Operation Of Heating, Ventilation, And Air Conditioning System Research Articles

HVAC energy consumption prediction based on RJITL deep neural network model

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods.

Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

Impact of Building Automation on Indoor Air Quality and HVAC Performance

In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.

Self-Powered Airflow Sensor Based on Energy Harvesting of Ventilation Air in Buildings

Heating, ventilation, and air conditioning (HVAC) systems account for one-third of the total energy consumption in office buildings. The use of airflow measurements to control the operation of HVAC systems can reduce energy consumption; thus, a sensor capable of monitoring airflow in a duct system is critical. Triboelectric nanogenerators (TENGs) can be utilized as self-powered sensors in airflow-driven TENGs (ATENGs) as self-powered sensors. By employing ferroelectric materials and surface modifications, the surface charges of TENGs can be increased. In this study, fibrous-mat TENGs were prepared using ferroelectric materials consisting of poly (vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) and polyamide 11 (nylon-11). And these materials were subsequently investigated. Poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) was added to PVDF-TrFE to enhance the ferroelectric crystalline phase. X-ray diffraction analysis revealed that this incorporation affects the β phase. In addition, the surface of nylon-11 was modified using the electrospray technique for post-treatment, thereby improving the interfacial adhesion between the fibers. These materials were then utilized in fibrous-mat ATENGs (FM-ATENGs) to demonstrate their practical application. The FM-ATENGs can be effectively used in an Arduino airflow-check sensor, showcasing their potential for application in HVAC systems, to enhance airflow control and energy efficiency.Graphical

CFD analysis of the air supply rate influence on the aerosol dispersion in a university lecture room

The airborne droplet route has been recognized as the main pathway of infection transmission in indoor environments, drawing the attention to a proper design of Heating, Ventilation and Air Conditioning (HVAC) systems. This is particularly relevant for gathering spaces such as schools and universities, due to the high crowding levels and the long exposure times. Using 3D Computational Fluid Dynamics (CFD) numerical simulations based on a Eulerian-Lagrangian approach and validated against velocity measurements, the present study investigates the distribution over time of airborne droplets (aerosol) emitted in correspondence of the teacher position inside a lecture room, during a 2-h speaking activity (so reproducing a 2-h lesson), as a function of different air supply rates from the HVAC system. As the aim of the paper is to provide useful information for proper design and operation of HVAC systems, the room has been assumed to be empty. Results show that increasing the air supply rate is not always an effective solution to reduce aerosol concentration in correspondence of the positions occupied by the students attending the lesson. In fact, for the specific case under investigation, the aerosol concentration around the classroom seats does not always decrease when increasing the supply rates, even though the overall number of particles in the room decreases. This behaviour is mainly due to the asymmetric disposition of the seats with respect to the diffusers and extraction grilles, and underlines the importance of CFD approaches in investigating the fluid dynamics in indoor environments.

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems.

Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings' energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

Dynamic optimization for minimal HVAC demand with latent heat storage, heat recovery, natural ventilation, and solar shadings

Satisfying thermal comfort in indoor spaces is still a challenge in terms of energy saving, and several HVAC (Heating, Ventilation, and Air-Conditioning) systems have been proposed for this purpose. This paper conducts an analysis to evaluate and optimize the long-term operation of a novel HVAC system installed at The Green Village, a living lab in Delft, the Netherlands. This system comprises all-glass facades with steerable solar shades, sky windows, a climate tower equipped with Phase-Change Material (PCM), a heat recovery unit, and a heat pump. The current analysis draws on transient modeling to predict the system’s behavior while relying on constrained nonlinear optimization to select the optimal design parameters (e.g. floor heat capacity and solar absorptance) and optimal operational conditions (e.g. use of PCM and heat recovery unit, aperture of sky windows and solar shadings). The goal is to schedule the control inputs to operate the system as much as possible as a passive energy system, with minimal active power all year round. The results show that the optimization can reduce the yearly heat demand by around 10.6%, with the solar shadings being the most significant component to be optimized. Furthermore, the optimized system is capable to supply 58% of the annual thermal demand passively – In this case, an auxiliary thermal demand of only 27 kWh/m2/year is required, which may qualify the system as a low-energy building.

Model predictive control of a thermal chimney and dynamic solar shades for an all-glass facades building

Controlling the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems is arguably the most effective way to reach desired indoor conditions in buildings. Nevertheless, such control may involve complex dynamics when dealing with passive energy technologies. In this paper, we focus on maximizing the passive operation of HVAC in a novel low-energy building design by means of Model Predictive Control (MPC). The low-energy building design, located in The Green Village, consists of a thermal chimney and solar shades over all-glass facades to provide the required indoor air conditioning as passively as possible. The MPC controller is based on a transient grey box model and a hierarchical control architecture to satisfy thermal comfort while minimizing the active energy requirements. Using sensor data collected from the actual building in April and May 2021, the grey box model shows a good agreement with the measurements, since the variance accounted for is 90% in most cases. Moreover, via a comparative study among different MPC architectures we show that managing the distinct transient response of each component (shades and chimney) is the best for successful overall performance – e.g. considering linear agents for shading and nonlinear agents for ventilation. The hierarchical MPC architecture established outperforms the standard ones by 22.7% in terms of control performance. We also compare the proposed MPC approach against the rule-based control method currently implemented in the actual building, which indicates that MPC demands about 78% less active energy, highlighting the proposed optimization-based control approach.

Systematic summary and analysis of Chinese HVAC guidelines coping with COVID-19.

Heating, Ventilation, and Air-Conditioning (HVAC) system that is almost indispensable service system of modern buildings is recognized as the most important engineering control measure against pandemics. However, the effectiveness of HVAC systems has been questioned on their ability to control airborne transmission. After the outbreak of COVID-19, China has controlled the spread within a relatively short period. Considering the large population, high population density, busy transportation and the overall underdeveloped economy, China’s control measures may have some implications to other countries, especially those with limited resources. This paper intends to provide a systematic summary of Chinese ventilation guidelines issued to cope with COVID-19 transmission. The following three aspects are the main focus of these guidelines: (1) general operation and management schemes of various types of HVAC systems, (2) operation and management schemes of HVAC system in typical types of buildings, and (3) design schemes of HVAC system of makeshift hospitals. In addition, some important differences in HVAC guidelines between China and other countries/institutions are identified and compared, and the possible reasons are discussed. Further discussions are made on the following topics, including the required fresh air supply, the extended operation time, the use of auxiliary equipment, the limited capacity of existing systems, and the use of personalized systems.

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.

COVID-19 Impact on Operation and Energy Consumption of Heating, Ventilation and Air-Conditioning (HVAC) Systems

Heating, ventilation and air-conditioning (HVAC) system is favourable for regulating indoor temperature, relative humidity, airflow pattern and air quality. However, HVAC systems may turn out to be the culprit of microbial contamination in enclosed spaces and deteriorate the environment due to inappropriate design and operation. In the context of COVID-19, significant transformations and new requirements are occurring in HVAC systems. Recently, several updated operational guidelines for HVAC systems have been issued by various institutions to control the airborne transmission and mitigate infection risks in enclosed environments. Challenges and innovations emerge in response to operational variations of HVAC systems. To efficiently prevent the spread of the pandemic and reduce infection risks, it is essential to have an overall understanding of impacts caused by COVID-19 on HVAC systems. Therefore, the objectives of this article are to: (a) provide a comprehensive review of the airborne transmission characteristics of SARS-CoV-2 in enclosed spaces and a theoretical basis for HVAC operation guideline revision; (b) investigate HVAC-related guidelines to clarify the operational variations of HVAC systems during the pandemic; (c) analyse how operational variations of HVAC systems affect energy consumption; and (d) identify the innovations and research trends concerning future HVAC systems. Furthermore, this paper compares the energy consumption of HVAC system operation during the normal times versus pandemic period, based on a case study in China, providing a reference for other countries around the world. Results of this paper offer comprehensive insights into how to keep indoor environments safe while maintaining energy-efficient operation of HVAC systems.

Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards.

Scalable distributed sensor fault diagnosis for smart buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants' productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

Theoretical model of buoyancy-driven air infiltration during heating/cooling seasons in large space buildings

Air infiltration is common in large space buildings during heating and cooling seasons, which leads to thermal discomfort and high energy consumption. However, there still lacks a valid theoretical model to analyze its mechanism and influence factors. In this study, a theoretical model of buoyancy-driven air infiltration in large space buildings is established with the factors of heating, ventilation and air-conditioning (HVAC) systems. The prediction results agree well with the field measurement and the numerical simulation. This model is used to analyze the influences of HVAC systems on air infiltration (i.e., thermal stratification and mechanical fresh/exhaust air). A dimensionless buoyancy-driven force of air infiltration (CT) is defined to quantify the thermal stratification. Compared with other typical terminal devices, the radiant floor has the lowest CT in the heating and cooling conditions, which results in the lowest air infiltration rate. Besides, the air infiltration rate can theoretically decrease to zero with a sufficient mechanical fresh air rate of 1.4 times of the initial air infiltration rate, which is too large to realize in real buildings. This model helps to understand the air infiltration in large space buildings, which is beneficial to energy-efficient design and operation of HVAC systems.

An IoT-Based Thermal Model Learning Framework for Smart Buildings

With the development of the Internet of Things (IoT) technologies, implementing intelligent controls in buildings to reduce energy consumption is becoming increasingly popular. Building climate control is of strong research interest due to high energy savings potentials associated with heating, ventilation, and air-conditioning (HVAC) controls. Because the operation of HVAC systems directly influences occupant thermal comfort, building-specific thermal models are needed for proper control. Such models describe how indoor temperature changes under different environmental conditions and HVAC status. Previous studies mainly developed thermal models for accurate building thermal simulation which are not practical for use in real-world applications as they require domain knowledge and lengthy building-by-building configuration. In this article, taking advantage of IoT technologies, a plug-and-play learning framework is proposed to automatically identify the thermal model of each thermal zone in a building without manual configuration. In contrast to the existing methods, a thermal model is learned using low-resolution temperature readings from IoT-based smart thermostats. The learning framework has been validated using the data collected from an IoT platform installed in a building over a summer. The performance of the learned model and the error of indoor temperature prediction based on this model have been evaluated and quantified. Findings demonstrate the learning process can be automated with either edge-computing or cloud-computing. This article shows the validity of the proposed IoT-based thermal model learning framework and offers a pragmatic solution for providing reliable thermal models to future smart building climate controls.

Integration of operational data in building information modelling: From ontology to application

Building design, construction, and operation has been suggested to benefit from comprehensive and well-formed repository of associated information. Building Information Modeling (BIM) has pursued the supposition that repositories could facilitate seamless exchange of information amongst the multiple stakeholders in the building delivery process. Related research and development efforts have primarily focused on representation of buildings’ geometry and specifications of their constituent structural and constructional components. More recently, representations of building environmental control equipment and systems have begun to become incorporated in BIM applications. Ongoing work in this area has likewise targeted development of common schemes to incorporate, in BIM, buildings’ sensory networks and elements that serve, for instance, the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems. To achieve these targets, comprehensive and robust ontologies of building monitoring data and building performance indicators are essential. In the present contribution, we first present a recently introduced original proposal for such ontologies, covering data regarding dynamic data relevant to state, operation, and performance of buildings. This ontology was developed based on an extensive review of building monitoring data and performance indicator catalogues in thermal, air quality, visual, and acoustical domains. The ontology’s structural core basically involves a systematic specification of the generic attributes of building performance variables. We then illustrate various benefits and applications of this ontology. It is shown to support data quality check, data visualization, building operation optimization, and preventive building systems maintenance. It can also add to the clarity of building performance requirements specifications, advance the understanding of building performance principles in educational and training settings, and provide and early integration of buildings’ operational attributes in BIM applications.

A Decentralized Swarm Intelligence Algorithm for Global Optimization of HVAC System

To solve the high labor and maintenance cost while save energy problems in actual engineering, a decentralized control structure and optimization method for the heating, ventilation, and air conditioning (HVAC) systems are proposed. In this decentralized HVAC control system, each updated smart equipment is connected according to the physical correlations and can communicate with adjacent nodes collaboratively. Furthermore, a novel fully distributed and self-organizing swarm intelligence optimization algorithm is presented. And the algorithm runs in each smart equipment to achieve the optimal operation and avoid the conflict among correlated equipment. Moreover, the convergence property of the novel method is analyzed theoretically. This method is confirmed effective for realizing the global optimal operation of HVAC system through simulation results and hardware tests on actual engineering.

A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis

Dynamic cooling and heating load forecasting of heating, ventilation and air conditioning (HVAC) systems is a basis for optimizing the operation of HVAC systems and can contribute to achieving the effective management for the HVAC systems. This paper proposes a load forecasting method for office buildings based on artificial intelligence and regression analysis, including wavelet transform, support vector machines (SVM), and partial least squares regression (PLS). An office building located in Tianjin, China is taken as the building case study to validate the proposed model. For selecting the input variables, the methods of sensitivity analysis and correlation analysis are used. The results of different prediction horizons, mainly including 1 h ahead, 2 h ahead, 3 h ahead and 24 h ahead forecasting, are finally provided. In order to illustrate the accuracy improvement of the proposed model, the other three models are built to compare with the proposed model. Further, the influence of weather forecast precision on the proposed model is analyzed in this paper. The results indicate that the proposed method can realize dynamic load forecasting with high accuracy for different time horizons.

A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems

Optimal control strategies have been increasingly developed in heating, ventilation and air conditioning (HVAC) systems. The commonly used evaluation method, which compares the system’s energy consumption under proposed strategy with that under its original control, has several limitations. A dual-benchmark based CPI (control-perfect index) evaluation method is presented to assess the operation of an airport HVAC system under various control strategies. Firstly, the exergy loss models based on the HVAC productive structure are developed to analyze the exergy efficiency of component and system. Secondly, control-perfect index based on the process exergy analysis is presented as an evaluation factor for the operation of HVAC system. In addition, the original control of system is used as the 1st benchmark to estimate the energy saving capacity of proposed strategy. The ideal operation, which is obtained through the global optimization of exergy loss models, is used as the 2nd benchmark to estimate the improving potential of candidate strategy. Finally, twelve control strategies used in the airport HVAC system are evaluated through the CPI analysis of both component and system. Their energy saving ratios to Benchmark 1 and improving potentials to Benchmark 2 are compared, respectively.

Modeling and identification of the cooling dynamics of a tropical island hotel

Minimizing energy consumption of heating, ventilation and air conditioning (HVAC) systems in buildings has experienced an increasing attention recently. Mainly motivated by the exploitation of building automation systems (BAS), as well as simulation tools, innovative practices and methodologies have been introduced to reduce the costs of energy required for heating and air conditioning of buildings. Outstanding has been the establishment of model predictive control (MPC) as a control strategy for the optimal operation of HVAC systems. The basis for MPC is a dynamic model, which is objectively challenging and time-consuming to obtain. This paper presents two modeling approaches of the return water temperature of a central chiller plant based on data of the real operation of a building, weather disturbances, and the temperature of a reference thermal zone. It integrates building's real measurements with a room simulator model. In this work, an original method for accurately describing the cooling dynamics of a case study hotel on a tropical island is proposed.