Heating Ventilation Air Conditioning Research Articles

Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage

A crucial aspect of a sustainable city is ensuring that energy and water supplies can adequately meet urban demand. With the scarcity of natural resources and growing electricity and water demand, it becomes increasingly important for consumers to manage their resource usage more efficiently. This paper proposes a novel perspective of demand-side management coordination strategy for a building's water-energy nexus to enhance the resilience and efficiency of the overall electricity-water-heating system. The model is formulated to optimally coordinates the onsite greywater recycling system, heating, ventilation, air conditioning (HVAC) loads, and distributed energy generation systems with a bidirectional grid connection in a residential building. All subsystems are controlled by a model predictive controller (MPC) receiving real-time time of use (ToU) pricing from electricity and water utilities. The presented mixed integer linear programming model is verified to meet the customers' demands while reducing the operational costs. Results are compared with benchmark systems lacking the water recycling or energy storage system showing 8.3 % operational cost reduction while reducing potable water consumption by 21.5 %. The effect of increased MPC control horizon is also studied showing reduction in cost with increased horizon. Detailed analysis of the proposed framework computational burden and effect of prediction errors is performed to prove the MPC adaptability and robustness. Testing under increased room size and different user preferences further validate the efficacy of the proposed scheme in reducing the operational costs.

Temperature stabilization of a lab space at 10 mK-level over a day.

Temperature fluctuations over long time scales (≳ 1h) are an insidious problem for precision measurements. In optical laboratories, the primary effect of temperature fluctuations is drifts in optical circuits over spatial scales of a few meters and temporal scales extending beyond a few minutes. We present a lab-scale environment temperature control system approaching 10mK-level temperature instability across a lab for integration times above an hour and extending to a day. This is achieved by passive isolation of the laboratory space from the building walls using a circulating air gap and an active control system feeding back to heating coils at the outlet of the laboratory's Heating-Ventilation-Air-Conditioning (HVAC) unit. These techniques together result in 20 dB suppression of the temperature power spectrum across the lab at 10-4Hz-approaching the limit set by statistical coherence of the temperature field-and 10mK Allan deviation around 15 °C after an hour of averaging, which is an order of magnitude better than any previous report for a full laboratory.

Self-powered electrochromic smart window helps net-zero energy buildings

Net-zero energy buildings are becoming an important energy-saving and low-carbon technology for global environmental protection and resource conservation. As the least energy-efficient building components, windows have promoted the development of electrochemically light/thermal management, where smart windows without external electrical supplies are highly desired for net-zero energy buildings. Here, we develop a self-powered electrochromic smart window system to regulate solar radiation transmittance, constructed by a triboelectric nanogenerator (TENG) and a high-performance electrochromic device. The visible–near-infrared (VIS-NIR) dual-band electrochromic device is well-designed based on an eco-friendly low-energy demand phenothiazine redox ionic liquid, demonstrating a fast switching response of ﹤1.9 s, a large transmittance modulation of >51.8 %, and outstanding electrochemical durability (5.5 % attenuation after 4000 cycles). Upon continuous pressing/releasing motions, the integrated TENG-powered electrochromic smart window (TECSW) displays a prompt light/thermal management behavior with desirable privacy preservation performance (transmittance modulation of 51 % in the visible region) and excellent thermal management property (12.3 °C reducti°n). The whole-building energy simulations show that TECSW can save 347.79 MJ/m2yr (amounting to 96.60 kWh/m2yr) of operational Heating Ventilation Air Conditioning (HVAC) warming/cooling energy consumption among 34 cities in China, accounting for 17.25 % of the total energy consumption. This research provides new insight into designing green and energy-efficient smart windows for net-zero energy buildings.

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.

Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples

Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder the effectiveness of conventional machine learning-based fault diagnosis methodologies. This paper proposes a method based on an improved conditional variational autoencoder (MCVAE) and co-training (CT) ideology to address the issue of insufficient training samples. Initially, we employ MCVAE to synthesize an extensive dataset of chiller fault samples from the original training dataset. Subsequently, the beneficial samples for training our fault diagnostic classifier, namely high-quality samples, are selected from the generated dataset using the CT-based framework. Finally, the selected high-quality samples are merged into the original training dataset to train the ultimate fault classifiers. Experimental results demonstrate that our proposed method outperforms in effectiveness and efficiency compared to recently published methods. For instance, in the case of fault level 1 compared to the suboptimal model, our approach exhibits improvements of 2.41% when each type has 5 fault samples.

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.

Distributed Control of HVAC-BESS under Solar Power Forecasts in Microgrid System

This paper investigates an energy management problem in the microgrid by scheduling heating ventilation air conditioning (HVAC) and battery energy storage system (BESS) with a distributed algorithm. A multi-layer energy management architecture is presented at a system-level to co-optimize the HVAC-BESS by taking into account solar energy forecasts. A surplus-based consensus algorithm is proposed to solve the optimization problem, where the local power mismatch is introduced as a surplus term, and the HVAC-BESS can thus be co-scheduled to maximize renewable energy efficiency at the peak generation time. A set of the convex cost functions are formulated to minimize the HVAC's user dissatisfaction degree and alleviate power loss during the BESS operation. The goal is to collectively minimize the total energy cost in a distributed manner, subject to individual load constraints and power balance constraints. It is theoretically proved that a global convergence of the proposed algorithm is achieved provided that the directed network is strongly connected. The results from a number of case studies are promising, demonstrating the effectiveness and robustness of the algorithm under practical scenarios.

Intelligent fault diagnosis for air handing units based on improved generative adversarial network and deep reinforcement learning

Data-driven Automatic fault detection and diagnosis (AFDD) for air handling units (AHUs) is crucial for ensuring the stable operation and energy consumption of the heating ventilation air-conditioning (HVAC) system. However, traditional machine learning methods often underperform when confronted with insufficient training sample data, especially when lacking samples from the fault types. Based on the issues of insufficient samples from the fault types and imbalanced training dataset, this study proposes a novel AFDD approach using transformer integrated conditional Wasserstein generative adversarial network and deep reinforcement learning (TCWGAN-DRL) to synthesize the fault data and select high quality synthetic data samples. Firstly, we utilize the proposed TransCWGAN to synthesize fault samples. Then, reinforcement learning is utilized to select high quality synthetic samples. Finally, the filtered samples and the real fault samples are merged to form the training dataset for conventional supervised learning classifiers. Experimental results demonstrate that the enriched training dataset can effectively improve the AFDD results and outperforms recently published existing methods, for instance, compared to the suboptimal model, our method exhibits an increase in fault recognition accuracy of 4.9%, 3.66%, and 4.02% when the number of real fault samples is 15, 20, and 30, respectively.

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.

Utilization of Distinct HVAC Operation Modes to Improve Demand Response Flexibility in the Pharmaceutical Industry and Economic Analysis for Optimization by HOMER Software

Abstract In the pharmaceutical industry (PMI), the major portion of energy is consumed in heat ventilation air conditioning (HVAC) system; therefore, building energy management systems (BEMS) primarily focus on optimizing the energy consumption in HVAC systems. The two operation modes of HVAC, function mode (FM) and non-function mode (NFM), are descriptively explained with their role in improving the flexibility of demanded energy. Both modes are also exposed to hybrid optimization of multiple electric renewables (HOMER) software analysis from an economic perspective. Concerning net present cost (NPC) and cost of energy (COE) constraints, the FM/NFM of HVAC is preferable to the FM. This paper recognizes a comparative evaluation of several demand response (DR) alliances to deliver a comprehensive image of the suitability of DR alliances for different PMIs. Further, the paper also explores an innovative concept in the form of a control algorithm and discusses the relevant challenges and future opportunities. Moreover, the use of renewable energy systems (RESs) for enhancing energy management (EM) flexibility with the economy in the PMI or other industries is emphasized through DR alliances. This review study could be helpful to the PMI in terms of managing energy demand and also incorporating DR as an essential aspect of EM.

Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

Due to the uncertainty and randomness of clean energy, microgrid operation is often prone to instability, which requires the implementation of a robust and adaptive optimization scheduling method. In this paper, a model-based reinforcement learning algorithm is applied to the optimal scheduling problem of microgrids. During the training process, the current learned networks are used to assist Monte Carlo Tree Search (MCTS) in completing game history accumulation, and updating the learning network parameters to obtain optimal microgrid scheduling strategies and a simulated environmental dynamics model. We establish a microgrid environment simulator that includes Heating Ventilation Air Conditioning (HVAC) systems, Photovoltaic (PV) systems, and Energy Storage (ES) systems for simulation. The simulation results show that the operation of microgrids in both islanded and connected modes does not affect the training effectiveness of the algorithm. After 200 training steps, the algorithm can avoid the punishment of exceeding the red line of the bus voltage, and after 800 training steps, the training result converges and the loss values of the value and reward network converge to 0, showing good effectiveness. This proves that the algorithm proposed in this paper can be applied to the optimization scheduling problem of microgrids.

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.

Air to Water Generator Integrated System Real Application: A Study Case in a Worker Village in United Arab Emirates

The water crisis is currently affecting billions of people. To mitigate the issue, unconventional water sources should be taken into account. Among them, atmosphere is a promising possibility, but it is still considered a novel source, and more studies, based on real results concerning the behaviour of the Atmospheric/Air-to Water Generator (AWG) systems, also known as Atmospheric Water Harvesting (AWH) systems, are needed to prove the water extraction sustainability. The current research work describes the real application of an integrated AWG system, based on a thermodynamic reverse cycle, designed to extract water from air and take advantage of the other useful effects of the cycle at the same time. The integrated machine was placed in Dubai, in a worker village, and tested. The machine is able to provide, at the same time, with the same energy consumption, water, heating and cooling energy. On the basis of onsite measurements, calculations about the efficiencies, using the Water Energy Transformation (WET), plastic savings, due to bottled water avoidance, and economic sustainability were carried out. The work answers to research questions concerning the potentiality of integrated systems in Heating Ventilation Air Conditioning (HVAC) plants revamping, the economic sustainability of water extraction from air and the lack of tests on real AWG machines of thousand-litre production capability (large size).

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.

COMMISIONING AND FAT HVAC FOR OFFSHORE

HVAC stands for Heating Ventilation Air Conditioning, in the industrial world it is very necessary to condition a room according to its needs, it can be used to regulate a room or freeze a product, the selection of HVAC materials for Offshore is very important so that the unit lasts a long time and does not rust easily.
 The importance of selecting materials that are in accordance with the provisions in terms of safety, function and quality, materials that are easy to obtain, sizes according to calculations, aesthetics, and materials that are not easy to rust.
 commissioning is done because to determine the quality and function of the HVAC unit whether it is in accordance with the design, function or calculation.

Inter-building effect on building energy consumption in high-density city contexts

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis.