HVAC Systems Research Articles

Quantifying the potential of load flexibility for building HVAC system using model predictive control strategy

Building as a Battery (BaB) is a concept aimed at utilizing building energy systems for load shifting purposes. However, there is a lack of study to accurately quantify building load flexibility and analyze how it is impacted by various factors. This study addresses this gap by first quantifying building load flexibility using battery parameters, specifically charging power, energy storage capacity, and round-trip efficiency. Subsequently, an analysis is conducted to examine the impacts of internal and external factors on building load flexibility, Specifically, we studied how ToU utility structure, load shifting duration, comfort range, ambient weather conditions, and building thermal properties influence charging power, energy storage capacity, and round-trip efficiency. Our simulation results revealed that an increase in the peak-to-valley ratio, load shifting duration, building thermal capacity, and cooling demand leads to higher charging energy and energy storage capacity, while an increase in building thermal resistance and widening of the comfort range result in their decrease. Regarding the round-trip efficiency, we found most parameters, except for building thermal capability, have a positive relation with the round-trip efficiency. Furthermore, sensitivity analysis showed that ToU utility structure and ambient weather conditions have the largest impacts on charging power, energy storage capacity, and round-trip efficiency. Additionally, our study indicates that leveraging buildings as virtual batteries for peak demand reduction is a feasible and cost-effective method compared with other energy storage techniques such as electric batteries. Overall, our findings offer valuable insights for parameterizing building load flexibility using battery parameters, analyzing factors that impact building load flexibility and informing grid operator to design property utility structures to meet their load shifting goals.

Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control

With the widespread application of solar-energy-air-source heat pump composite heating, optimizing the regulation of air temperature in heating zones, enhancing system thermal comfort, and reducing energy consumption have become crucial. To ensure residential comfort while minimizing HVAC system energy costs, a control method combining guided policy search (GPS) with model predictive control (MPC) is proposed for composite heating systems. This method replaces state estimation in MPC with policy search and substitutes the offline trajectory optimization used in guided policy search with MPC, thus integrating the strengths of both approaches. This strategy not only improves control precision but also reduces energy consumption and enhances system robustness. The GPS-MPC control algorithm was validated against reinforcement learning and MPC. A simulation model was developed and validated on a real physical platform. Simulations compared the control effects of on-off control and MPC on pump frequency, flow rate, stratified thermal storage tank temperature, component, and system energy consumption. The data results indicate that the GPS-MPC algorithm offers superior predictive accuracy, efficiency, and robustness in composite heating control systems compared to conventional methods. Under the GPS-MPC strategy, indoor temperature fluctuations, pump frequency response speed, and energy consumption were significantly improved, with temperature fluctuation limited to only 0.8 °C, and the system achieving energy savings of over 12 %.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

Optimal control strategy for building HVAC systems: Satisfying flexible demand response with different value-based selection

Growing renewable energy integration leads to significant fluctuations in electricity generation. To address this challenge, market-based mechanisms for dynamic carbon emission factors and electricity prices are emerging, particularly in regions like China pursuing carbon neutrality. Buildings, with HVAC systems as the largest consumers, require flexible Demand Response (DR) strategies to balance these fluctuations while maintaining occupant comfort. This paper proposes a hierarchical control framework for building HVAC systems. It optimizes operation for various objectives (minimizing energy consumption, reducing electricity costs, and lowering carbon emissions), while ensuring thermal comfort. The framework involves three modules. First is the time-based demand allocation, which uses genetic algorithms to optimize energy use based on future cooling demands, indoor temperature, and fluctuating electricity and carbon prices. It provides building-level temperature setpoints and electricity consumption plans. Second is the spatial indoor demand adjustment, which balances overall cooling load and temperature satisfaction across individual spaces, taking into account room-specific costs and difficulty confidents for temperature adjustments. This module assigns future temperature setpoints to each space. Third is HVAC system follow-up control, which Implements the optimized setpoints for real-time control of the HVAC system. The framework’s effectiveness is verified through application to a real commercial building project. Compared to a baseline scenario, the optimal electricity cost strategy achieved a 6.5% cost reduction, and the optimal carbon emission reduction strategy achieved an 8.2% reduction. Real-world control based on carbon emission optimization resulted in an actual year-on-year carbon emission reduction of 8.7%.

User-centric approach to optimizing thermal comfort in university classrooms: Utilizing computer vision and Q-XGBoost reinforcement learning

The ’one-size-fits-all’ setting of heating, ventilation, and air conditioning (HVAC) systems in university classrooms not only compromises occupant comfort but also leads to potential energy wastage. This study proposes a user-centered approach to optimize the thermal environment in university classrooms by integrating computer vision and Q-XGBoost learning. Computer vision facilitates non-intrusive and real-time sensing of thermal comfort in dynamic university classroom environments. Simultaneously, the Q-XGBoost model identifies optimal operations for HVAC systems by learning from the interactions between occupants and their environment, ensuring a balance between comfort and energy efficiency. These innovative methodologies are harmoniously integrated into a smart air conditioning control box (SACC-Box), achieving intelligent operation of HVAC systems in the real world. Subsequently, a controlled study was conducted to test the user-centered approach’s performance in improving thermal comfort and energy efficiency. The results reveal that classrooms equipped with the SACC-Box significantly improve occupant satisfaction with thermal comfort by approximately 13.5 % while concurrently achieving up to a 5 % reduction in energy consumption compared to classrooms operating with traditional built-in smart control systems. This investigation offers a viable solution to balance energy efficiency and user comfort in university classrooms, underlining the transformative potential of integrating intelligent technologies that inspire a greener, more adaptable, and user-centric futures in higher education establishments.

Assessment of airborne transmitted infection risk in classrooms using computational fluid dynamics and machine learning-based surrogate modeling

In recent years, the assessment of airborne transmitted infection risk has been extensively performed using Computational Fluid Dynamics (CFD) simulations, especially in response to the COVID-19 pandemic. Nevertheless, the high computational demands and time-intensive nature of CFD simulations highlight the need for fast or real-time infection risk predictions. This capability is crucial for swift decision-making in dynamic environments where timely health interventions are critical. This paper presents a thorough analysis of airborne infection risks in classroom environments based on CFD simulations to understand key factors such as ventilation strategies, air change rates, occupant arrangements, source locations, and particle sizes. This study also employs data-driven supervised learning methods—specifically, Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN)—to generate surrogate models for predicting airborne infection risk. Key findings reveal that different ventilation strategies significantly affect airborne infection risk, reducing it by 49 %–77 %. Moreover, the conventional Wells-Riley model was identified as lacking in its ability to accurately predict local infection risks. The study further challenges the assumption that higher air change rates are universally beneficial, considering that occupants seated in the back rows of a classroom experienced up to a 166 % increased risk, despite elevating air change rates from 1.1 h−1 to 11 h−1. These results suggest that physical distancing alone may be insufficient and highlight the importance of considering other factors such as occupant arrangements. Regarding the model performance, the ANN-based surrogate model demonstrated varying prediction accuracy. For inhalable particle concentration predictions for susceptible occupants, R2 values ranged from 0.31 to 0.65 with CVRMSE values between 100 % and 180 %. In contrast, the model achieved an R2 of 0.79 and a CVRMSE of 34 % for infectors. The insights and methodologies from this study can inform HVAC system design and operation strategies to better mitigate infectious disease transmission in densely occupied indoor environments.

A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. Data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account; from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6.7% of the literature focus on the impacts of human behavior and climate change on building retrofits, respectively. In the future, it is necessary to give further consideration to user behaviors and long-term climate change in the retrofit process, in addition to improving the accuracy of optimization models and exploring the generalization and migration capabilities of surrogate models.

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Testing, Validation, and Simulation of a Novel Economizer Damper Control Strategy to Enhance HVAC System Efficiency

Buildings account for over 40% of global carbon dioxide (CO2) emissions, with supply and return fans in air handling units consuming a significant portion of energy. To address this, researchers have explored innovative economizer damper control methods and identified the “split-signal” strategy, which optimizes supply airflow using a single damper as a promising approach. In this study, split-signal was further refined for practical application and energy simulation, aiming to demonstrate its effectiveness and encourage adoption in real-world building mechanical systems. Laboratory testing on chilled water variable air volume (VAV) system showed fan energy savings of 0.2–5% compared to traditional “three-coupled” control, depending on ventilation air proportions, and prevented reverse airflow. A statistical regression model, based on experimental data, was developed to predict energy savings and streamline comparisons. Energy simulations were conducted across various U.S. climate zones and revealed potential savings of 15–20% in energy use, operational costs, and CO2 emissions. With minimal financial investment, split-signal control offers a cost-effective solution to improve energy efficiency and reduce environmental impact, promoting its adoption in real-world building applications.

Advances in fuzzy control systems for energy management in smart homes

The drive towards energy efficiency and sustainability has spurred technological innovations for managing energy consumption in residences, with fuzzy control systems standing out. These systems, based on fuzzy logic, offer adaptive and intuitive solutions to optimize energy use in smart homes, dealing with uncertainties and imprecisions in environmental conditions and occupant preferences. Fuzzy logic allows for refined control of devices such as lighting, heating, ventilation, and air conditioning (HVAC), adjusting in real-time to variations in conditions and user behaviors. Recent studies have demonstrated the effectiveness of these systems in various applications. Kontogiannis, Bargiotas, and Daskalopulu (2021) developed a fuzzy control system to recommend optimal energy consumption values based on environmental data, using the Mamdani approach and decision tree linearization. Keshtkar and Arzanpour (2017) introduced an autonomous thermostat combining supervised learning with wireless sensors and dynamic electricity pricing, adjusting temperatures and maintaining user comfort. Ain et al. (2018) proposed a Fuzzy Inference System that incorporates humidity and internal temperature variations to optimize energy consumption without sacrificing comfort. Additionally, Khalid et al. (2019) presented an energy management controller for smart grids using fuzzy logic and heuristic optimization techniques, improving load management and reducing costs and consumption. Collotta and Pau (2015) explored the integration of IoT technology with fuzzy-based systems using Bluetooth Low Energy (BLE) to manage energy consumption in home automation networks. These studies highlight that fuzzy control systems are crucial for optimizing energy consumption in smart homes, balancing efficiency and comfort, with ongoing advancements promising continuous improvements in energy management.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

Aerodynamic Size-Dependent Collection and Inactivation of Virus-Laden Aerosol Particles in an Electrostatic Precipitator.

Electrostatic precipitators (ESPs) may enable high particle collection efficiency with minimal pressure drop in HVAC systems. However, studies of pathogen collection and inactivation in ESPs at medium to higher flow rates are limited. Here, a single-stage, wire-plate ESP operated at flow rates of 51 and 85 m3 h-1 was used to study the removal of virus-laden aerosol particles for three different airborne viruses: (1) bovine coronavirus (BCoV), (2) influenza A virus (IAV), and (3) porcine reproductive and respiratory virus (PRRSV). Size-resolved measurements of collection efficiency were obtained using Andersen cascade impactors (ACI) sampling upstream and downstream of the ESP. All measurements were analyzed based on three distinctive but complementary methods: (1) fluorimetry to assess physical collection, (2) RT-qPCR to assess viral RNA concentrations and (3) virus titration to assess virus viability. In general, log reductions by virus titration were highest followed by those from RT-qPCR, and last fluorimetry, suggesting that a portion of virus may be potentially inactivated in flight in the ESP. An effective migration (deposition) velocity ranging from 3.10 to 10.05 cm s-1 was also determined using the spatially resolved measurements of virus collection on the ESP plates.

Determination of Turbulent Prandtl Number for Thermal Fluid Dynamics Simulation of HVAC Unit by Data Assimilation

Abstract Regarding high-precision temperature field prediction of a heating ventilation and air conditioning (HVAC) unit using thermal fluid dynamics simulation, the determination of the turbulent Prandtl number (Prt) was a key issue. In this study, we present an attempt to improve the accuracy of thermal fluid dynamics simulations in HVAC units by adjusting the Prt using data assimilation techniques. First, we simultaneously measured the velocity and temperature in the hot and cold air mixing zone of a simple HVAC model using particle image velocimetry and thermocouples. Second, we coupled data assimilation to the thermal fluid simulation model to determine the Prt in the mixing field. Finally, we proposed two functions of the Prt for high velocity and low velocity regions using multidimensional analysis. In the future, we believe that the Prt functions can be applied to thermal fluid simulations of actual HVAC unit to accurately predict performance without conducting prototype experiments, thereby contributing to reducing development cost and time of HVAC unit.

Progressive Dies for L-hanger Ducting (L-HD) Utilizing Low-Carbon Steel SPCC-SD Material: An Experimental and Numerical Analysis

Industrial developments, especially in the manufacturing and construction sectors, recognize L-hanger ducting as a critical component in HVAC (heating ventilation and air conditioning) ducting systems, which play a role in supporting and stabilizing air ducts. The L-Hanger ducting manufacturing process involves a series of stages, such as shearing, blanking, piercing, trimming, and bending processes. This research focuses on the design and simulation of dies and punches for piercing, blanking, and bending processes using 1.6 mm-thick SPCC-SD material. The aim of this research is to design and analyze progressive dies in order to increase the efficiency of the production process. A comprehensive calculation of the forces involved in the shearing, blanking, piercing, trimming, and bending processes is required in order to predict press machine tonnage requirements to support the production process. This research applies theoretical and numerical validation approaches. Theoretical analysis is used to calculate the overall forces, which are then compared with numerical results and verified through an experimental approach. By understanding and optimizing the design of progressive dies, it is hoped that we can increase the production efficiency of L-hanger Ducting and expand knowledge in the field of metal forming, contributing to the metal forming industry and supporting the development of science.

Energy, exergy, environment and economic (4E) analysis of PV/T module assisted vapor compression refrigeration system: An experimental study

Despite the rise in the prices of fossil fuels, the increase in their demand, and damaging the environment, a large part of the world's energy needs have been today met by fossil fuels. In this direction, interest in renewable energy sources has increased. Solar energy stands out among renewable energy sources because it is endless and clean. However, today, the use of solar energy is not used alone, but in combination with other thermal energy systems. In building applications, it is mostly used in heating, refrigeration, and HVAC systems, which have a high part in energy consumption. In this study, the solar energy and cooling system were not used separately as in previous studies but were used as a hybrid. The focus was on increasing the performance of both systems by operating them together. In this study, energy, exergy, thermoeconomic, and environmental analyses were applied to the PV/T-assisted vapor compression refrigeration system (PV/T-VCRS) at different storage temperatures (25 °C, 30 °C, and 35 °C). As a result, an 8.5 % lower surface temperature of the module in PV/T-VCRS 25 °C was measured compared to PV/T-VCRS 30 °C and 35 °C. In direct proportion to the module surface temperatures, 13 % better electrical efficiency was obtained in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. The COP value increased by 15.46 % in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. A 13 % improvement in exergy efficiency was observed in PV/T-VCRS 25 °C compared to 30 °C and 35 °C. The enviroeconomic parameter PV/T-VCRS is calculated as 15.17 ¢/h, 16.52 ¢/h, and 17.6 ¢/h for 25 °C, 30 °C and 35 °C. Another advantage of the system is that hot water is obtained at set temperatures. In PV/T-VCRS, 475 L, 300 L, and 210 L of hot water were obtained at 25 °C, 30 °C, and 35 °C, respectively. As a result, the performance of the PV/T-VCRS was good, with the added benefit of performing close to the performances when PV/T and VCRS were used separately.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

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.

Energy recovery ventilators to combat indoor airborne disease transmission: A sustainable approach

Ventilation plays a crucial role in preventing indoor airborne disease transmission. Nevertheless, ventilation increases the energy consumption of HVAC systems. Therefore, energy efficiency measures or alternative methods must be adopted to reduce the energy demand of HVAC systems, which is necessary to achieve sustainability in the building sector. This study proposes a method of utilizing an energy recovery ventilator (ERV) to provide supplementary ventilation to reduce airborne disease transmission. The proposed method is tested for an office building with one source room (with an infected occupant) and two connected rooms (no infection source). The contributions of the present study are (i) the development and verification of a new supplement ventilation method using an ERV to reduce the probability of infection from airborne pathogens and (ii) providing the economic and environmental benefits of the proposed method to promote its adaption by the building managers/HVAC engineers. The results of the present study show that the proposed method can reduce the probability of infection by 10 to 40% and demonstrate that utilizing an ERV is a sustainable and economical method to improve ventilation to reduce indoor airborne disease transmission.

Performance simulation and energy efficiency analysis of multi-energy complementary HVAC system based on TRNSYS

The failure rate and operating cost of the C-B (chiller-gas boiler) system, which has been in use for a long time, are increasing year by year, but there is a lack of reasonable programs and effective measures on how to retrofit the C-B system. In order to explore the prospect of ground-source heat pumps applied to the retrofit of C-B systems in HSCW (hot summer and cold winter) zone, this study designed a G-C-B (GSHP-Chiller-Boiler) system in a partial retrofit context and constructed a TRNSYS numerical simulation model of the retrofit system. Secondly, the performance and energy efficiency of the system is analyzed as well as different control strategies for the ground source heat pump cooling tower are discussed. Finally, the energy conversion efficiency of the chiller is compared based on the energy flow of the system. Studies have shown that the G-C-B system can achieve a seasonal performance factor of 5.3 during the cooling season and 4.1 during the heating season. Compared to the existing C-B system, year-round electricity consumption was reduced by 21 %, gas consumption by 92 %, and combined energy demand by 49.6 %. With the addition of GSHP, the energy conversion efficiency of the chiller has also increased by 21 %. The soil temperature of the G-C-B system increased by less than 0.5 °C after ten years of continuous operation. This paper can provide a valuable theoretical basis for the energy form modification of the C-B system in the HSCW zone.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.