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Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in production environments. Optimising the control strategy of an HVAC system is an effective approach to achieving energy savings and reducing emissions in a production environment. In existing optimal control models for HVAC systems, occupant behaviour has been incorporated as a key influencing factor. However, model predictive control algorithms have not introduced to examine its performance in optimising HVAC system operation. An optimisation strategy considering occupant (leaders and users of HVAC systems) behaviour (OSOB) is proposed based on model predictive control algorithm. The concept of environmentally specific transformational leadership is introduced to characterise the behaviours of leaders. Then the distinct decisions driven by double objectives (carbon emissions and electricity costs) can be explained. The OSOB was applied to a representative office building in Beijing. The findings indicate that incorporating occupant behaviour into algorithm can lead to a significant reduction in carbon emissions, up to 45%, as well as a decrease in electricity bills, up to 17%. This study not only incorporates occupant behaviour as a significant influencing factor, but also offers valuable insights for product manufacturers seeking to reduce the carbon emissions produced during building operations. [ABSTRACT FROM AUTHOR]

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies. [ABSTRACT FROM AUTHOR]

In the context of the effort to find the right key performance indicator that allows comparing the energy efficiency of the ELI-NP research infrastructure equipped with a unique HVAC geothermal system with other similar infrastructures, the paper presents key performance indicators used to evaluate the energy efficiency of buildings. The end goal is to optimize and make the infrastructure energy efficient while maintaining the operating parameters imposed by the research activity. [ABSTRACT FROM AUTHOR]

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139°C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1°C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) of heating, ventilation, and air conditioning (HVAC) systems can help to improve the energy saving in building energy systems. However, most data-driven trained FDD models have limited generalizability and can only be applied to specific systems. The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD. Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data. In this study, a novel transfer learning approach for HVAC FDD is proposed. First, the transformer model is modified to incorporate one encoder and two decoders connected, enabling two outputs. This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning. It has effective performance in complex systems and achieves an accuracy of 91.38% for a system with 16 faults and multiple fault severity levels. Second, the adapter-based parameter-efficient transfer learning method, facilitating the transfer of trained models simply by inserting small adapter modules, is investigated as the transfer learning strategy. Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters. It works well with limited data amount in target domain. Furthermore, the findings highlight the significance of adapters positioned near the bottom and top layers, emphasizing their critical role in facilitating successful transfer learning.

The escalating energy demands in buildings, particularly for heating and cooling demands met by heat pumps, have placed a growing stress on energy resources. The bi-functional thermal diode tank (BTDT) is proposed as thermal energy storage to improve the heating and cooling performances of heat pumps in both summer and winter. The BTDT is an insulated water tank with a gravity heat pipe (GHP), which can harvest and store heat passively from sun radiation and the external environment during the daytime. In summer, it harvests and stores cold energy from the air and night sky during the daytime. The performance of the BTDT-assisted heat pump (BTDT-HP) system in Adelaide, Australia, during the 2021–2022 summer and winter seasons was evaluated by conducting a TRNSYS simulation. This study revealed that the BTDT-HP system outperformed the reference ASHP system, where up to 8% energy in heating and 39.75% energy in cooling could be saved. An overall reduction in the energy consumption of 18.89% was achieved. Increasing the BTDT volume and GHP panel area enabled the tank to store more thermal and cold energy across the winter and summer seasons, thereby improving the system's performance. The maximum ESPs were found to be 31.6% and 41.2% for heating and cooling for the study case under optimal conditions. When the GHP panel area was fixed at 15 m2, the BTDT volume should be at least 28 m3 for the BTDT-HP system, boasting cooling and heating capacities of 40 kW and 43.2 kW, to achieve positive energy savings. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model's accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low. [ABSTRACT FROM AUTHOR]

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building's HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions. [ABSTRACT FROM AUTHOR]

In comparison to commonly employed iterative learning controls and reinforced learning techniques in model predictive controls for buildings, a supervised learning based iterative learning control platform that is more suitable and computationally efficient for real-world applications is proposed. The proposed control system relies on a data-driven model and utilizes the Random Forest algorithm to develop an HVAC start-stop model; this model considers only a limited system history period that can influence the current state, thus avoiding prolonged learning periods and time-consuming exploration. Specifically, within the current timeframe, the HVAC start-stop model learns from daily errors, and start and stop times “labeled as adjusted” accordingly.The proposed platform was validated against the TRNSYS baseline of a research facility, which was meticulously calibrated with actual measurements. In comparison with the convention, the proposed approach yielded significant energy savings of 6.5–7.6 % in HVAC annual energy consumption, while maintaining temperature comfort for approximately 97–98 % of the annual operating days. Notably, by implementing supply air volume ramp-up in conjunction with HVAC optimal start control, temperature comfort for up to 99 % of the annual operating days was achieved, along with a notable 9.7 % reduction in HVAC annual energy consumption.

With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic and distributed optimal energy management approach for active distribution networks (ADNs) within office buildings. The proposed approach aims at scheduling office buildings fitted with heating ventilation and air conditioning (HVAC) systems, and electric vehicle (EV) charging piles, to participate in the ADN optimization. First, an energy management approach for the ADN with aggregated office buildings is proposed. And the ADN optimization model is formulated considering the detailed building thermal dynamics and the mobile behaviors of workers. Then, to consider uncertainties of photovoltaic (PV) power, scenario-based stochastic programming is integrated into the ADN optimization model. To further realize the stochastic energy management of the ADN within office buildings in a distributed manner, the alternating direction method of multipliers (ADMM) is used to solve the ADN optimization model. The original ADN optimization problem is divided into the network-side and the building-side sub-problems to effectively protect the privacy of the ADN and the office buildings. Finally, the ADN optimization model incorporating office buildings is validated in the winter by numerical studies. In addition, the impacts of comfort temperature range and expected state of charge (SOC) at departure are analyzed.

Nowadays, heat, ventilation and air conditioning system are an essential part in a building. In order to achieve energy saving, the heating, ventilation, and air conditioning (HVAC) system have to enhance the designs of their various types of integrated electrical and mechanical components so that to control and operate plant optimally. This paper presented the development of smart sensor controller for heat, ventilation and air conditioning system by using Arduino. Smart sensor controller can be regarded a device that can control the speed of the motor based on the number of person detected by the infrared sensor. The motor used in this project will represent the fan motor of the normal air conditioner. The objective of the smart sensor controller is to prove the energy saving concept and to reduce the electrical consumption for the HVAC system. The experiment conducted show that the implementation of the smart sensor controller provides an average energy saving efficiency of 45.13 % compare to HVAC system without smart sensor controller.

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

With the emergence of Internet of Things (IoT), the usage of sensors for controlling and monitoring remote devices to achieve sustainability has gained researchers' interest. It has been observed that buildings are one of the largest consumers of energy hence, effective measures are required to achieve sustainability. In this sector, substantial amount of energy is used by HVAC (Heating, Ventilation and Air Conditioning) systems to offer ease for occupants. In most cases, HVAC systems of these buildings run on fixed schedules and do not provide any satisfactory control, based on detailed occupancy information. In this paper, a new solution is presented for estimating occupancy using network of thermal sensor arrays. The system provides near real time actionable information for controlling HVAC system and conditioning the rooms based on usage. The proposed system is a network of wired sensors, wireless sensors and gateway nodes, working together. Energy readings estimate the battery life of over two years, while working accurately. The system shows potential energy savings of 10% to 15%. Recurrent neural network are also used to train the model and compared with the proposed method. We conclude this new approach remarkably improves the results of occupancy detection using network of thermal sensor arrays. [ABSTRACT FROM AUTHOR]

assessments are essential in High-Performance Building Design (HPBD) in order to reduce energy consumption and carbon emissions. With advancement in data analytics, rapid and accurate machine learning-based building energy consumption prediction models have emerged. These models can be used by non-professionals as an alternative to time-consuming energy simulation software, offering benefits in HPBD. Therefore, the main objective of the present study is to develop a prediction model using data generated by physics-based simulations of a typical open-plan office space. The model predicts annual energy consumption, CO2 emissions, and percentage of comfort hours during the design phase. Various configurations of Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forrest (RF), and K-Nearest Neighbors (KNN) algorithms were trained and tested with the generated data via the DesignBuilder software. The technical parameters considered as inputs include U-values of envelope constructions, Window to Wall Ratio (WWR), orientation, and Heating, Ventilation, and Air-Conditioning (HVAC) systems. The results indicate that there is no clear linear relationship between individual inputs and the target indicators. However, ANN, with its ability to handle non-linear relationships, performed the best, achieving a maximum Coefficient of Determination (R2) value of 0.997 for predicting percentage of comfort hours and outperforms the other algorithms. Furthermore, the results show that RF is the next best algorithm, with 0.96 ≤ R2Test ≤ 0.98 for predicting the various target variables. SVM with Radial Basis Function (SVM-RBF) follows, with 0.89 ≤ R2Test ≤ 0.95. Contrary to ANN, SVM, and RF algorithms with high abilities to learn complex pattern between various independent parameters and the target variable, KNN exhibits the poorest performance, with 0.88 ≤ R2Test ≤ 0.91. Additionally, it is observed that with a maximum time cost of 619 s, ANN with three layers is able to learn the relationships between the inputs and target indicators at a convenient speed. Since knowledge-based decision making in the early design stages is crucial for achieving the optimum solutions to reduce energy consumption and related CO2 emissions while ensuring occupants’ comfort and minimizing future modifications and costs, high-speed and accurate prediction methods for design stage evaluation are essential. [ABSTRACT FROM AUTHOR]

HVAC systems are crucial for maintaining indoor temperature and humidity in buildings but consume significant energy, accounting for over 50% of a building’s energy use. This study proposes a deep reinforcement learning (DRL) algorithm for optimizing energy consumption in residential building HVAC systems while maintaining occupant comfort. Climate data was collected using low-cost sensors, and a co-simulation framework was developed for offline training and validation of our DRL-based algorithm. The proposed DRL-based algorithm was compared to a rule-based HVAC system regarding energy consumption and occupant comfort. Results show that the proposed algorithm can reduce energy consumption by up to 15% compared to the rule-based HVAC system. DRL is a suitable approach for optimizing HVAC systems due to its ability to adapt to the dynamics of multiparameterized systems. This study contributes to sustainable building design by proposing a DRL-based algorithm to reduce energy consumption while maintaining a comfortable indoor temperature. Using low-cost sensors and a co-simulation framework provides a practical and cost-effective method for training and validating the proposed algorithm. [ABSTRACT FROM AUTHOR]

Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings. [ABSTRACT FROM AUTHOR]

Following the recent pandemic of COVID-19, scientists have made many efforts to devise a workable solution for it, worldwide. However, it was shown that the protective effect of a well-conditioning system is as high as five times in comparison to the face-covering and other proposed procedures. In this context, the age of air and the type of filtration systems in closed spaces became the critical criteria for comparing the capability of ventilation systems. In this paper, a validated numerical model for the perforated duct diffusers is used to study the behaviour of the local age of air at the full-scale office with 8 feet (2.44 [m]) height, under various initial conditions like initial velocity and air change per hour. Also, different geometries for the ducts have been investigated under the same initial condition, as well as the effect of direction, ventilation effectiveness, and flow pattern. Finally, the volume average of the age of air at different zones has been nominated to perform the sensitivity analysis of each variable based on the variation of the airflow. The results show that diffusers with vertical perforations would be more effective during the pandemic than the other types in airborne mitigation. Moreover, the highest available airflow shall be set until such time there is no windy area in the breathing zone. Within these modifications, the residence time of the infectious nuclei in the breathing zone may decrease by up to 30%.

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model’s accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low.

The escalating energy demands in buildings, particularly for heating and cooling demands met by heat pumps, have placed a growing stress on energy resources. The bi-functional thermal diode tank (BTDT) is proposed as thermal energy storage to improve the heating and cooling performances of heat pumps in both summer and winter. The BTDT is an insulated water tank with a gravity heat pipe (GHP), which can harvest and store heat passively from sun radiation and the external environment during the daytime. In summer, it harvests and stores cold energy from the air and night sky during the daytime. The performance of the BTDT-assisted heat pump (BTDT-HP) system in Adelaide, Australia, during the 2021–2022 summer and winter seasons was evaluated by conducting a TRNSYS simulation. This study revealed that the BTDT-HP system outperformed the reference ASHP system, where up to 8% energy in heating and 39.75% energy in cooling could be saved. An overall reduction in the energy consumption of 18.89% was achieved. Increasing the BTDT volume and GHP panel area enabled the tank to store more thermal and cold energy across the winter and summer seasons, thereby improving the system’s performance. The maximum ESPs were found to be 31.6% and 41.2% for heating and cooling for the study case under optimal conditions. When the GHP panel area was fixed at 15 m2, the BTDT volume should be at least 28 m3 for the BTDT-HP system, boasting cooling and heating capacities of 40 kW and 43.2 kW, to achieve positive energy savings.

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building’s HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions.

This work presents a study about the design, environment, energy, and comfort in buildings equipped with a Predicted Mean Vote (PMV)-controlled HVAC system. The control system, based on the three categories of the international standard ISO 7730 uses the level of thermal comfort instead of the traditional control of the air temperature. In this type of control, using the PMV index, the air temperature (Tair), air relative humidity (RHair), Mean Radiant Temperature (MRT), air velocity (Vair), level of clothing (CL), and level of physical activity (AL) are considered. The initial four parameters are associated with environmental ones, and the last two are associated with personal ones. The simulation is carried out using a simulator of the dynamic thermal response of buildings and the thermophysiology of the occupants. The simulator considers energy and mass balance integral equations, based on the building's design. This equation system is generated by the simulator. In this study, three cases are performed: Categories A, B, and C. According to the results obtained, it is possible to verify that it takes some time to achieve acceptable comfort conditions when the HVAC system is connected. However, after the conditions are achieved, the system guarantees acceptable conditions during the occupancy time. Category A provides higher levels of energy consumption than Categories B and C. [ABSTRACT FROM AUTHOR]

In order to reduce global energy consumption, energy-efficient, green and smart buildings should be built. In addition to the application of other energy efficiency measures, effective management of heating, ventilation and air conditioning (HVAC) systems are also required. High-quality management and control of those systems ensures optimal occupant comfort, proper operation, rational energy consumption, and a positive impact on the environment. This is especially important for large buildings with complex systems such as hotels. As a contribution to the creation of appropriate tools for the management and control of HVAC systems in smart buildings, this paper presents the results of the development of a detailed dynamic simulation model based on data collected from a smart room system in a hotel in Zagreb, Croatia. The smart room system provides historical data on a set and actual room temperatures, occupancy schedule, windows openings, fan coil unit (FCU) operating status, fan speed, control valve opening, and heating or cooling modes with a time step of 5 minutes. The simulation model based on TRNSYS software uses part of the available data and predicts the room temperatures. A comparison of the predicted and measured temperatures at each time step shows that the deviations of the manually calibrated model are within acceptable standardized limits. The developed model has been applied to identify major anomalies in the operation of the FCU system, yielding promising results. [ABSTRACT FROM AUTHOR]

Aiming at optimizing the energy consumption of HVAC, an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis (SA), named the sensitivity analysis combination method (SAC). Based on the SA, neural network and the related settings about energy conservation of HVAC systems, such as cooling water temperature, chilled water temperature and supply air temperature, were optimized. Moreover, based on the data of the existing HVAC system, various optimal control methods of HVAC systems were tested and evaluated by a simulated HVAC system in TRNSYS. The results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.

Although HVAC (Heating Ventilation, and Air Conditioning) layout design has transitioned from 2D drawings to 3D BIM models, the traditional manual process of sizing and placing terminal units is still a time-consuming task, which can lead to inappropriate airflow and wastage of resources due to human error. The purpose of this paper is to develop a highly robust method for sizing and placing HVAC terminal units in rooms, while also systematically avoiding obstacles based on the locations of air terminal units and FCUs (Fan Coil Units) to which they belong. The method employs a "scan" approach to solve the problem of insufficient robustness caused by the traditional grid division method. Rule-based iterative algorithms are utilized for the sizing of terminals, airflow verification, and obstacle avoidance to generate a complete set of FCU and CAV (Constant Air Volume) system terminal layouts within a building. The method was tested for the automated design of HVAC terminal layouts in six different buildings and successfully completed the task within seconds, demonstrating the method's immediacy and robustness. Moreover, airflow organization tests showed that the terminal layouts generated by the algorithm had a 95% pass rate, indicating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO 2 ) is one of the pollutants that most affects people's health. An automatic system able to accurately forecast CO 2 concentration can prevent a sudden rise in CO 2 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 CO 2 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 CO 2 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. [ABSTRACT FROM AUTHOR]

A fault diagnosis method of heating, ventilation, and air conditioning (HVAC) systems based on the ReliefF-recursive feature elimination based on cross validation-support vector machine (ReliefF-RFECV-SVM) combined model is proposed to enhance the diagnosis accuracy and efficiency. The method initially uses ReliefF to screen the original features, selecting those that account for 95% of the total weight. The recursive feature elimination based on cross validation (RFECV), based on a random forest classifier, is then applied to select the optimal feature subset according to diagnostic accuracy. Finally, a support vector machine (SVM) model is constructed for fault classification. The method is tested on seven typical faults of the ASHRAE 1043-RP water chiller dataset and three typical faults of an air-cooled self-built air conditioner simulation dataset. The results show that the ReliefF-RFECV-SVM method significantly reduces diagnosis time compared to SVM, shortening it by about 50% based on the ASHRAE 1043-RP dataset, while achieving an overall accuracy of 99.98%. Moreover, the proposed method achieves a comprehensive diagnosis accuracy of 99.97% on the self-built simulation dataset, with diagnosis time the reduced by about 65% compared to single SVM. [ABSTRACT FROM AUTHOR]

An effective approach to increasing energy efficiency in buildings without compromising thermal comfort is to optimize heating, ventilation, and air conditioning (HVAC) systems through the use of advanced building-management system features, such as fault detection and diagnosis. Such functions are usually developed based on simulation models that must be calibrated and validated to achieve an appropriate level of accuracy and reliability. The objective of this study was to develop and calibrate a room-level simulation model of a hotel building and its HVAC system using TRNSYS 18 software and real data collected from the smart room system installed in the building. The calibration process was performed with 100 rooms using 5-min samples of room temperatures in selected 1-month periods during the summer and winter seasons by minimizing the root mean squared error (RMSE) in the average of the observed rooms using a genetic algorithm. The calibrated model was able to predict room temperatures with an RMSE of 0.79 ± 0.14 °C and a coefficient of variation in the root mean squared error (cvRMSE) of 3.58 ± 0.7%, which is well below the limits prescribed by international guidelines. The model was then applied to detect faults in the operation of fan coil units in the rooms based on the residual analysis and defined if–then rules. The results obtained show that the model can track the trends of temperature changes in real conditions and successfully detect major anomalies in a system. [ABSTRACT FROM AUTHOR]

Considering the risk of infection in surgeries,maintaining an acceptable indoor air quality in Operating Rooms(ORs) to ensure the health and safety of patients and surgical team is very essential.Since air flow is one of the primary mechanisms for transmitting infections and pollution, it is crucial to examine the air distribution systems in the ORs. In the present study, the effect of Turbulent and Laminar Airflow (TAF/ LAF) systems on the air and CO2 distribution in an OR was examined. The effects of inlet and outlet configurations were evaluated for seven different models. The results indicate that the LAF systems are superior to TAF systems. Based on the findings,the LAF with the air curtain configuration brings about the minimum CO2 concentration level in the OR. The results showed that LAF with the air curtain model could reduce the CO2 concentration by about 64.66% and 88.96% on the central plane, which passes along the body patient on 1.14 m and 1.7 m above the floor, respectively, compared to the existing model. [ABSTRACT FROM AUTHOR]

The building sector accounts for a substantial amount of energy consumption, resulting in higher carbon emissions and environmental impact worldwide. Electrification and energy efficiency in building systems can be the key to decarbonization in buildings. This research proposes new heating and cooling loops consisting of heat pumps to lower natural gas usage in building systems. Typical chillers and boilers in the cooling and heating loops are replaced with heat pumps to serve the loads and maintain thermal comfort in the building. In addition, a new optimal supply air temperature (SAT) reset strategy is also implemented with the proposed configuration for better system performance. A large multi-zone office building is simulated as a case study to measure the conventional system's electricity and natural gas consumption and the proposed design. Even with heat pumps that use electricity as the energy source, electricity consumption is reduced by 3.3% to 11.8% in different climate zones for the proposed system. On the other hand, 10.2% to 67% lower natural gas is consumed when the proposed system and the optimal SAT reset are utilized. The carbon emission is also reduced by 10.8% to 38% compared to the conventional system. The results show that the proposed design and optimization strategy can lead to significant energy and cost savings in conjunction with lower carbon emissions. [ABSTRACT FROM AUTHOR]

This paper presents a study on temperature control with a heater in a building using Model Predictive Control (MPC) with a focus on addressing two uncertainties: in model and the weather forecast. In previous works, a grey-box model of the building system was developed, and the values of parameters were estimated by the estimation techniques. In this work, based on the model, simulations are conducted comparing four types of MPC controllers: Deterministic MPC, Multistage MPC, Chance-constrained MPC, and hybrid MPC. The hybrid framework integrates the strengths of the multistage and chance-constrained MPCs to achieve conservative performance and increased robustness in constraint satisfaction. The simulations demonstrate that while deterministic MPC may not always guarantee constraint satisfaction, the hybrid framework offers improved robustness by considering uncertainties in model mismatch and uncertain weather forecasts. The 95% confidence region of model uncertainty is used to assess the robustness of simulations. The results show that the hybrid MPC approach is effective in maintaining temperature in the desired range and ensuring constraint satisfaction in controlling the temperature in a building.