Control Of Heating, Ventilation And Air Conditioning 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.

A comparative study of DQN and D3QN for HVAC system optimization control

Ensuring the optimal performance of Heating, Ventilation, and Air Conditioning (HVAC) systems is paramount for achieving energy efficiency. This paper investigates the application of deep reinforcement learning algorithms in HVAC system control, aiming to identify the most suitable Q-network structure for optimizing HVAC systems and comparing the performance of Deep Q-learning (DQN) and Double Dueling Deep Q-learning (D3QN) algorithms. Initially, this paper evaluates and analyses existing literature to perform a normalization treatment on the state space. Through systematic simulation and rigorous data analysis, the impact of the Q-network structure on the efficacy of the DQN and D3QN algorithms is evaluated, resulting in the proposal of specific values for the Q-network structures within these two algorithms. Subsequently, comparisons are drawn on the optimization effectiveness, stability and reliability of these algorithms across diverse engineering projects. Results highlight the superiority of the D3QN algorithm over the DQN algorithm regarding both optimization effectiveness and stability across all evaluated projects. The proposed efficient Q-network structure comprises two hidden layers, with 64 and 12 neurons respectively in each layer. The findings of this paper are crucial in providing insights for HVAC systems control optimization using reinforcement learning and pave the way for advanced research and applications in the future.

Development of a DNN model using calibrated simulation for the optimal control of HVAC systems

Acquiring actual data for developing data-based prediction models for the optimization of control setpoints (hereinafter referred to as optimal control (OC)) in heating, ventilation, and air conditioning (HVAC) systems is challenging, especially when changing control setpoints while considering all possible boundary conditions. Thus, considering the scalability of OC, it is a promising solution to generate sufficient learning data according to the control settings using a highly accurate simulation model, build a source model, and expand the learning model based on the actual data of the target system. This is the first study to improve the scalability of OC aiming to develop a deep neural network (DNN) model to predict the power consumption of a heat source system. First, this study will construct a calibrated heat source system simulation model and then use the generated learning data to train a DNN model. The actual operation conditions of the target building and heating system located in Vietnam were analyzed using operation data. A physics-based simulation model of the target heat source system was developed and calibrated using actual operation data to improve accuracy. By using the calibrated simulation, sufficient learning data was generated for all control settings allowing for the development of a learning model based on DNN that predicted the power consumption of the heat source system. In addition, a highly accurate prediction model through hyperparameter optimization was secured.

Overall and local environmental collaborative control based on personal comfort model and personal comfort system

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control.

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Comparing the life cycle costs of a traditional and a smart HVAC control system for Australian office buildings

Many smart technologies have been introduced in buildings with the aim to reduce the energy and GHG emissions associated with their operation, particularly through improved control systems for regulating heating, ventilation and air conditioning (HVAC) equipment. Despite their energy saving potential, only a few studies have comprehensively assessed the costs associated with their practical implementation from a life cycle perspective. Accordingly, this study quantifies and compares the life cycle costs of a smart HVAC control system with that of a traditional control system, in the context of an Australian office building. For both systems, the required hardware are specified based on the characteristics of these systems and the layout of the serviced spaces in the reference building. The costs incurred over the period of assessment are quantified using the net present cost (NPC) approach. To evaluate the effects of these control systems on the operational energy costs of the building HVAC system, the control logics of both these systems are modelled through building energy simulations. The results show that, over the period of assessment, the smart control system incurred a higher total cost compared to the traditional control system. However, the findings from the simulations show that the HVAC energy cost savings achieved through the specification of the smart control system offset the additional cost incurred to deploy this system over the traditional control system. The smart control system resulted in HVAC operational cost savings between 9 % and 10 % compared to the traditional control system. Sensitivity analyses indicated that the total life cycle costs varied between −27 % and +50 %, with the discount rate and energy price increase rate being the most influential parameters.

Assessing the Impact of Climate Changes, Building Characteristics, and HVAC Control on Energy Requirements under a Mediterranean Climate

Assessing the Impact of Climate Changes, Building Characteristics, and HVAC Control on Energy Requirements under a Mediterranean Climate

Medium spatiotemporal characteristics based global optimization method for energy efficiency trade-off issue in variable flow rate HVAC system

During the optimization and control processes of variable flow rate heating, ventilation, and air conditioning (HVAC) systems, the medium temporal and spatial characteristics often significantly influence the control effectiveness and energy efficiency of the overall system. Unfortunately, much of the existing research predominantly concentrates on enhancing the performance of specific individual components, neglecting the impact of medium temporal and spatial characteristics on the overall HVAC system performance. Consequently, issues such as energy efficiency trade-offs in practical systems remain inadequately addressed. In this paper, the medium spatiotemporal characteristics of HVAC systems are systematically illustrated and modeled. A novel optimization method based on these characteristics is proposed to improve the overall performance of the HVAC system. This method adopts an event-driven optimization strategy to determine the optimal timing with consideration of temporal characteristics, and an adaptive hybrid algorithm is proposed to determine the optimal setpoints with consideration of spatial characteristics. By comparing to conventional methods with constant setpoints, genetic algorithm (GA) based setpoints, and differential evolution (DE) based setpoints in both simulation test and experiment, the proposed method demonstrates the ability to reduce electricity consumption by 21.2%, 13.1%, and 10.0%, respectively. The methodology in this research provides significant guidance for engineers to promote the decoupling control and overall optimization of HVAC systems. Additionally, the results have referable values for implementing the most appropriate algorithm in practice.

Design–operation gap caused by parameter variance in HVAC system control sequences: A simulation-based study on energy efficiency and temperature controllability

During the design phase, the annual energy consumption of heating, ventilation, and air-conditioning (HVAC) systems is frequently simplified or fixed under certain assumptions due to uncertain parameters within the control sequence, resulting in a performance gap between the intended and actual performances. This study assessed the impact of six uncertain parameters on system energy consumption and indoor temperature control under various updating scenarios. First, sensitivity analysis was conducted using the one-at-a-time method to identify the influential impact of the single uncertain parameter. Then, mutual impact analysis was conducted to showcase how different parameters affect each other, and mixed impact analysis was applied to evaluate how intended design performance converges to actual operating outcomes. A detailed physical model of a single-zone variable air volume system was developed to enable a comprehensive evaluation across different operational conditions. The results indicate that a 10% change in some parameters can lead to a 20∼30% increase in energy consumption, especially during low-load winter months. Properly evaluating the influence of one parameter is difficult without taking the effects of additional parameters into account, and some combinations of parameters can significantly decrease the system performance. These results underscore the necessity of considering the updating and combination scenarios of different parameters within the control sequences during the design phase.

Intelligent monitoring and optimal control of HVAC system and its cloud-edge implementation

In this work, the solution of the intelligent monitoring and optimal control of a real-world Heating, Ventilation, and Air Conditioning (HVAC) system is presented with its cloud-edge implementation. The main contribution and novelty of this work are three-fold: (1) an improved just-in-time-learning aided canonical correlation analysis (JITL-CCA) based health monitoring method is proposed; (2) a hybrid particle swarm-based optimization control method to improve the energy efficiency of HVAC systems is proposed; (3) a cloud-edge platform is developed for its implementation. Particularly, this platform allows online verifying and implementing the monitoring and optimal control methods of real HVAC systems via Internet, which is significantly different from the simulation-based and the test rig-based verification ways. Such a verification is available at: www.acain.cn.

Life cycle energy and greenhouse gas emissions of a traditional and a smart HVAC control system for Australian office buildings

In recent years, many novel smart technologies have been proposed to reduce the energy consumption and greenhouse gas (GHG) emissions attributed to the building sector. One such application of these technologies is the reduction of the Heating, Ventilation and Air Conditioning (HVAC) energy needs and GHG emissions using smart control systems. The environmental benefits of smart control systems during building operation have been explored in many studies, however, their embodied effects, associated with the extraction of raw materials, manufacturing and replacement are often overlooked. Accordingly, this study quantifies the life cycle energy needs and GHG emissions of a smart HVAC control system and assesses its potential for reducing the HVAC operational energy and GHG emissions in an Australian office building. A comparative assessment is performed, considering a traditional HVAC control system and an equivalent smart HVAC control system. The components of both the traditional and smart control systems are specified based on the characteristics of these systems as well as the layout of the serviced spaces in the reference building. The embodied energy and GHG emissions of both the traditional and smart control systems are quantified through a hybrid life cycle inventory (LCI) approach. To evaluate the effects of these control systems on the building HVAC operational energy, a building energy simulation is performed by applying the control logics of both systems. The results show that energy and GHG emissions savings in the HVAC operational offset the additional energy needs and GHG emissions of deploying the smart HVAC control system.

Development of an HVAC system control method using weather forecasting data with deep reinforcement learning algorithms

Heating, ventilation, and air conditioning (HVAC) systems account for a significant proportion of the energy consumption of a building. With the global energy demand increasing every year, optimal HVAC control methods that reduce energy consumption while maintaining the thermal comfort of the occupants have become more important. Weather forecasting data have thus attracted significant research attention for the optimization of building energy consumption in model-based studies. However, these HVAC control approaches have failed to consider the reduction in the predictive error of the incorporated weather model, leading to performance limitations. Accordingly, model-free reinforcement learning (RL)-based HVAC systems that can be controlled dynamically have been proposed, and these have exhibited significantly better performance than model-based methods. However, RL algorithms using weather forecasting data have not yet been assessed for use in optimal HVAC control for buildings. In this study, we propose WDQN-temPER, a hybrid HVAC control method combining a deep Q-network with a novel prioritized experience replay (PER) technique (referred to as temPER) and a gated recurrent unit (GRU) model. The GRU model predicts the future outdoor temperature and this is used as a state variable in the RL model. The proposed temPER algorithm facilitates the use of samples in the RL training process with larger changes in the outdoor temperature based on the predicted temperature. We experimentally demonstrate in EnergyPlus simulations that our proposed WDQN-temPER model outperforms a rule-based baseline model in terms of HVAC control, with energy savings of up to 58.79%.

Multi-indicator adaptive HVAC control system for low-energy indoor air quality management of heritage building preservation

Regenerated commercial heritage buildings employ heating, ventilation, and air conditioning (HVAC) systems that significantly increase their energy consumption, necessitating a methodology that supports low-energy operation to achieve a sustainable built environment. Poor indoor air quality (IAQ) management can cause irreversible damage to heritage buildings. However, there are risks of the destruction of inherent heritage values if energy-efficiency approaches are implemented without considering the visual impacts and multiple IAQ parameters in heritage buildings. To preserve heritage buildings, this study developed a multi-indicator adaptive ventilation control system for IAQ management using digital twin technology, which consisted of triggers and feedback. A digital representation of heritage buildings was established using Heritage Building Information Modelling (HBIM) with sensors to trigger adjustments in ventilation system settings. The sensor placement rules for IAQ monitoring and HVAC control of heritage buildings were demonstrated using computational fluid dynamics (CFD) simulations. The relationships among the HVAC inlet velocity, multiple IAQ parameters, and energy consumption were quantified using CFD and energy simulations. Simulation data were used to generate responsive charts for adaptive ventilation control, and the sensor data provided feedback to the ventilation system. The optimal ventilation system control strategy achieved up to 30% energy savings in the illustrative example. The proposed multi-indicator adaptive HVAC control system contributes significantly to the timely reduction of multiple air pollutants, IAQ parameter adjustments for the preservation of heritage buildings with minimal structural and visual impacts, and the need for more autonomous and energy-efficient HVAC systems.

Transfer learning for occupancy-based HVAC control: A data-driven approach using unsupervised learning of occupancy profiles and deep reinforcement learning

Model-free heating, ventilation, and air conditioning (HVAC) control systems have demonstrated promising potential for adjusting indoor setpoint temperature based on dynamic occupancy patterns in smart buildings. Although these control systems offer the advantage of not needing building or occupancy models, the involved trial-and-error learning process can cause considerable thermal discomfort for occupants, particularly during the initial learning period. Given the critical importance of thermal comfort, this limitation is a major barrier to the practical implementation of such systems. To address this challenge, the present study proposes a framework to enhance the learning process of the model-free HVAC controllers. Specifically, a transfer learning (TL) technique is adopted based on a similarity analysis of occupancy patterns using an unsupervised learning of occupancy profiles. This control framework leverages a k-means clustering algorithm with dynamic time warping to match the most similar households in terms of occupancy patterns within 26 residential units. The results demonstrate that the proposed method significantly improves the performance of the HVAC control system. It enhances the jumpstart performance and total rewards by nearly 25% and 5%, respectively, compared to a conventional model-free controller. Furthermore, it reduces the deviation period and mean temperature deviation by approximately 4% and 68%, respectively. Overall, this framework presents a promising approach to enhancing the performance and practicality of model-free HVAC control systems by reducing the thermal discomfort during the learning process.

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.

Research on optimal control of HVAC system using swarm intelligence algorithms

Energy conservation in buildings is of vital importance for solving the problem of global warming and energy scarcity in today's world. In this paper, we discuss the optimal control methods of the heating, ventilation, and air-conditioning (HVAC) system using swarm intelligence algorithms to reduce energy consumption of buildings. The performances of ten optimization algorithms are compared. The results on test functions manifest the artificial bee colony algorithm (ABC) exhibits the best performance among the focused optimization algorithms. Taking a real HVAC system as an example, the holistic energy models of equipment in the HVAC system are established and validated. Then, the focused algorithms are used for the optimal control of a real HVAC system aiming at energy conservation. The simulated results based on 24-h real operation data show that the energy-saving ratio through by the ABC can reach 24.07% on average. The work in this study will contribute to the building energy conservation and carbon neutral development in the future.

Multi-Sensor Platform for Predictive Air Quality Monitoring.

Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people's health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people's comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware-software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants' physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days.

The Role of IoT in Enhancing HVAC Control Systems

This comprehensive research paper investigates the paradigm shift in Heating, Ventilation, and Air Conditioning (HVAC) control systems propelled by the transformative integration of the Internet of Things (IoT). In an era marked by the convergence of digital technologies, the infusion of IoT into HVAC systems heralds a new era of dynamic, interconnected control mechanisms. This study undertakes a thorough examination of the evolving landscape, shedding light on the profound advancements, discernible benefits, and nuanced challenges intrinsic to harnessing IoT for the augmentation of HVAC control systems. The journey begins by elucidating the fundamental shifts catalyzed by the assimilation of IoT in HVAC systems. The traditional boundaries of HVAC control are transcended as interconnected devices seamlessly communicate, fostering an environment where each component becomes an intelligent node in a networked ecosystem. Real-time data exchange becomes the bedrock, facilitating a level of monitoring and control hitherto unseen. The paper explores the intricacies of this interconnectedness, unveiling the potential for granular control and adaptability that IoT ushers into HVAC operations. A focal point of this research is the exploration of the tangible benefits that arise from this symbiosis of IoT and HVAC control. The paper meticulously examines how real-time monitoring empowers system operators with unprecedented insights into performance metrics, energy consumption patterns, and environmental conditions. Harnessing this wealth of data, IoT-equipped HVAC systems demonstrate an unparalleled capacity for adaptive control, responding dynamically to fluctuating demands and external variables. The consequential improvements in energy efficiency and resource utilization contribute not only to operational cost savings but also align with global sustainability objectives. However, in the pursuit of technological advancement, challenges inevitably emerge. This research critically evaluates the impediments and challenges inherent in the integration of IoT into HVAC control systems. Security concerns, data privacy issues, and the evolving landscape of technology standards are among the multifaceted challenges explored in depth. The paper endeavors to provide a nuanced understanding of these challenges, offering insights that can inform the development of robust and resilient IoT-enabled HVAC control systems

A novel method based on thermal image to predict the personal thermal comfort in the vehicle

A passenger-centered Heating, Ventilation, and Air Conditioning (HVAC) system is an urgent need with the innovative development of intelligent vehicles. A prerequisite for such a system is precise real-time estimation of human thermal comfort. This paper introduces a new method based on deep learning to predict the personal thermal comfort in the vehicle via facial thermal image. Specifically, a deep learning-based ResNet34 coupled with a spatial attention mechanism (SAM-ResNet34) is designed for feature extraction from the facial thermal image. Unlike the existing methods, this method extracts the gray features from the different areas of facial thermal images instead of measuring the facial skin temperature to predict the thermal comfort states. The thermal comfort data were collected from the experiment with 22 subjects. It has highlighted that the model trained with the thermal image dataset can achieve an accuracy as high as 93.75% in the test set. The result suggests that the non-invasive method proposed in this study could accurately predict personal thermal comfort in the vehicular environment and holds great potential to be applied to the HVAC control system of a vehicle in the future.

Evaluating optimal control of active insulation and HVAC systems in residential buildings

Recently, active insulation systems (AIS) have been conceptualized in building envelopes to optimally modulate thermal resistance in response to changing environmental conditions. Building flexibility can further be improved if the building is also equipped with optimized heating, ventilating, and air conditioning (HVAC) control. In this work, we investigate the annual potential benefits of jointly optimizing AIS and HVAC system controls in both heating and cooling days over all climate zones (CZs) in the U.S. To reduce the computational complexity of applying model predictive control (MPC) to annual operations and detailed whole-building energy models, timeseries clustering was used to identify a set of representative days for optimizing in each climate zone. To isolate the increase in benefits from this joint optimization, we compare the performance to cases where the AIS and HVAC controls are optimized separately. Results indicate savings potential in all CZs, with the largest annual average savings of 9.02% and 4.02% observed in the cooling days with large daily temperature swings and heating days with cold sunny conditions, respectively. Savings patterns across climate zone, day types, and HVAC modes (i.e., heating or cooling) are also discussed along with the implications of important system design variables.