HVAC Systems Research Articles

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Development of Competency Instrument for HVAC Maintenance Technical Executor of Oil and Gas Industry in Malaysia

Heating, ventilating, and air-conditioning (HVAC) are critical mechanical systems maintained in the oil and gas (O&G) industry. However, there are no guidelines to define and measure the competencies of the personnel who execute HVAC maintenance in the O&G industry in Malaysia. The research objective is to develop a competency instrument for HVAC maintenance technical executors in the O&G industry in Malaysia. A questionnaire survey was designed in the study by using a quantitative approach. The developed instrument has been distributed to 163 respondents using cluster sampling based on four regions. The analysis results through Winstep Software V.3.69.1.11 identified 19 competencies with 119 sub-competencies in technical skill, three competencies with 30 sub-competencies in non-technical skill, and four competencies with 18 sub-competencies in core personality. These competency instruments were pertinent to academic and professional communities based on perspectives from the industry. For practical purposes, the HVAC competency instruments may assist the employees in their career advancement to be competent.

Heat Transfer Performance Factors in a Vertical Ground Heat Exchanger for a Geothermal Heat Pump System

Ground heat pump systems (GHPSs) are esteemed for their high efficiency within renewable energy technologies, providing effective solutions for heating and cooling requirements. These GHPSs operate by utilizing the relatively constant temperature of the Earth’s subsurface as a thermal source or sink. This feature allows them to perform greater energy transfer than traditional heating and cooling systems (i.e., heating, ventilation, and air conditioning (HVAC)). The GHPSs represent a sustainable and cost-effective temperature-regulating solution in diverse applications. The ground heat exchanger (GHE) technology is well known, with extensive research and development conducted in recent decades significantly advancing its applications. Improving GHE performance factors is vital for enhancing heat transfer efficiency and overall GHPS performance. Therefore, this paper provides a comprehensive review of research on various factors affecting GHE performance, such as soil thermal properties, backfill material properties, borehole depth, spacing, U-tube pipe properties, and heat carrier fluid type and velocity. It also discusses their impact on heat transfer efficiency and proposes optimal solutions for improving GHE performance.

An artificial neural network approach for out‐of‐control stream identification in multiple stream processes

AbstractA multiple stream process (MSP) is a process at a point in time that generates several streams of output with quality variables and specifications that are identical in all streams. Statistical process control (SPC) schemes for MSPs are designed to issue an out‐of‐control (OC) signal when a possible change occurs from the in‐control (IC) state of the process. However, the SPM literature for MSPs lacks contributions regarding post‐hoc identification of the stream or group of streams responsible for an OC signal. In this article, we propose an artificial neural network (NN) specifically trained for this purpose and, through a Monte Carlo simulation, show its superiority in correctly identifying OC streams, since an OC signal has been correctly issued. The research is motivated by the need for a post hoc diagnosis of the heating, ventilation, and air conditioning (HVAC) systems installed on modern train coaches, generating multiple streams of quality variables of interest. A case study in this area based on the HVAC data (openly available online at https://github.com/unina‐sfere/NN4OCMSP) illustrates the practical applicability of the proposed approach, which is implemented in the Python package NN4OCMSP and published on the PyPI software repository.

Model predictive control with self-learning capability for automated demand response in buildings

This paper presents an optimal management strategy, called Building Optimizer, based on a Model Predictive Control (MPC) approach with self-learning capabilities for buildings. The proposed MPC is a key enabler of cooperative demand response strategies at community level, ensuring the allocation of an optimal demand profile at each participating member of the community according to an optimal consumption reference defined by a complementary agent at community level. In this way, the proposed solution can contribute to provide demand response services exploiting the unlocked residential flexibility. The MPC calculates the optimal setpoints of the HVAC system’s terminal units, considering the expected usage of the buildings and the outdoor conditions, and exploiting the building’s thermal inertia. The models embedded in the MPC are grey-box models representing a thermal zone of the building. An adaptive MPC is proposed to mitigate the high uncertainty present in building management due to the time-variant characteristics of the building and the uncertain internal heat gains due to occupancy and other disturbances. For that, the reduced models incorporate self-learning capabilities implemented as Moving Horizon Estimators that perform a continuous calibration based on real-time measurements. This solution allows full automation for model calibration and management of the terminal units. This paper presents a case study to assess the thermal comfort assurance and power flexibility provision potential of the proposed solution. First, the Building Optimizer is compared to a regular benchmark consisting of an on–off controller, evaluating the performance of each controller for indoor temperature tracking. For the assessment of the flexibility provision, a baseline MPC with fixed model parameters obtained by an offline calibration is used for comparison to the Building Optimizer with self-learning capabilities. Different flexibility scenarios are simulated, considering different restrictions regarding thermal comfort and various flexibility request signals. Notably, the Building Optimizer outperforms the baseline MPC in all scenarios, particularly guaranteeing thermal comfort. Incorporating self-learning capabilities enhances controller performance by mitigating the effect of uncertainty, allowing the Building Optimizer to shift up to 13.08% of peak periods into valley periods, and tracking a flexibility request scenario of 10% with no discomfort.

Performance Analysis of a HVAC System with a Heat Recovery Wheel for a Hospital Building

Performance Analysis of a HVAC System with a Heat Recovery Wheel for a Hospital Building

Performance and PV benefits analysis of multi-source renewable energy systems for different types of buildings on university campus

Due to the large campus area and high personnel density, universities experience significant building energy consumption. The comprehensive utilization of renewable energy can greatly alleviate energy consumption in higher education institutions. To explore the application prospects of renewable energy systems in universities, this study takes a university in Wuhan as a case example, investigating the performance and potential of various types of Photovoltaic-assisted Heating, Ventilation, and Air Conditioning (PV-HVAC) systems. First, the building load was calculated using DeST software. Subsequently, the potential for solar energy application on campus was assessed using a combination of QGIS, Labelme, and Python. Moreover, three types of PV-HVAC systems were designed for five categories of buildings based on their distinct characteristics, and TRNSYS software was utilized for performance simulation and analysis. The results indicate that the annual performance coefficients of the three systems are 3.8, 3.2, and 4.5, with corresponding annual energy self-sufficiency rates of 114 %, 72 %, and 32 %, respectively. Finally, the overall energy self-sufficiency rate of the PV-HVAC system across the campus is 65 %. This research provides valuable insights for achieving nearly zero-energy HVAC systems in university campuses.

Prediction of train interior noise due to the HVAC system

In recent years, the significance of acoustic comfort in rail vehicles has grown considerably, owing to its direct impact on passenger well-being. As a consequence, interior noise levels are progressively becoming more restrictive. Meeting these requirements is a key challenge for rolling stock manufacturers. The predominant source in stationary condition is the HVAC (Heating, Ventilation, and Air Conditioning) system. To effectively assess and potentially reduce the noise generated by the HVAC system, the availability of tools and methodologies for predicting the propagation of noise through the air ducts and interior noise levels in the passenger compartment during the design phase is crucial. This paper presents a simulation methodology for predicting interior noise levels by evaluating various alternatives within a commercial software. Input data for the simulations encompass the geometry and materials of the supply and return ducts, along with the acoustic power of each outlet measured at the source. This data is associated with the nominal operating mode of the HVAC system. Finally, the paper establishes a correlation between the simulation results and in-situ measurements conducted on the completed train, with the HVAC equipment operating under nominal conditions.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

Using geothermal energy in enhancing all-air HVAC system performance - Case study, thermal analysis and economic insights

The background is the same for addressing the twin challenges of attaining thermal comfort and concurrently reducing energy use and CO2 emissions. As a safeguard against worsening contamination, combining geothermal energy with an All-Air Heating, Ventilating, and Air Conditioning system (HVAC) with 100 % fresh air intake is also crucial in the context of the lessons learned from the COVID-19 pandemic. As a result, this study suggests using geothermal energy as a backup source to run a typical All-Air centralized HVAC system. The resulting system is referred to as a combined system from now on, and its primary goals are to lower the amount of operating energy needed while being environmentally and financially sound. In this context, the creative way of combining the suggested geothermal duct with the All-Air HVAC system possesses various insights, as demonstrated by the thorough thermal analysis and related design. Strong conclusions were derived from a thorough case study focused on Lebanon: adding 100 % fresh air intake leads to an astounding yearly energy savings of 67 %, whereas configurations with 10 % and 30 % fresh air produced energy savings of 52 % and 36 %, respectively. In the study, a geothermal multi-duct system was also suggested and looked at. The research revealed that the 311-m length of the single geothermal duct connected to a pressure of 7000 Pa was reduced to 210 m and the pressure drop was lowered to 140 Pa when a geothermal multi-duct system was employed. Furthermore, as the computed payback period makes clear, the integrated system presents an appealing chance to drastically cut resource usage during energy consumption, lower CO2 emissions, and provide 100 % fresh air circulation.

A loudness evaluation test of environmental noises with complex frequency characteristics: a comparison of A-weighted sound pressure level and Moore-Glasberg loudness level

Noises generated from HVAC systems and machinery have complex frequency characteristics that include various frequency components such as tonal components. In order to evaluate and cope with these environmental noises appropriately, it is necessary to apply a noise evaluation index that fits well with psychological responses. In this study, the authors focused on loudness, the most primary aspect of psychoacoustics, and conducted a evaluation experiment using actual recorded sounds. The results of the loudness evaluation were examined using two noise evaluation indexes: A-weighted sound pressure level and Moore-Glasberg loudness level dictated by ISO532-2. As a result, the Moore-Glasberg loudness level showed better agreement with the evaluated loudness regardless of the type of the sound stimuli.

Influence of characteristic lengths on the aeroacoustic properties of in-duct tube bank configurations

The flow over circular cylinders or tubes arranged in different configurations is widely employed in various engineering applications, ranging from combustion systems to HVAC systems. The Reynolds number serves as the governing parameter defining the flow around the tubes, determining the separation of the flow from the tube surface, and establishing a separation point. This separation point, in turn, dictates the formation of a jet with parameters such as jet height, jet velocity, and jet length. The jet formation, influenced by varying Reynolds numbers, introduces the possibility of various characteristic lengths, including the diameter of the tubes, height of the duct, gap between adjacent tubes, jet height, and jet length. This study aims to explore the profound influence of these characteristic lengths on the aeroacoustic properties and the flow acoustic coupling at cylinders in cross flow. The jet parameters are derived from the analysis of experimental and analytical modeling data. Through a systematic investigation, we examine the interdependencies between these characteristic lengths and the resulting aeroacoustic properties.

Energy dataset of Frontier supercomputer for waste heat recovery

The Hewlett Packard Enterprise–Cray EX Frontier is the world’s first and fastest exascale supercomputer, hosted at the Oak Ridge Leadership Computing Facility in Tennessee, United States. Frontier is a significant electricity consumer, drawing 8–30 MW; this massive energy demand produces significant waste heat, requiring extensive cooling measures. Although harnessing this waste heat for campus heating is a sustainability goal at Oak Ridge National Laboratory (ORNL), the 30 °C–38 °C waste heat temperature poses compatibility issues with standard HVAC systems. Heat pump systems, prevalent in residential settings and some industries, can efficiently upgrade low-quality heat to usable energy for buildings. Thus, heat pump technology powered by renewable electricity offers an efficient, cost-effective solution for substantial waste heat recovery. However, a major challenge is the absence of benchmark data on high-performance computing (HPC) heat generation and waste heat profiles. This paper reports power demand and waste heat measurements from an ORNL HPC data centre, aiming to guide future research on optimizing waste heat recovery in large-scale data centres, especially those of HPC calibre.

Improving solar HVAC performance by incorporating sutterby SiO2–Cu/C3H8O2 hybrid nanofluid flow via a collocation based Gegenbauer wavelets technique: Effects of electromagnetic control and Smoluchowksi's temperature slippage

AbstractThe utilization of solar energy in heating, ventilation, and air conditioning (HVAC) systems has gained significant attention as a sustainable and environmentally friendly solution to meet the increasing energy demands. Therefore, this research work introduces a novel engineering study that explores solar‐HVAC systems. However, the study utilized hybrid nanofluids (HNFs) consisting of silicon dioxide and copper nanoparticles, with propylene glycol serving as the base fluid. Furthermore, this exploration features a magnetohydrodynamics (MHD) driven rotating flow with activation energy to enhance the heat transfer performance of solar‐HVAC systems. To increase the model novelty, 3D mathematical models were developed to characterize boundary conditions, which include the speed slippery and Smoluchowski temperature slippage. The partial differential equations in the model are transformed into ordinary differential equations (ODEs) through the use of similarity transformations. The Wavelets and Gegenbauer wavelets method in Mathematica was utilized to solve ODEs and investigate physical attributes such as plate friction, Nusselt number, Sherwood number, and mass flux. The findings show that the solar thermal radiation and magnetic field improve the thermal transfer efficiency of solar‐HVAC. Therefore, the findings of the research have practical applications in solar energy for HVAC systems and can contribute to technological improvements in the manufacturing industry.

Optimizing energy efficiency through building orientation and building information modelling (BIM) in diverse terrains: A case study in Pakistan

Building orientation plays a key role in enhancing the built environment's energy efficiency through the application of Building Information Modelling (BIM) and energy simulation. Due to their significant global energy consumption, buildings are the focus of energy optimization efforts. This study explores how building orientation, when combined with modern methods like BIM and energy modelling, affects energy usage and savings throughout Pakistan's numerous geographic terrains. The methodology is quantitative, beginning with an extensive review of literature on energy analysis, BIM applications, and urban building energy modelling. Autodesk Insight 360 is employed for energy simulations, which enables the precise prediction of energy consumption by considering various factors such as building orientation, window-to-wall ratios, shading, wall and roof construction, infiltration rates, lighting efficiency, occupancy controls, plug load efficiency, and HVAC systems. Energy simulation of the data indicates that optimizing building orientation alone can result in an average energy savings of 18 %, while combining orientation optimization with improvements in window arrangements and construction materials can achieve savings of up to 30 % over 30 years. The energy simulation results were validated through case study representing typical terrains and building types (commercial and residential), with simulated energy expenditures showing a high correlation (R2 = 0.92) with actual utility bills, adjusted for local energy rates. The findings of this study highlight substantial financial benefits, with potential annual savings ranging from $2500 to $4000 for residential buildings and $10,000 to $15,000 for commercial buildings, depending on building size and location. These results suggest broader applicability to diverse geographical areas and building types in future research. This research contributes significantly to energy-efficient building practices, paving the way for sustainable construction and reduced environmental impact across the varied landscapes of Pakistan.

The Impact of Green Building Design on Indoor Air Quality and Occupant Experience

This study examines the impact of green building design on indoor air quality (IAQ) and occupant experience, using Heritage Place in Ikoyi, Lagos, as a case study. Green buildings are known for incorporating sustainable practices such as the use of eco-friendly materials, advanced ventilation systems, and biophilic design elements, all aimed at enhancing environmental sustainability and occupant well-being. A descriptive survey methodology was employed, with data gathered through a questionnaire administered to 50 occupants of the building to assess their perceptions of IAQ, health, comfort, and overall satisfaction. The findings indicate that the majority of respondents reported high levels of satisfaction with IAQ, attributing improvements in their health, productivity, and comfort to the building’s green design features. Specifically, low-emission materials and enhanced ventilation systems were highly praised for reducing pollutants and maintaining a healthy indoor environment. However, a few respondents expressed concerns about inconsistent thermal comfort in certain areas. These results underscore the importance of sustainable design in creating healthier, more productive indoor environments. Recommendations for future green building projects include fine-tuning HVAC systems to improve thermal comfort and enhancing occupant education on the benefits of sustainable features.

A Sustainable Agri-Photovoltaic Greenhouse for Lettuce Production in Qatar

Qatar identified that food supply security, including self-sufficiency in vegetable production and increasing sustainable renewable energy generation, is important for increasing economic and environmental resiliency. Very favorable solar energy resources in Qatar suggest opportunities to simultaneously meet this goal by integrating solar energy generation and food production. This study examines the feasibility of developing a sustainable agri-photovoltaic (APV) greenhouse design. A comprehensive greenhouse with solar energy generation included is developed for year-round operation in Lusail, Qatar. The performance of the system is predicted by integrating meteorological data and MATLAB simulations of system components. Important design considerations included optimizing solar energy generation by fixed solar photovoltaic panels placed on the maximum available surface area of the greenhouse canopy, while balancing crop insolation and energy needs for greenhouse HVAC systems. Electrical energy is also stored in an industrial battery. Results suggest the APV greenhouse is technically and economically viable and that it could provide benefits, including enhancing food security, promoting renewable energy, and contributing to sustainable food and energy production in Qatar.

Experimental evaluations of Berkeley thermal sensation and comfort models in electric vehicle cabin under cold outdoor conditions

As the automobile industry is transitioning toward electric vehicles, manufacturers have started implementing local warmers alongside cabin heating, ventilation, and air conditioning (HVAC) systems for effective thermal comfort management. However, optimal operating strategies need to be developed for integrating local warmers with HVAC systems. Although the Berkeley models comprising local/overall thermal sensation and comfort models offer insights in this regard, they lack follow-up assessments for occupants transitioning from very cold states. In this study, Berkeley models were evaluated using two sets of experimental data collected in a transient vehicle cabin under cold outdoor conditions: test (A) with cabin HVAC alone and test (B) with both HVAC and local warmers. The findings confirm the satisfactory performances of the Berkeley models for predicting overall sensation and comfort, with a maximum root mean-squared error (RMSE) of 0.15. The local comfort model performed poorly with the original coefficients across both datasets (maximum RMSE of 1.96). Therefore, the model coefficients were regressed for the dataset from test A and validated against the dataset from test B to achieve a maximum RMSE of 0.49. With these regressed coefficients, it was observed that moving toward a neutral overall state diminished the potential to maximize local comfort. Conversely, the local sensation model showed poor agreement (maximum RMSE of 1.9); we confirmed that accurate adaptive setpoint temperatures are a prerequisite for ensuring good predictions from the model. These findings are expected to contribute toward future efforts in using Berkeley models to formulate effective local warmer–HVAC operational strategies in electric vehicles.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

Fuzzy logic-supported building design for low-energy consumption in urban environments

Climate, building materials, occupancy patterns, and HVAC (heating, ventilation, and air conditioning) systems all interact in complex ways, making it difficult to design low-energy buildings. Thus, innovative architectural and engineering design strategies are required to meet the worldwide need to decrease building energy usage. To improve the calculation of energy consumption of buildings, this work introduces the FCR-BCS (fuzzy clustering rule-based building control systems), which integrates fuzzy logic concepts into computational simulations. FCR-BCS can contemplate real-world uncertainties and fluctuations using linguistic factors and approximate reasoning for more precise and trustworthy results in energy-efficient building design. This method's significance rests in its potential to significantly reduce energy use, advance sustainability, and improve urban residents' quality of life; architects and engineers can thus employ FCR-BCS to enhance the efficiency of HVAC systems and insulation. The outcomes of FCR-BCS simulation assessments show that it is capable of making buildings more energy efficient. The experimental outcomes demonstrate that the suggested model increases the sensitivity analysis by 99.4%, energy efficiency analysis by 99.8%, occupancy patterns analysis by 97.5%, temperature profile analysis by 98.8%, and energy consumption analysis by 99.6% compared to other existing models.