Control Of HVAC Systems Research Articles

A Visualized Method of Airflow between Adjacent Zones inside a Multizone Building Based on Pressure Difference Frequency: Airflow Mapping

The airflow movement inside a multizone building has a significant impact on pollutant transfer, thermal comfort, and indoor air quality. However, there are difficulties in visualizing the airflow movement with existing methods. This study proposes a visualization method for evaluating airflows between adjacent internal zones inside a multizone building based on the analysis of pressure difference frequency. After the distribution of absolute pressure is measured and the wind pressures on the surfaces of the building are calculated, the variation of pressure differences between each couple of adjacent zones is analyzed for three levels: greater than 0 Pa, equal to 0 Pa, and less than 0 Pa (for any given zones selected as target zones). Finally, an airflow mapping is created for each floor using the visNetwork tool based on the R language. A target building was selected for applying the proposed method. The airflow mappings were derived from a detailed analysis of the pressure difference frequency between each couple of adjacent zones, presenting the variations of airflow direction and the dominant airflow during the measurement period in a visualized form. For example, the airflow direction from 1F_Z2 to 1F_Z3 is 92.0%. The spatial similarity in the variations of the airflow directions can also be observed on certain floors. The results of this experimental study show that the airflows between multiple zones can be easily identified without a complex building zone analysis. The variation in internal airflow direction between adjacent zones can be intuitively visualized, providing insight to the airtightness levels of building components. It is also observed that the airflow rates computed based on the airflow mappings can provide more guidance for the control of HVAC systems.

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

Real-time predictive control of HVAC systems for factory building using lightweight data-driven model

This paper presents a real-time implementation of model predictive control (MPC) for HVAC systems in an ice-cream factory building. The target building consists of two large open spaces served by two HVAC systems. We developed four artificial neural network (ANN) models that predict the thermal states of the supply air and indoor air of the two thermal zones and prove to be accurate enough (MBE = 2.65, CVRMSE = 9.43). The control variables employed in this study are the number of operating chillers, frequency of supply-air fan inverter and outdoor-air intake ratio. The objective function minimizes total energy use, and a constraint was set to maintain average indoor air temperatures close to set points. Real-time MPC was implemented at a sampling time of 20 min from 3 August to 30 August 2021 and could save approximately 31.7% of electricity when compared to the existing simple rule-based control.

Modelling virtual sensors for real-time indoor comfort control

Increasing demands on indoor comfort in buildings and urgently needed energy efficiency measures require optimised HVAC systems in buildings. To achieve this, more extensive and accurate input data are required. This is difficult or impossible to accomplish with physical sensors. Virtual sensors, in turn, can provide these data; however, current virtual sensors are either too slow or too inaccurate to do so. The aim of our research was to develop a novel digital-twin workflow providing fast and accurate virtual sensors to solve this problem. To achieve a short calculation time and accurate virtual measurement results, we coupled a fast building energy simulation and an accurate computational fluid dynamics simulation. We used measurement data from a test facility as boundary conditions for the models and managed the coupling workflow with a customised simulation and data management interface. The corresponding simulation results were extracted for the defined virtual sensors and validated with measurement data from the test facility. In summary, the results showed that the total computation time of the coupled simulation was less than 10 min, compared to 20 h of the corresponding CFD models. At the same time, the accuracy of the simulation over five consecutive days was at a mean absolute error of 0.35 K for the indoor air temperature and at 1.2% for the relative humidity. This shows that the novel coupled digital-twin workflow for virtual sensors is fast and accurate enough to optimise HVAC control systems in buildings.

Bayesian Optimization Framework for HVAC System Control

The use of machine-learning algorithms in optimizing the energy efficiency of HVAC systems has been widely studied in recent years. Previous research has focused mainly on data-driven model predictive controls and reinforcement learning. Both approaches require a large amount of online interactive data; therefore, they are not efficient and stable enough for large-scale practical applications. In this paper, a Bayesian optimization framework for HVAC control has been proposed to achieve near-optimal control performance while also maintaining high efficiency and stability, which would allow it to be implemented in a large number of projects to obtain large-scale benefits. The proposed framework includes the following: (1) a method for modeling HVAC control problems as contexture Bayesian optimization problems and a technology for automatically constructing Bayesian optimization samples, which are based on time series raw trending data; (2) a Gaussian process regression surrogate model for the objective function of optimization; (3) a Bayesian optimization control loop, optimized for the characteristics of HVAC system controls, including an additional exploration trick based on noise estimation and a mechanism to ensure constraint satisfaction. The performance of the proposed framework was evaluated by using a simulation system, which was calibrated by using trending data from a real data center. The results of our study showed that the proposed approach achieved more than a 10% increase in energy-efficiency savings within a few weeks of optimization time compared with the original building automation control.

A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems

Indoor thermal comfort represents a key aspect of building design. The reference standards do not consider the thermal adaptability of the human body, and HVAC system control strategies are based on a steady-state assumption that returns an incorrect estimate of occupants' thermal demand with a consequential misleading of the building energy consumption and of system sizing. To overcome these issues, a physiological thermal comfort model for the human body thermal behaviour evaluation is developed in MatLab environment for assessing the dynamic variation of the physiological parameters and for characterizing the occupants’ thermal sensation. Finally, the developed human body multi-node model is implemented in a building energy simulation tool (called DETECt 2.4) to perform three proposed strategies for the dynamic control of the building thermo-hygrometric parameters of the building and of the corresponding heating and cooling demands. These strategies provide an hourly regulation of relative humidity and air temperature by means of a two-step optimization that maximizes the thermal comfort of the occupants and minimizes energy consumption. To show the potentiality of the developed model, a suitable case study consisting of an office space is considered. Here, space heating and cooling demands obtained by applying the novel developed model are compared to those obtained through standard set-point values of air temperature and relative humidity (20 °C, 45% for heating needs, and 26 °C, 50% for cooling ones). By the comparison, between the proposed model – which considers the optimal hourly set point assessing the occupant in thermal evolution – and the reference ones, interesting results are obtained. Generally, a higher consumption is achieved by considering the proposed comfort strategies (from 2 to 16%), representing the price to be paid to maximize the comfort of the occupants and to annul the 3650 h of discomfort of the reference case.

Utilizing Smart Systems to Enhance the Sustainability of Egyptian Administrative Buildings

Smart buildings have grabbed the attention of researchers since the 1980s. Until now, there has been no specific definition for smart buildings, which came from modern technologies and applications that rely on smart systems, significantly affecting the construction industry. Based on the importance of the administrative building sector in Egypt, this study proposed a general framework enumerating smart systems that positively impact this sector at the environmental, social, and economic levels (the three key pillars of sustainability). Various methods were used, including induction, descriptive, analysis, survey, and deduction. It enumerated and analyzed the Egyptian context through three case studies to reveal the gap between their current situation and the one hoped for in its three categories, Smart Security and Safety Systems, Smart Auxiliary Systems, and Smart Entertainment Systems. The findings of this study for the first category showed an average of 47% for security systems and 67% for safety systems. The category of smart auxiliary systems was divided into five sub-categories, showing 39% in climatic control systems, 60% in energy management systems, 100% in HVAC control systems, 50% in lighting control systems, and 56% in water management systems. Finally, the smart entertainment systems category was divided into three sub-categories, showing 42% in Wi-Fi and Internet systems, 44% in video surveillance systems, and 67% in sound and video intercom systems. Therefore, these quantitative findings represent promising results that motivate the Egyptian context to move forward in this direction by using smart systems to improve the sustainability of Egyptian administrative buildings.

Optimization for the Model Predictive Control of Building HVAC System and Experimental Verification

This article presents an optimized prediction model of building dynamic HVAC system load, which simplifies the input parameters of the model while meeting the accuracy requirements of the prediction results. The model was established using the open-source Modelica-based building library, and the linear aggregation method was used to establish the model. A reduced-order model was developed, and the accuracy of the simplified and reduced-order models was verified. A control strategy was constructed using the indoor mean radiant temperature (MRT) aggregated from a simplified prediction model of HVAC system load as the target feedback parameter, and its feasibility was verified experimentally. It was found that the MRT adopted by the new control strategy can reflect the changes in outdoor air temperature and load in a timely manner; moreover, using this as a control parameter can significantly reduce the influence of load changes to maintain a stable indoor temperature. The control system is further simplified by the predictive model, which improves the engineering practicability by maintaining the control accuracy.

Indoor fire detection utilizing computer vision-based strategies

Fires are an ever-increasing risk in the world for both indoor and outdoor environments. Current technologies for detection in indoor environments are smoke and flame detectors. However, these detectors have several limitations during both the ignition phase of a fire and propagation. These systems cannot detect an exact position of the fire nor how the fire is spreading, or its size, all of which is necessary information for fire services when dealing with these incidents. A potential solution is to use artificial intelligence techniques such as computer vision, which has shown the potential to detect and recognize objects and activities in indoor spaces. This study aims to develop a vision-based indoor fire and smoke detection system. Existing models based on the Faster R–CNN Inception V2 and the SSD MobileNet V2 models were explored and adopted in this work. This study utilized small training and testing datasets (for indoor specific fire cases) of varying pixel density images. Initial evaluation of the approach was carried out by testing both models on videos, including a mock-up bedroom and living room and a CCTV video of office space. Both high-density smoke environments and flame density scenarios were recognized. The promising results were achieved from using only 480 training images. Despite the success achieved by the Faster R–CNN Inception V2, the SSD MobileNet V2 model showed low accuracy and missed detection results. Future works can focus on integrating this with our previously developed approach, such as occupancy detection, enhancing training data and models, using more advanced detection models, and integrating the proposed approach with the fire fighting and HVAC control systems.

Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System

As global warming accelerates due to greenhouse gas emissions, more efforts are required to reduce greenhouse gas emissions. One of the methods used to save building energy is the efficient management of building mechanical systems. The economizer control of HVAC systems is an energy-efficient measure that improves operating methods by introducing outdoor air to save cooling energy when the outdoor-air temperature is sufficiently low. When the HVAC system is operated using economizer control, cooling energy can be saved, and the set-point of the mixed-air temperature is kept constant. Several studies are being conducted on the saving of energy using economizers. Although various studies have been conducted on the control of economizers, there is insufficient research dealing with the optimal control of mixed-air temperature in economizers that consider real-time changes. Therefore, in this study, predictive model-based mixed-air temperature optimization for a single-duct VAV system was constructed. For this, an ANN (Artificial Neural Network) that could be analyzed regardless of the variables was applied to predict the load and energy consumption and a simulator was constructed for the optimized mixed air temperature of the system. The predictive model-based control was evaluated in terms of its thermal comfort and energy, along with the existing economizer control. According to the application of the optimal economizer control, the energy consumption of the building was reduced by 28.9% compared to the existing dry-bulb temperature control, and was within ±1 °C of the indoor-air temperature set point.

Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system

Determining a proper trade-off between energy consumption and indoor thermal comfort is important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal control strategy is designed for temperature setpoint real-time reset to balance the energy consumption and indoor air temperature. In addition, this study develops an EnergyPlus-Python co-simulation testbed to evaluate DQN control strategy in a simulation environment. A case study experiment is conducted to evaluate the performance of DQN control strategy for real-time reset of supply air temperature and chilled supply water temperature setpoint in a multi-zone building VAV system. The developed EnergyPlus-Python co-simulation testbed is used to train and test the DQN control strategy for performance analysis. The applied DQN strategy leans to update control actions (i.e. temperature setpoint) through interaction with the simulation environment. Simulation results show that the DQN control strategy is effective in finding a proper trade-off between the energy consumption of HVAC system and indoor air temperature. Meanwhile, the DQN control strategy can find a proper temperature setpoint reset sequence in smaller training episodes, and the control actions can be stable after ten DQN training episodes. This study provides a preliminary direction of deep reinforcement learning control strategy for temperature setpoint real-time reset in multi-zone building HVAC systems.

A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems

Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m2, would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Global Prospects, Advance Technologies and Policies of Energy-Saving and Sustainable Building Systems: A Review

Automation, modernization, economic development and global progress depends on efficient extraction and utilization of energy. Power generation by burning fossil fuels makes various adverse impacts on the environment. Additionally, the worldwide fossil fuel reserve is limited and depleting very fast. Hence, efficient energy usage and savings are crucial to address the environmental issues to ensure sustainable development. Buildings, both commercial and residential, represent a major energy consumption sector. Approximately 40% of the total energy is reportedly consumed in the building sector. Worldwide building energy consumption, performance measuring systems and best practices, energy-saving techniques and policies are reviewed and summarized in this article. Underfloor air distribution, double-glazed windows, use of highly efficient electric motors and variable speed drives may play a great role in reducing building energy consumption. In the UK, the application of double-glazed windows in commercial buildings can save 39–53% energy. The proper maintenance of a building’s central heating system can save up to 11% energy. The automatic HVAC control system can reduce up to 20% of the building’s total heating load. Proper utilization of a VSD system in motor and building performance optimization by an ANOVA tool also proved instrumental in saving energy. Apart from this, the building codes of different countries also help to improve building performance by reducing energy consumption. This study will help building researchers and policymakers to make a framework for sustainable, green building.

Zoned heating, ventilation, and air–conditioning residential systems: A systematic review

Historically, humanity has considered the control of cooling and heating an aspect related to both comfort and survival, currently being it evaluated not as a luxury, but as a fact of modern existence. In the global context, at the beginning of the 20th century, the efforts to develop cooling techniques with the aim of maintaining control climate at indoor spaces of the office and residential buildings, were evident. This initial direction that is taken to achieve comfort levels in terms of cooling and heating generates what is now called heating, ventilation, and air–conditioning (HVAC). This paper provides a general description of the zoning methods that support the structural description of the thermal zones of a building, required for the design, control and maintenance of HVAC systems. The main objective of this study is to systematically review the current research on zoning HVAC residential systems. The article conclusion is that an intelligent zoning strategy using a criterion associated with user behavior, occupancy patterns and its corresponding schedules, will promote the development of efficient optimization processes for energy consumption and thermal comfort if they are considered as a structural part of the HVAC system. Finally, in terms of modeling the advice is to start with a white box model, phenomenological, but to follow-up or support it with data-based models. • This paper provides the zoning methods that support the structural description of the thermal zones of a building. • The main objective of this study is to systematically review the current research on HVAC residential systems. • An intelligent zoning strategy using behavior, occupancy and schedules could give adaptation capacity to the HVAC system. • The ideal HVAC modeling would be to start with a white box model, and then supported it with data-based models.

Evaluation of linear and nonlinear system models in hierarchical model predictive control of HVAC systems

Buildings are responsible for one third of the global final energy consumption. Model predictive control (MPC) can reduce their energy consumption and improve thermal comfort. However, designing the required models can be time consuming. Splitting the control problem into smaller subproblems could make the modeling process more modular and therefore cheaper. A hierarchical MPC structure is proposed in this work, where the building model is divided into a lower layer consisting of the producer side and an upper layer consisting of the consumers. Linear and non-linear model equations as well as a cost-based and a control quality-based cost function for a building energy system are developed. In a simulation, the nonlinear controller outperforms the linear controller in both constraint satisfaction and energy costs.

Effect of Different HVAC Control Strategies on Thermal Comfort and Adaptive Behavior in High-Rise Apartments

In the residential sector, householders play an active role in regulating the indoor climate via diverse control measures such as the operation of air-conditioners or windows. The main research question asked in this paper is whether control decisions made by householders are rational and effective in terms of achieving comfort and energy efficiency. Based on a field study in South Korea, this paper explores how a HVAC control strategy for high-rise apartment buildings can affect occupant comfort and adaptive behavior. Two different control strategies: (1) occupant control (OC), where occupants were allowed to freely operate the HVAC system and (2) comfort-zone control (CC), where the operation of the HVAC system was determined by the researcher, based on a pre-defined comfort zone, were applied to, and tested within the participating households in summer. The impact of the two control strategies on indoor thermal environments, thermal comfort, and occupant adaptive behavior were analyzed. We find that the CC strategy is more energy/comfort efficient than OC because: (1) comfort was be achieved at a higher indoor temperature, and (2) unnecessary control behaviors leading to cooling load increase can be minimized, which have major implications for energy consumption reduction in the residential sector.

Climatic division for the design of HVAC systems

Different climate characteristics put forward different requirements for the performance of air conditioning system. Climatic division has been proposed to facilitate the selection or design of HVAC systems to match indoor cooling/heating loads of buildings better. However, humidity parameter and distinction of sensible and latent loads are usually ignored, bringing problems to the operation and control of HVAC systems. Therefore, this study proposed a novel climatic division method for China during the cooling period taking the coupling effect of cooling and dehumidification of HVAC systems into account. The humidity-based cooling degree-day was firstly taken as an index to evaluate the influence of outdoor humidity parameter. Then, 96 cities, whose cooling loads are particularly affected by the humidity, were selected for further analysis. Indoor cooling loads were calculated using EnergyPlus and the climatic division based on sensible heat ratio and total cooling load was presented. Finally, on the basis of the novel climatic division, hourly sensible cooling load and latent cooling load of representative cities in each zone were analyzed to get the characteristics of load boundary and frequency distribution. A new design method was therefore proposed and discussed, paving a new way for the customized design of HVAC systems for each climate zone.

Modeling and multiobjective optimization of indoor airborne disease transmission risk and associated energy consumption for building HVAC systems

The COVID-19 pandemic has renewed interest in assessing how the operation of HVAC systems influences the risk of airborne disease transmission in buildings. Various processes, such as ventilation and filtration, have been shown to reduce the probability of disease spread by removing or deactivating exhaled aerosols that potentially contain infectious material. However, such qualitative recommendations fail to specify how much of these or other disinfection techniques are needed to achieve acceptable risk levels in a particular space. An additional complication is that application of these techniques inevitably increases energy costs, the magnitude of which can vary significantly based on local weather. Moreover, the operational flexibility available to the HVAC system may be inherently limited by equipment capacities and occupant comfort requirements. Given this knowledge gap, we propose a set of dynamical models that can be used to estimate airborne transmission risk and energy consumption for building HVAC systems based on controller setpoints and a forecast of weather conditions. By combining physics-based material balances with phenomenological models of the HVAC control system, it is possible to predict time-varying airflows and other HVAC variables, which are then used to calculate key metrics. Through a variety of examples involving real and simulated commercial buildings, we show that our models can be used for monitoring purposes by applying them directly to transient building data as operated, or they may be embedded within a multi-objective optimization framework to evaluate the tradeoff between infection risk and energy consumption. By combining these applications, building managers can determine which spaces are in need of infection risk reduction and how to provide that reduction at the lowest energy cost. The key finding is that both the baseline infection risk and the most energy-efficient disinfection strategy can vary significantly from space to space and depend sensitively on the weather, thus underscoring the importance of the quantitative predictions provided by the models.

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.