heat pump (GSHP) and energy storage (ES) technologies, it is difficult to design a building HVAC system to achieve optimized design with regard to investment cost, energy saving and environmental protection. This is a study on developing a fuzzy evaluation system by including GSHP and ES with comprehensively determined weights into an HVAC system. A questionnaire method was used and the answers from 21 HVAC experts were analyzed to facilitate the modeling. Taking a commercial integrated project in Nanjing as an engineering case, the evaluation system was tested. If the system with a comprehensive merit value of 0.303 is adopted, the annual operating cost is reduced by 32.5%, the annual total life cycle cost is reduced by 26.5% and the primary energy consumption and carbon emissions are reduced by 10.5%, with the initial investment increased by 6.5%. This study revealed that the newly developed evaluation system is very useful for realizing the optimal design of HVAC systems.

Optimization of intelligent heating ventilation air conditioning system in urban building based on BIM and artificial intelligence technology

The study aims to effectively reduce building energy consumption, improve the utilization efficiency of building resources, reduce the emission of pollutants and greenhouse gases, and protect the ecological environment. A prediction model of heating ventilation air conditioning (HVAC) energy consumption is established by using back propagation neural network (BPNN) and adapted boosting (Adaboost) algorithm. Then, the HVAC system is optimized by building information modeling (BIM). Finally, the effectiveness of the urban intelligent HVAC optimization prediction model based on BIM and artificial intelligence (AI) is further verified by simulation experiments. The research shows that the error of the prediction model is reduced, the accuracy is higher after the Adaboost algorithm is added to BPNN, and the average prediction accuracy is 86%. When the BIM is combined with the prediction model, the HVAC programme of hybrid cooling beam + variable air volume reheating is taken as the optimal programme of HVAC system. The power consumption and gas consumption of the programme are the least, and the CO2 emission is also the lowest. Programme 1 is compared with programme 3, and the cost is saved by 37% and 15%, respectively. Through the combination of BIM technology and AI technology, the energy consumption of HVAC is effectively reduced, and the resource utilization rate is significantly improved, which can provide theoretical basis for the research of energy-saving equipment.

Design of Supervisory Model Predictive Control for Building HVAC System With Consideration of Peak-Load Shaving and Thermal Comfort

This paper proposes a design of a supervisory model predictive controller for a heating-ventilation-air-conditioning (HVAC) control system. The control objective is to minimize the operating cost and take into account of electrical load shaving and thermal comfort of users. To ensure that thermal comfort is well regulated, we utilize the Predicted Mean Vote (PMV) as an indicator and determine an acceptable bound of a desired set-point temperature. The control design consists of two layers, namely, a supervisory control (SC) layer and a model predictive control (MPC) layer. For the SC layer, we explore a configuration for the SC layer including the choice of predesign controller, the analysis of steady-state response, and the analysis of the possible range of the set-point temperature. We incorporate the effect of set-point temperature, air velocity, outside air temperature, heat load inside zone onto the HVAC electrical power. Then, we search for an optimal profile of the set-point temperature that minimizes a weighted sum of a total operating cost (TOC) and a thermal comfort cost (TCC). Moreover, exploration of trade-off between TOC and TCC helps us to achieve both control objectives efficiently. For the MPC layer, we formulate the control design with the objective of tracking the optimal set-point temperature and minimizing the control inputs. We apply the proposed control design to a complex dynamical model of HVAC system with volumetric flow rate, electrical power of heat exchanger, and removed moisture as control variables. When the allowable PMV of the zone is specified within 0.5, the TOC of the proposed supervisory MPC is reduced by 12.46% compared with that of the nominal operation. The maximum electricity demand of HVAC system is reduced by 13.43% and the electrical power profile is smoothly shaved under the proposed control scheme. Furthermore, the zone relative humidity is well regulated which corresponds to the assumption in the supervisory control.