Efficient Heating, Ventilation And Air Conditioning Research Articles

Toward residential decarbonization: Analyzing social-psychological drivers of household co-adoption of rooftop solar, electric vehicles, and efficient HVAC systems in Georgia, U.S.

The marketplace of climate solutions has grown considerably. Three technologies, in particular, exhibit strong potential for disruptive change and advancement in the residential sector: electric vehicles (EVs), rooftop photovoltaics (RPVs), and efficient heating, ventilation, and air conditioning (HVAC) systems. Many scholars have studied the adoption of these technologies; we address their co-adoption. Using a survey conducted in Georgia, we examine pro-environmental behavior, risk tolerance, and energy-related knowledge as social-psychological drivers for the co-adoption of RPVs, EVs, and efficient HVACs (including heat pumps). We conclude that some but not all of these drivers are relevant to the adoption and co-adoption of the three transformational climate solutions. Pro-environmental behaviors, for instance, appear to create conditions for “positive spillover,” propelling consumers toward adopting multiple climate solutions, whereas energy knowledge has no clear relationship with co-adoption. At the same time, the = influence of these drivers are unique to different combinations of technologies – one notable example is the significance of risk tolerance when RPV is added to technology bundles. Our paper contributes to the literature by broadening the understanding of the adoption of bundled (or co-adopted) climate solutions.

Analysis of demisting strategies for electric vehicles

Worldwide expansion of electric vehicles is more than obvious nowadays. However, there are still problems related to their driving range. Consequently, solutions for continuous improvement of electric vehicles range are required. In this context, more efficient HVAC (Heating, Ventilation, and Air Conditioning) systems, including defogging and defrosting, are extremely important as the use of these systems inevitably leads to significant decrease of electric vehicles range. Accordingly, the goal of this study is to investigate demisting windshield strategies in terms of energy consumption (without posing safety road concerns related to driver’s visibility). The approach is based on Computational Fluid Dynamics (CFD) full-scale simulations of a vehicle cabin, including special (innovative) model concerning windshield condensation. Different demisting strategies are analyzed, based on the developed numerical model: several supply air velocities for demisting grids and levels of power for heating up the windshield. The results indicate that, under the conditions of this study, choosing lower supply air velocity in conjunction with higher power levels of heated windscreen can lead to more energy efficient demisting strategies. Finally, the methodology proposed in this work can be easily extrapolated for other conditions to achieve comprehensive databases concerning the optimization of windscreen demisting strategies for electric vehicles.

Computer vision-based smart HVAC control system for university classroom in a subtropical climate

To respond to the increasing demand for a comfortable, productive and energy efficient study environment, the application of artificial intelligence technologies in the smart control of Heating, ventilation, and air conditioning (HVAC) systems plays an increasingly important role. This research uses a classroom, equipped with a traditional central HVAC system, in Hong Kong as a case study to demonstrate an innovative approach for a more intelligent and efficient HVAC system. Through a field investigation (i.e. measurement and questionnaire) and Computational Fluid Dynamics (CFD) simulation, it is found that the number and spatial location of students have a significant impact on their thermal comfort. Applying a computer vision model (YOLOv5) detected dynamic occupant information (variations in student numbers and locations) in a classroom, the SimScale (a cloud-native simulation platform) was then used to estimate the current thermal comfort state (predicted mean vote, PMV) and change in PMV (ΔPMV) of students in the classroom. Furthermore, a fuzzy logic control system is implemented to adjust air temperature and air velocity based on the simulation results. Preliminary scenario analysis has proven the feasibility of the proposed smart HVAC system for classrooms, as well as its ability to provide better quality of thermal comfort with more robust control. This study contributes to the smart and low-carbon retrofitting of university buildings with traditional central HVAC systems, while also serving as a benchmark for the energy-efficient transformation of HVAC systems in other types of indoor spaces.

Advanced control framework of regenerative electric heating with renewable energy based on multi-agent cooperation

Heating, ventilation, and air conditioning (HVAC) systems have accounted for a significant proportion of building energy growth. It is crucial to develop efficient HVAC systems based on renewable energy (RE) utilization to reduce energy costs (EC) and carbon emissions. However, HVAC system regulation is meeting a huge challenge owing to the randomness of RE and fluctuation of demand-side load. Aiming to tackle this problem, a multi-agent cooperative optimization control framework based on deep reinforcement learning was proposed to achieve the optimal match between the supply and demand sides of the regenerative electric heating system. Then, the dueling double deep Q-network and deep deterministic policy gradient method were applied to achieve the discrete and continuous control of independent agents, respectively, and value-decomposition network was exploited to realize the above agents' cooperative optimization. Meanwhile, to further improve the stability, efficiency, and flexibility of the optimization control framework, the feasible action screening mechanism, prioritized experience replay mechanism, and regulation mechanism based on occupant behavior were introduced. According to simulation results, in contrast to the baseline model, the reduction of thermal discomfort duration, unconsumed RE, and EC under the control of the multi-agent cooperative optimization framework can reach 84.86%, 70.79%, and 16.08%, respectively.

Enabling Energy Efficient HVAC Operation Through IWSNs

Many industrial processes rely on specific environmental conditions to ensure product quality and the well-being of human workers. To ensure the required production conditions, typically, energy-intensive Heating, Ventilation and Air Conditioning (HVAC) systems are used. In this paper, we present a variable Industrial Wireless Sensor Network (IWSN) as well as the linked measurement strategy and two different real-world deployments. The sensor nodes can measure environmental conditions such as thermal comfort, air purity, and three-dimensional airflow. For reliable wireless communication, the nodes feature a dual transceiver radio that can operate at sub-1 GHz and 2.4 GHz frequency bands. The collected data is used for a cyber physical production system (CPPS) to provide decision support and enable a demand-driven control of HVAC systems. We show that even with energy savings as low as 5% through our CPPS, up to 3990.56t <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CO_{2}$ </tex-math></inline-formula> -eq could be saved per year in our exemplary real-world deployments.

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

Distributed and localized cooling with thermoelectrics

G. Jeffrey Snyder is a Professor of Materials Science and Engineering at Northwestern University in Evanston Illinois. His interests are focused on engineering of electronic and thermal properties, and he is well known for his work on thermoelectric materials. He has developed new methods of electron band structure engineering and microstructure engineering of thermal and electrical properties of complex materials. His interdisciplinary approach stems from his background in solid state chemistry at Cornell University and the Max Planck Institute for solid state research, applied physics at Stanford University, and thermoelectric materials and device engineering at NASA/Jet Propulsion Laboratory and California Institute of Technology (Caltech).

Nanostructured composite coating endowed with antiviral activity against human respiratory viruses deposited on fibre-based air filters

The widespread of viral airborne diseases is becoming a critical problem for human health and safety, not only for the common cold and flu, but also considering more serious infection as the current pandemic COVID-19. Even if the current heating, ventilating and air conditioning (HVAC) systems limit the disease transmission by air, the air filters are susceptible to microbial colonization. In addition, viruses spread via droplets (aerosol) produced by direct or indirect contact with infected people. In this context, the necessity of an efficient HVAC system, able to capture and inactivate viruses- and bacteria-rich aerosols, thus preserving a safe indoor air environment and protecting people, is of enormous importance. The aim of this work is the assessment of the antiviral properties of a silver nanoclusters/silica composite coating deposited via co-sputtering technique on glass, on metallic fibre-based air filters as well as on cotton textiles. The selected human respiratory viruses are: respiratory syncytial virus (RSV), the human rhinovirus (HRV) and the influenza virus type A (FluVA). The coated air filters show that the nanostructured coating develops a strong virucidal activity against RSV and FluVA, but not against the HRV.

Building temperature regulation in a multi-zone HVAC system using distributed adaptive control

During recent years there have been considerable research efforts on improving energy efficiency of buildings. Since Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible for a big part of energy consumption, developing efficient HVAC control systems is crucial. In most of the developed approaches, precise knowledge of system parameters and/or adequate historical data is required. However, these approaches may not perform as well in the presence of dynamic parameter changes due to human activity, material degradation, and wear and tear, or disturbances and other operational uncertainties due to occupancy, solar gains, electrical equipment, and weather conditions. In this paper, we consider buildings with several climate zones and propose a distributed adaptive control scheme for a multi-zone HVAC system which can effectively regulate zone temperature by applying on-line learning and assuming exchange of information between neighboring zones. The controller of each zone achieves the local objective of controlling zone temperature by compensating for the effects of neighboring zones as well as for possible changes in the parameters of the system. Despite the exchange of information, each local controller does not know how the control actions and temperature of a neighboring zone affect the temperature of its own zone. For this reason, each local controller is estimating the parameters of the interconnections in real time and uses them together with the exchanged information to provide a more accurate local zone temperature control. The proposed method is illustrated using an example of temperature control in a six-zone building as well as a large school building, which are implemented in a Building Controls Virtual Test Bed (BCVTB) environment using EnergyPlus and MATLAB/Simulink.

Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates

Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for a significant share of the energy consumed in commercial buildings. While energy system retrofits have been found to reduce buildings' carbon footprint substantially, these measures are often hindered by financial, regulatory or design constraints. Recent research sheds light on energy management approaches to energy conservation such as energy-efficient settings of HVAC temperature setpoints. While existing case studies confirm the significant energy saving potential of efficient HVAC operation, there is scarcity of studies quantifying energy savings from optimal HVAC temperature setpoints comprehensively, while controlling for important factors, such as guaranteeing tenant thermal comfort levels and the impact of different climate conditions on the results. In this work, we apply simulation-based multi-objective optimization to fine-tune heating and cooling setpoints of large “typical” office buildings with respect to energy consumption and occupant thermal comfort. We apply the framework in seven climate zones across the US in an effort to examine spatial variations in the energy savings potential due to different climate conditions and propose targeted energy-saving strategies and policies. We show that locations with mild climates, such as San Francisco, CA, can realize up to 60% of annual HVAC-related energy savings without compromising the occupants' thermal comfort. This untapped potential to simultaneously improve building performance and occupants’ comfort drives the discussion on revisiting HVAC setpoint configuration standards in commercial buildings, either as part of individual building retrofit planning or as part of energy policy regulations.

A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing.

The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The cooling demand primarily relies on the accurate detection of occupancy. The current researches of demand-based HVAC control tend to detect the occupant count using cameras or other sensors, which often impose high computation and costs with limited real-life applications. Instead of detecting the occupant count, this paper proposes to detect the occupancy density. The occupancy density (estimated by image foreground moving pixels) together with the indoor and outdoor information (acquired from existing sensors) are used as inputs to an artificial neural network model for cooling demand estimation. Experiments have been implemented in a university design studio. Results show that, by adding the occupancy density, the cooling demand estimation error is greatly reduced by 67.4% and the R value is improved from 0.75 to 0.96. The proposed approach also features low-cost, computationally efficient, privacy-friendly and easily implementable. It shows good application potentials and can be readily incorporated into existing building management systems for improving energy efficiency.

COMPLIANCE WITH ENERGY SAVINGS AND LABOUR LEGISLATION REQUIREMENTS IN WIDE-OPEN INDUSTRIAL BUILDINGS REFURBISHMENT

Given the continuing requirements of the EU in terms of reducing the emissions of gases and improving energy efficiency, the refurbishment of existing buildings is of particular importance, considering that 40% of the energy is consumed in building sector. With respect to partially-open industrial buildings, air conditioning while refitting leads to increase of productivity, for it maximizes the working hours. Nevertheless, due to the resulting energy consumption increase, it seems necessary to clarify what would be the most efficient way of air conditioning this type of buildings, considering at the same time its economic viability. Therefore, the study presents an integrated approach to HVAC (Heating, Ventilation and Air Conditioning) systems to identify the most adequate of them in the case of an existing workshop. An application to a workshop in Carmona (Seville, Spain) shows that a highly efficient HVAC system, along with a climate separation by means air curtains, makes possible to keep the indoor thermal conditions of the premises between the mandatory ranges prescribed by the work legislation during much longer periods of time (reducing the hours out of compliance from 56% to 2%), with a reasonable energy consumption level (79.63% lower than a conventional system). Keywords: wide-open industrial buildings; air curtain; climate separation; HVAC systems; energy efficiency

Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution

Since commercial and residential buildings have become the largest energy consumers across all sectors, building energy efficiency has attracted increasing attention in recent years. Many studies suggest occupancy detection is critical in promoting building energy efficiency because it is premised on the idea of avoiding unnecessary waste while providing sufficient service. In this paper, we propose the integration of an iBeacon-enabled indoor positioning system (IPS) and a Variable Air Volume (VAV) heating, ventilation, and air-conditioning (HVAC) system to optimize system control and save energy based on high-resolution occupancy detection. The proposed system aims to match thermal service with the spatial distribution of occupants and redefine occupancy as a dynamic spatial occupancy distribution (DSOD) occupancy matrix. For this reason, this paper proposes measuring spatial occupancy by meshing large indoor spaces into zones and patches, and uses a feature-scaled artificial neural network algorithm to map the spatial IPS signal patterns. After acquiring the detailed spatial distribution, we also developed a ventilation control mechanism based on occupancy distribution. To validate the proposed control mechanism, we compared it with other traditional controllers in an on-site experiment and through a computational fluid dynamics (CFD) simulation. The results suggest that a 20% energy saving potential can be realized when the proposed approach is properly implemented.

Energy Efficient Building HVAC Control Algorithm with Real-time Occupancy Prediction

Abstract A large amount of energy is wasted through inefficient operation of heating, ventilation, and air conditioning (HVAC) system due to the lack of reliable building occupancy measurement and prediction. To mitigate this problem, an innovative change-point logistic regression model is developed to provide an accurate forecast of building occupancy. A novel building HVAC control algorithm is then developed by embedding the occupancy prediction model into the model predictive control (MPC) framework. The occupancy-based MPC algorithm tries to minimize building electricity consumption and maximize building occupants’ comfort at the same time. A penalty factor is introduced which allows building occupants to determine the optimal trade-off between comfort and energy efficiency. Numerical simulation results show that the proposed HVAC control strategy with real-time occupancy prediction not only reduces electricity consumption but also improves building occupants’ comfort.

Distributed Scheduling for Efficient HVAC Pre-cooling Operations

Supply fan takes up to 30% of Heating, Ventilation and Air Conditioning (HVAC) systems' in-building energy consumption. Since the fan power consumption is a cube function of the mass flow rate of supply air, it is thus possible to reduce the energy consumption significantly by elongating and scheduling the pre-cooling processes. To handle the computational complexity raised for large scale buildings, two distributed consensus-like scheduling algorithms are proposed in this paper. Simulation studies indicate higher energy efficiency compared to uncoordinated pre-cooling processes; the computation is faster than centralized optimization, although with performance sacrifice.

A review of different strategies for HVAC energy saving

Decreasing the energy consumption of heating, ventilation and air conditioning (HVAC) systems is becoming increasingly important due to rising cost of fossil fuels and environmental concerns. Therefore, finding novel ways to reduce energy consumption in buildings without compromising comfort and indoor air quality is an ongoing research challenge. One proven way of achieving energy efficiency in HVAC systems is to design systems that use novel configurations of existing system components. Each HVAC discipline has specific design requirements and each presents opportunities for energy savings. Energy efficient HVAC systems can be created by re-configuring traditional systems to make more strategic use of existing system parts. Recent research has demonstrated that a combination of existing air conditioning technologies can offer effective solutions for energy conservation and thermal comfort. This paper investigates and reviews the different technologies and approaches, and demonstrates their ability to improve the performance of HVAC systems in order to reduce energy consumption. For each strategy, a brief description is first presented and then by reviewing the previous studies, the influence of that method on the HVAC energy saving is investigated. Finally, a comparison study between these approaches is carried out.

A hybrid energy efficient building ventilation system

The present paper presents a high performance cooling/heating ventilation system using a rotary heat exchanger (RHE), together with a reverse-cycle heat pump (RCHP) that can be integrated with various heat sources. Energy consumption in the building sector is largely dominated by the energy consumed in maintaining comfortable conditions indoors. For example in many developed countries the building heating, ventilation and air conditioning (HVAC) systems consume up to 50% of the total energy consumed in buildings. Therefore energy efficient HVAC solutions in buildings are critical for realising CO2 targets at local and global level. There are many heating/cooling concepts that rely upon renewable energy sources and/or use natural low temperature heat sources in the winter and heat sinks in the summer. In the proposed system, waste energy from the exhaust air stream is used to precondition the outdoor air before it is supplied into the building. The hybrid system provides heating in the winter and cooling in the summer without any need for additional heating or cooling devices as required in conventional systems. Its performance is better than a typical reheat or air conditioning system in providing the same indoor air quality (IAQ) levels. It is shown that an energy saving up to 60% (heat energy) is achieved by using the proposed hybrid system in building ventilation applications.

Application of Flow Control Techniques for Indoor Ventilation

Abstract Significant amount of energy is consumed each year on heating, ventilating and air conditioning (HVAC), raising ongoing demand for energy efficient HVAC systems. This study explores the applicability of flow control techniques to improve the ventilation performance of a mixed ventilated environment. The effect of two kind of periodic excitations, periodic air supply and synthetic jet, on the representative flowfield are investigated. The results show that the flow pattern becomes oscillatory and the fresh air sweeps a wider region by applying the periodic supply condition. The mean age of air is reduced as a consequence, indicating an improvement in the ventilation performance. By activating synthetic jet close to the air supply inlet, the ventilation air jet is vectored, showing the potential to more actively control the fresh air direction.

Grey prediction on indoor comfort temperature for HVAC systems

This paper describes determination of indoor comfort temperature for efficiently Heating, Ventilating, and Air-Conditioning (HVAC) system under dynamical environment. Making occupants’ satisfaction on thermal comfort is still challenging by how the temperature setpoint of the fresh made-up air in conventional HVAC systems can be adapted properly (normally fixed) when surroundings changes in time. Essentially, being unknown ahead of time, the outdoor temperature is systematically predicted by grey prediction model in this work. The Adaptive Comfort Theory (ACT) model captures relation of the indoor comfort temperature to the outdoor temperature based on the survey data on thermal comfort in real occupants’ living environment. With the grey prediction model and the ACT model, the predicted indoor comfort temperature can be implemented as the comfort temperature reference for HVAC control systems. The experiment results show the viability of the proposed methodology for efficient HVAC control system.

Evaluating performance indices of a shopping centre and implementing HVAC control principles to minimize energy usage

Heating, ventilating and air-conditioning (HVAC) systems in buildings must be integrated with an efficient control scheme to maintain comfort under any load conditions. Efficient HVAC control is often the most cost-effective option to improve the energy efficiency of a building. However, HVAC processes are non-linear, and characteristics change on a seasonal basis so the effect of changing the control strategy is usually difficult to predict. The present study aims to reduce energy consumption by defining new HVAC control strategies and tuning control loops in a shopping centre. First, an energy audit was performed to investigate the potential for energy savings and to redefine the control scenarios, while a methodology for the shopping centre was developed. Performance indices were then calculated and compared with the yardsticks. Next, normalised performance indices were computed to reach out a better understanding of the building’s efficiency. Finally, new strategies were implemented with the help of the existing building management system (BMS) and about 22% of energy saving was achieved.