HVAC Systems Research Articles

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

Aerodynamic Size-Dependent Collection and Inactivation of Virus-Laden Aerosol Particles in an Electrostatic Precipitator.

Electrostatic precipitators (ESPs) may enable high particle collection efficiency with minimal pressure drop in HVAC systems. However, studies of pathogen collection and inactivation in ESPs at medium to higher flow rates are limited. Here, a single-stage, wire-plate ESP operated at flow rates of 51 and 85 m3 h-1 was used to study the removal of virus-laden aerosol particles for three different airborne viruses: (1) bovine coronavirus (BCoV), (2) influenza A virus (IAV), and (3) porcine reproductive and respiratory virus (PRRSV). Size-resolved measurements of collection efficiency were obtained using Andersen cascade impactors (ACI) sampling upstream and downstream of the ESP. All measurements were analyzed based on three distinctive but complementary methods: (1) fluorimetry to assess physical collection, (2) RT-qPCR to assess viral RNA concentrations and (3) virus titration to assess virus viability. In general, log reductions by virus titration were highest followed by those from RT-qPCR, and last fluorimetry, suggesting that a portion of virus may be potentially inactivated in flight in the ESP. An effective migration (deposition) velocity ranging from 3.10 to 10.05 cm s-1 was also determined using the spatially resolved measurements of virus collection on the ESP plates.

Determination of Turbulent Prandtl Number for Thermal Fluid Dynamics Simulation of HVAC Unit by Data Assimilation

Abstract Regarding high-precision temperature field prediction of a heating ventilation and air conditioning (HVAC) unit using thermal fluid dynamics simulation, the determination of the turbulent Prandtl number (Prt) was a key issue. In this study, we present an attempt to improve the accuracy of thermal fluid dynamics simulations in HVAC units by adjusting the Prt using data assimilation techniques. First, we simultaneously measured the velocity and temperature in the hot and cold air mixing zone of a simple HVAC model using particle image velocimetry and thermocouples. Second, we coupled data assimilation to the thermal fluid simulation model to determine the Prt in the mixing field. Finally, we proposed two functions of the Prt for high velocity and low velocity regions using multidimensional analysis. In the future, we believe that the Prt functions can be applied to thermal fluid simulations of actual HVAC unit to accurately predict performance without conducting prototype experiments, thereby contributing to reducing development cost and time of HVAC unit.

Progressive Dies for L-hanger Ducting (L-HD) Utilizing Low-Carbon Steel SPCC-SD Material: An Experimental and Numerical Analysis

Industrial developments, especially in the manufacturing and construction sectors, recognize L-hanger ducting as a critical component in HVAC (heating ventilation and air conditioning) ducting systems, which play a role in supporting and stabilizing air ducts. The L-Hanger ducting manufacturing process involves a series of stages, such as shearing, blanking, piercing, trimming, and bending processes. This research focuses on the design and simulation of dies and punches for piercing, blanking, and bending processes using 1.6 mm-thick SPCC-SD material. The aim of this research is to design and analyze progressive dies in order to increase the efficiency of the production process. A comprehensive calculation of the forces involved in the shearing, blanking, piercing, trimming, and bending processes is required in order to predict press machine tonnage requirements to support the production process. This research applies theoretical and numerical validation approaches. Theoretical analysis is used to calculate the overall forces, which are then compared with numerical results and verified through an experimental approach. By understanding and optimizing the design of progressive dies, it is hoped that we can increase the production efficiency of L-hanger Ducting and expand knowledge in the field of metal forming, contributing to the metal forming industry and supporting the development of science.

Energy, exergy, environment and economic (4E) analysis of PV/T module assisted vapor compression refrigeration system: An experimental study

Despite the rise in the prices of fossil fuels, the increase in their demand, and damaging the environment, a large part of the world's energy needs have been today met by fossil fuels. In this direction, interest in renewable energy sources has increased. Solar energy stands out among renewable energy sources because it is endless and clean. However, today, the use of solar energy is not used alone, but in combination with other thermal energy systems. In building applications, it is mostly used in heating, refrigeration, and HVAC systems, which have a high part in energy consumption. In this study, the solar energy and cooling system were not used separately as in previous studies but were used as a hybrid. The focus was on increasing the performance of both systems by operating them together. In this study, energy, exergy, thermoeconomic, and environmental analyses were applied to the PV/T-assisted vapor compression refrigeration system (PV/T-VCRS) at different storage temperatures (25 °C, 30 °C, and 35 °C). As a result, an 8.5 % lower surface temperature of the module in PV/T-VCRS 25 °C was measured compared to PV/T-VCRS 30 °C and 35 °C. In direct proportion to the module surface temperatures, 13 % better electrical efficiency was obtained in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. The COP value increased by 15.46 % in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. A 13 % improvement in exergy efficiency was observed in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. The enviroeconomic parameter PV/T-VCRS is calculated as 15.17 ¢/h, 16.52 ¢/h, and 17.6 ¢/h for 25 °C, 30 °C and 35 °C. Another advantage of the system is that hot water is obtained at set temperatures. In PV/T-VCRS, 475 L, 300 L, and 210 L of hot water were obtained at 25 °C, 30 °C, and 35 °C, respectively. As a result, the performance of the PV/T-VCRS was good, with the added benefit of performing close to the performances when PV/T and VCRS were used separately.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Energy recovery ventilators to combat indoor airborne disease transmission: A sustainable approach

Ventilation plays a crucial role in preventing indoor airborne disease transmission. Nevertheless, ventilation increases the energy consumption of HVAC systems. Therefore, energy efficiency measures or alternative methods must be adopted to reduce the energy demand of HVAC systems, which is necessary to achieve sustainability in the building sector. This study proposes a method of utilizing an energy recovery ventilator (ERV) to provide supplementary ventilation to reduce airborne disease transmission. The proposed method is tested for an office building with one source room (with an infected occupant) and two connected rooms (no infection source). The contributions of the present study are (i) the development and verification of a new supplement ventilation method using an ERV to reduce the probability of infection from airborne pathogens and (ii) providing the economic and environmental benefits of the proposed method to promote its adaption by the building managers/HVAC engineers. The results of the present study show that the proposed method can reduce the probability of infection by 10 to 40% and demonstrate that utilizing an ERV is a sustainable and economical method to improve ventilation to reduce indoor airborne disease transmission.

Performance simulation and energy efficiency analysis of multi-energy complementary HVAC system based on TRNSYS

The failure rate and operating cost of the C-B (chiller-gas boiler) system, which has been in use for a long time, are increasing year by year, but there is a lack of reasonable programs and effective measures on how to retrofit the C-B system. In order to explore the prospect of ground-source heat pumps applied to the retrofit of C-B systems in HSCW (hot summer and cold winter) zone, this study designed a G-C-B (GSHP-Chiller-Boiler) system in a partial retrofit context and constructed a TRNSYS numerical simulation model of the retrofit system. Secondly, the performance and energy efficiency of the system is analyzed as well as different control strategies for the ground source heat pump cooling tower are discussed. Finally, the energy conversion efficiency of the chiller is compared based on the energy flow of the system. Studies have shown that the G-C-B system can achieve a seasonal performance factor of 5.3 during the cooling season and 4.1 during the heating season. Compared to the existing C-B system, year-round electricity consumption was reduced by 21 %, gas consumption by 92 %, and combined energy demand by 49.6 %. With the addition of GSHP, the energy conversion efficiency of the chiller has also increased by 21 %. The soil temperature of the G-C-B system increased by less than 0.5 °C after ten years of continuous operation. This paper can provide a valuable theoretical basis for the energy form modification of the C-B system in the HSCW zone.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Adaptive neural network control of a centrifugal chiller system

In this paper, a neuro-adaptive PI control strategy for a water-cooled centrifugal chiller system is developed, and its control performance is examined. The system model consists of a flooded evaporator, a flooded condenser, a centrifugal compressor, and an electronic expansion valve. The overall system consists of three control loops: a compressor speed control loop, an inlet guide vane (IGV) control loop, and a condenser liquid level control loop. A neuro-adaptive control strategy for compressor speed control was designed. The control performance of the chiller was tested by carrying out simulation runs using an integrated building and HVAC (IB-HVAC) system model. The major details of the IB-HVAC model are described in this paper. Results show that the neuro-adaptive controller can adapt to new system dynamics of the IB-HVAC system and give good setpoint tracking responses under a wide range of operating conditions. The results show that the neuro-adaptive controller performs better than the constant gain PI controller in terms of speed of response and setpoint tracking properties.

Comprehensive performance evaluation of HVAC systems integrated with direct air capture of CO2 in various climate zones

Direct Air Capture (DAC) is a rapidly evolving technology that extracts CO2 directly from ambient air. This study presents a comprehensive performance evaluation of integrating DAC in HVAC systems, which can reduce indoor CO2 concentration and improve energy efficiency of HVAC systems. The DAC equipment is modeled in Modelica based on isotherm and thermodynamic equations, and pressure drop curves of the CO2 sorbent described in literature. The model is validated with data from the literature, and then integrated into a typical HVAC system available in Modelica Buildings library. The HVAC system is a Variable Air Volume (VAV) with reheater system for a one-floor office building with standard ASHRAE 2006 control sequences. Demand control ventilation strategies are designed to reduce the outdoor air flowrates when indoor CO2 concentrations are lower than the threshold, which is to maximize the benefits of integrating DAC. Four cases are proposed to assess the impacts of integrating DAC and DCV in HVAC systems in 8 different ASHRAE climate zones in the USA. The results show that by integrating DAC unit into the HVAC system, the average indoor CO2 concentration can be significantly reduced by over 45 % against the baseline without a DAC unit. By integrating DCV, 0.39–21.66 % of annual energy savings and 226–9539 kg carbon emissions reduction are observed across different climate zones. The highest energy savings are found to be achieved with cold climatic conditions while the lowest energy savings occur with favorable weather.

Identifying untraced faults associated with high return temperatures from heating systems in buildings connected to district heating networks

The district heating (DH) system is in its transition towards the 4th generation district heating (4GDH), and the high DH return temperatures need to be addressed during the process. In the existing building heating systems, many faults, malfunctions, or sub-optimal operations can lead to high DH return temperatures. However, the field of fault detection and diagnostics (FDD) within heating systems is notably under-researched in contrast to their counterparts in ventilation and air conditioning in building HVAC systems. This divergence can be attributed to several factors, including limited digital integration, a scarcity of data, and the non-obvious nature of faults in heating systems. In this study, we utilized heat cost allocators (HCA) and energy meters to investigate the features and potential impacts of four untraced faults that can lead to high district heating (DH) return temperatures in both space heating and domestic hot water systems in large buildings. We identified component-level faults, including heat exchanger overflow, space heating temperature controller failures, and excessive operating temperatures due to bypass, as well as system-level issues such as non-uniform heat distribution in buildings and its impacts. This exploration provides new, critical insights for advancing FDD research in HVAC and DH systems.

Monitoring airborne pathogen transmission for enhanced safety at food processing facilities

AbstractFood manufacturing and processing are part of the nation's critical infrastructure. Due to the recent global spread of the SARS-CoV-2 virus, the potential contamination of the food chain and the resulting public health implications are of high consequence to society. The current primary food manufacturing and processing facilities already have various mechanisms such as hazard analysis and critical control point (HACCP) system in place. However, the widespread microbial infections in these facilities raise concerns that they will not only threaten the welfare of food processing workers, but also have a potentially greater consequence on the public if the food is contaminated with an infectious agent.Despite the increasingly recognised role of the environment in the spread of microbes, the effect of air properties remains poorly understood. Heating, ventilation, and air conditioning (HVAC) systems in meat processing facilities not only provide a means of transport for viruses and bacteria but may also deposit them on surfaces where they can survive for days. To maintain a stable and safe food chain supply during the pandemic, the challenges to ensure safe food supply and protect the workers' health must be quickly addressed through sustainable, safe and economic approaches. With these two imminent challenges in mind, the overall goal of this review article is to provide a comprehensive overview of the role of the environment in the impaction and resuspension of bioaerosols, focusing on airborne bacteria and viruses. The review includes the latest results of modeling the spread of microbial aerosols in the airflow and the development of preventive measures to mitigate virus contamination in the unique environment of meat processing operations. By understanding how the environmental factors and seasonality affect the infectivity and spread of airborne pathogens, mitigation measures can be designed to minimise future infections within and beyond these facilities.

Thermoelectric Generator Applications in Buildings: A Review

With growing concerns about building energy consumption, thermoelectric generators (TEGs) have attracted significant attention for their potential to generate clean, green, and sustainable power. This comprehensive review explores the applications of thermoelectric generators (TEGs) in building systems, focusing on recent advancements from 2013 to 2024. The study examines TEG integration in building envelopes, including façades, walls, windows, and roofs, as well as non-integrated applications for waste heat recovery and HVAC systems. Key findings highlight the potential of TEGs in energy harvesting and thermal management, with façade-integrated systems generating up to 100.0 mW/m² and hybrid LCPV/T-TEG systems achieving overall efficiencies of 57.03%. The review also identifies critical parameters affecting TEG performance, such as solar intensity, thermoelectric arm length, and PCM melting temperature. Despite promising results, challenges remain in improving overall system efficiency, cost-effectiveness, and scalability. Future research directions include developing more efficient thermoelectric materials, optimizing system designs for various climatic conditions, and exploring integration with smart building management systems. This review provides valuable insights for researchers and practitioners working towards more energy-efficient and sustainable building designs using TEG technology.

Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm

Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm

Comparative Analysis of Cooling Load Calculations: CLTD Method vs. Carrier HAP 5.01 Software for Hotel HVAC Design

This study examines the cooling load requirements of a hotel building by comparing two methodologies: the traditional Cooling Load Temperature Difference (CLTD) method and the Carrier Hourly Analysis Program (HAP) 5.01 software. The primary objective is to validate the accuracy and reliability of these methods in calculating cooling loads across different room types, from standard rooms to larger, more complex suites. The results show that the CLTD method consistently yields higher cooling load estimates, with discrepancies ranging from 3% to 14% compared to HAP 5.01 calculations. These differences are most significant in larger rooms, such as suites and owner’s suites, which have more extensive glass areas, higher occupancy, and more heat-generating equipment. The findings indicate that while the CLTD method is valuable for quick, preliminary estimates, the HAP 5.01 software provides a more precise and comprehensive analysis, taking into account hourly variations, equipment schedules, and other factors that impact cooling loads. This research highlights the need for careful selection of the appropriate calculation method to ensure the efficient design of HVAC systems, maximizing energy efficiency, and maintaining occupant comfort. The study concludes that for projects requiring high accuracy, particularly in complex or large spaces, dynamic simulation tools like HAP 5.01 are preferable. Detailed cooling load results and comparisons are provided in the supplementary documentation, offering further insights into the analysis and its implications for HVAC design.

Thermal performance assessment of sandwich wall panel fabricated with polyurethane foam and natural husk

The demand for enhancing thermal efficiency in building construction has accelerated the evolution of sandwich wall panels in recent years. In this study, the novel sandwich panel is developed using Polyurethane (PU) as core and PU replaced with Rice husk (RH) and Coconut Husk (CH) at varying percentage (10, 20, 30, 40 and 50 %) with calcium silicate board as outer material. Dimension of sandwich panel is 30 × 30 cm with 3 cm as a core thickness is tested for compressive strength. The panel owing highest compressive load with combination of Polyurethane foam Coconut Husk panel at 20 % (PUC3–20 %) and polyurethane foam Rice husk panel at 30 % (PUR3–30 %) was taken further for thermal analysis. The temperature study was carried out on a prototype model using IR thermographic camera showing the temperature difference of indoor and outdoor of the sandwich panel. The PUR3–30 % shows the thermal conductivity of 0.28 w/mk and the difference of thermal heat flux was 10.42 W/m2. Regression analysis was carried out to validate the experimental results and the correlation accuracy of R2 > 0.90 was obtained. A residential building (G+3) was created in Autodesk Revit to conduct an energy analysis audit, where all the data of fabricated sandwich panel was added as new material and the analysis was carried out in green building studio. The annual energy and annual fuel end use for Heating Ventilation and Air Conditioning (HVAC) has been decreased by 3.5 and 4.5 % using PUC3–20 % and PUR3–30 % panels compared to the base material.

Investigating the influence of uncertainty on office building energy simulation through occupant-centric control and thermal comfort integration

Buildings with occupant-centric control (OCC) systems can adjust heating, ventilation, and air conditioning (HVAC) operations based on occupant status to minimize unnecessary energy use. However, because occupant behavior is stochastic rather than constant, and preferred setpoints can vary across occupant groups, this randomness significantly impacts energy use in buildings with OCC systems.This study used a stochastic simulation approach to model the uncertainty inherent in occupancy patterns and evaluate its impact on the energy consumption of an office building with OCC. In this case, the setpoint temperature was determined based on the Predicted Mean Vote (PMV) method so that each zone could be controlled to the preferred temperature according to the occupant group.The results showed that the uncertainty of the annualized sum of electricity consumption for heating and cooling was 4.3 % and 1.8 %, respectively, with 95 % confidence intervals. Notably, a negative correlation was observed between the number of occupants and the uncertainty of energy consumption. Thus, as the uncertainty in the setpoint temperature increased during periods of low occupancy, the uncertainty in the energy consumption of the HVAC system also tended to increase. These insights underscore the significant influence of occupancy-related factors on building energy utilization.