Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Energy Performance Analysis of Ice Thermal Storage for Commercial HVAC Systems

Ice thermal storage is a promising technology to reduce energy costs by shifting the cooling cost from on-peak to off-peak periods. The paper investigates the application of ice thermal storage and its impact on energy consumption, demand and total energy cost. Energy simulation software along with a chiller model is used to simulate the energy consumption and demand for the existing office building located in central Florida. Furthermore, the study presents a case study to demonstrate the cost saving achieved by the ice storage applications. The results show that although the energy consumption may increase by using ice thermal storage, the energy cost drops significantly, mainly depending on the local utility rate structure. It found that for the investigated system the annual energy consumption increases by about 12% but the annual energy cost drops by about 3 6%.

Active-Disturbance-Rejection-Control for Temperature Control of the HVAC System

Heating, ventilation, and air conditioning (HVAC) system is significant to the energy efficiency in buildings. In this paper, temperature control of HVAC system is studied in winter operation season. The physical model of the zone, the fan, the heating coil and sensor are built. HVAC is a non-linear, strong disturbance and coupling system. Linear active-rejection-disturbance-control is an appreciate control algorithm which can adapt to less information, strong-disturbance influence, and has relative-fixed structure and simple tuning process of the controller parameters. Active-rejection-disturbance-control of the HVAC system is proposed. Simulation in Matlab/Simulink was done. Simulation results show that linear active-rejection-disturbance-control was prior to PID and integral-fuzzy controllers in rising time, overshoot and response time of step disturbance. The study can provide fundamental basis for the control of the air-condition system with strong-disturbance and high-precision needed.

An Approach to Optimize Multi-family Residence HVAC Systems Using Digitalization

As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.

EVALUATION OF SMART BOOSTER FANS AND DAMPERS FOR ADVANCED HVAC SYSTEMS

There is potential to significantly reduce CO_(2) emissions by increasing the efficiency and reducing the duty cycle of HVAC systems by using smart booster fans and dampers.Smart booster fans fit in the vents within a home,operating quietly on low power(2W)to augment HVAC systems and improve their performance.In this study,a prototype duct system is used to measure and evaluate the ability for smart booster fans and dampers to control airflow to different vents for the purpose of increasing the efficiency of HVAC systems.Four case studies were evaluated:an HVAC system(1)without any fans or dampers,(2)with a fan installed in one vent,but without any dampers,(3)with dampers installed at the vents,but without any fans,and(4)with both fan and dampers installed.The results from both the experi-mental and numerical evaluation show that the smart booster fan and dampers can significantly improve the airflow at a vent that is underperforming.For example,the airflow at the last vent in a ducting branch was increased from 17 to 37 CFM when a smart booster fan was installed at this vent.Results from the numerical analysis show that for the case of an underperforming vent during the winter season the HVAC running time may be reduced from 24 hr/day to 5.6 hr/day.Furthermore,results from the numerical analysis show the HVAC running time is further reduced to 4.5 hr/day for cases 3 and 4.

Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different seasons.We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption data.To enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and deconstruction.This method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold cross-validation.Within the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models.The dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.

A systematic review on COVID-19 related research in HVAC system and indoor environment

The on-going COVID-19 pandemic has wrecked havoc in our society,with short and long-term consequences to people’s lives and livelihoods-over 651 million COVID-19 cases have been confirmed with the number of deaths exceeding 6.66 million.As people stay indoors most of the time,how to operate the Heating,Ventilation and Air-Conditioning(HVAC)systems as well as building facilities to reduce airborne infections have become hot research topics.This paper presents a systematic review on COVID-19 related research in HVAC systems and the indoor environment.Firstly,it reviews the research on the improvement of ventilation,filtration,heating and air-conditioning systems since the onset of COVID-19.Secondly,various indoor environment improvement measures to minimize airborne spread,such as building envelope design,physical barriers and vent position arrangement,and the possible impact of COVID-19 on building energy consumption are examined.Thirdly,it provides comparisons on the building operation guidelines for preventing the spread of COVID-19 virus from different countries.Finally,recommendations for future studies are provided.

A modified transformer and adapter-based transfer learning for fault detection and diagnosis in HVAC systems

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.

Study on the application of reinforcement learning in the operation optimization of HVAC system

Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems.

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

Fast prediction for multi-parameters(concentration,temperature and humidity)of indoor environment towards the online control of HVAC system

Heating,ventilation and air conditioning(HVAC)systems are the most energy-consuming building implements for the improvement of indoor environmental quality(IEQ).We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation,through the fast prediction methods by using low-dimensional linear ventilation model(LLVM)based artificial neural network(ANN)and low-dimensional linear temperature model(LLTM)based contribution ratio of indoor climate(CRI_((T))).To be continued for integrated control of multi-parameters,we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model(LLHM)and contribution ratio of indoor humidity(CRI_((H))),and thermal sensation index(TS)for assessment.CFD was used to construct the prediction database for CO_(2),temperature and humidity.Low-dimensional linear models(LLM),including LLVM,LLTM and LLHM,were adopted to expand database for the sake of data storage reduction.Then,coupling with ANN,CRI_((T)) and CRI_((H)), the distributions of indoor CO_(2) concentration,temperature,and humidity were rapidly predicted on the basis of LLVM-based ANN,LLTM-based CRIm and LLHM-based CRM respectively.Finally,according to the self-defined indices(i.e.,E_(V),E_(T),E_(H)),the optimal balancing between IEQ(indicated by CO_(2) concentration,PMV and TS)and energy consumption(indicated by ventilation rate,supplied air temperature and humidity)were synthetically evaluated.The total HVAC energy consumption could be reduced by 35%on the strength of current control strategies.This work can further contribute to development of the intelligent online control for HVAC systems.

BIM-based automated design for HVAC system of office buildings-An experimental study

Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency,the heating,ventilation,and air conditioning(HVAC)design process is still very time-consuming.In this paper,we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures.This framework includes the following automated processes:building information modeling(BIM)simplification,building energy modeling(BEM)generation&load calculation,HVAC system topology generation&equipment sizing,and system diagram generation.In this study,we analyze the importance of each process and possible ways to implement them using software.Then,we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach.The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence(Al)technology based on the traditional computer-aided design(CAD)process.Experimental results show that the automatic processes are feasible,compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour,and improve the efficiency.

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.

Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Building sector account for significant global energy consumption and Heating Ventilation and Air Conditioning (HVAC) systems contribute to the highest portion of building energy consumption. Therefore, the potential for energy saving by improving the efficiency of HVAC systems is huge and various fault detection and diagnosis (FDD) methods have been studied for this purpose. Although amongst all types of existing FDD methods, datadriven based ones are regarded as the most effective methods. As a relatively new branch of data-driven approaches, deep learning (DL) methods have shown promising results, a comprehensive review of DL applications in this area is absent. To fill the research gap, this systematic review with meta analysis analyses the relevant studies both quantitatively and qualitatively. The review is conducted by searching Web of Science, ScienceDirect, and Semantic search. There are 47 eligible studies included in this review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. 6 out of the 47 studies are identified as eligible for meta analysis of the effectiveness of DL methods for FDD. The most used DL method is 2D convolutional neural network (CNN). Results suggest that DL methods show promising results as a HVAC FDD. However, most studies use simulation/lab experiment data and real-world complexities are not fully investigated. Therefore, DL methods need to be further tested with real-world scenarios to support decision-making.

运行策略信息缺失条件下的HVAC系统设备模型校准方法

性能准确的HVAC系统仿真模型对于研究建筑需求响应具有重要意义,模型校准是提高仿真结果可靠性的关键环节,但现实中会遇到运行策略信息缺失的情况,严重影响了校准结果的有效性。本文提出了一种适用于实际运行策略信息缺失条件下的模型校准方法,在设备性能优化校准前,增加运行策略校验环节,以皮尔逊相关系数作为评价指标判断运行策略是否准确。利用该方法可以在实测数据集中识别出运行策略准确数据集,以此为基础进行设备性能优化校准,从而确保校准结果的有效性。以某制冷站的冷却塔模型校准为例,验证了所提方法的校准效果。该方法可推广于其他HVAC设备模型的运行台数策略验证与名义性能参数校准中。

船用HVAC风管减隔振工艺措施试验及分析

某船典型环境下,HVAC风管在管内风速风量的激励下,产生明显振动。采用安装橡胶减振元件及消音装置等措施后,HVAC风管振动下降最大达10%以上。本试验研究风管安装相关措施对管路振动的影响,发现橡胶垫的厚度、硬度等材料参数对风管路振动传递影响呈复杂关系。其中,橡胶垫的减隔振效果随着厚度的增加不断提升,最终趋于稳定效果,并且软管径向安装偏差对管路各方向振动亦有较大影响,为该型船减隔振措施提供一定参考。

考虑客流变化的机场航站楼HVAC系统动态控制仿真研究

机场航站楼是以建筑结构复杂、人流量大且流动速率高等为特征的重要交通基础设施,其暖通、通风和空调系统(HVAC)通常是预先安排的,无差别的控制大面积区域,忽略了航站楼内客流分布的不均匀特征。提出客流模拟和建筑能耗模拟相结合的方法,通过智能体仿真建立航站楼的客流模型,用所得到的客流数据动态控制航站楼的温度与新风量,并以哈尔滨太平国际机场为案例进行研究,从节能效益和热舒适性进行分析。研究结果为未来机场航站楼的HVAC系统的设计和优化提供支持,为机场能源管理提供更多可能。

ENERGY EFFICIENT SYSTEMS AND STRATEGIES FOR HEATING,VENTILATING,AND AIR CONDITIONING(HVAC)OF BUILDINGS

INTRODUCTION The heating,ventilating,and air conditioning(HVAC)systems maintain and control temperature and humidity levels to provide an adequate indoor environment for people activity or for processing goods.The cost of operating an HVAC system can be signifi cant in commercial buildings and in some industrial facilities.In the U.S.,it is estimated that the energy used to operate HVAC systems can represent about 50%of the total electrical energy use in a typical commercial building(Krarti,2000).It is therefore important that buildings designers recognize some of the characteristics of the HVAC systems and determine if any available design and operating options can be considered to improve the energy of these systems.

高温工业环境下HVAC系统能效提升策略

高温工业环境下HVAC系统面临高能耗、效率低下和维护成本高等挑战。为提升能效,可采用高效节能设备、热回收技术和相变材料等创新技术;系统优化设计包括分区控制、智能通风系统和绝热保温;智能控制与管理系统结合物联网技术、预测性维护系统和能源管理系统,实现精准控制和优化运行。这些策略不仅能显著降低能耗,还能提高系统稳定性和可靠性,为高温工业环境HVAC系统的可持续发展提供了有效解决方案。

HVAC(Heating,Ventilation and Air Conditioning,空气调节系统)现场设备——市场份额分析、行业趋势与统计

2024年HVAC(Heating,Ventilation and Air Conditioning,空气调节系统)现场设备市场规模预计为211.3亿美元,预计到2029年将达到287.2亿美元,在预测期内(2024-2029年)复合年增长率为6.3%。新冠肺炎疫情对暖通空调行业产生了重大影响,由于封锁限制和企业避免投资新设备,全球许多建设项目被迫暂停。