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。

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.

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。

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.

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.

HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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 ofHVAC 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.

MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep reinforcement learning(DRL)methods have recently been proposed to address the HVAC control problem.However,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow convergence.In this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal comfort.MAQMC is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort violation.In addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility

We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown dynamics.It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning algorithm.We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather changes.To address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control parameters.Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.

极地LNG模块E-House HVAC系统设计

暖通空调(Heating,Ventilation and Air Conditioning,HVAC)系统是液化天然气(Liquefied Natural Gas,LNG)模块E-house的重要支持系统,也是维持LNG模块持续运行的关键。位于北极圈内的俄罗斯LNG开采、处理、运输模块,需要承受超低温极端恶劣工况,满足严格的防火防爆,及诸多俄罗斯本地法规要求。因此,通常的HVAC系统设计已无法满足其使用要求,亟待设计一套适用于极地LNG模块的HVAC系统:在系统计算上充分考虑极端运行环境因素,对比传统传热与逐时分析程序(Hourly Analysis Program,HAP)计算方法,介绍散热量选择和风量计算方法。此外,解析俄罗斯本地法规的特殊要求并提供相应的解决方案。研究成果可为极地LNG模块E-house的HVAC系统设计提供一定参考。

Preliminary Research on Water Supply,Drainage,and HVAC Equipment in Shanghai Historic Buildings During the Northern Warlord Period

During the Northern Warlord Period(1912–1928),the construction industry in Shanghai underwent robust development.As an integral element of buildings,equipment served both functional purposes and stood witness to the evolution of the economy and society,thus earning its place as part of the architectural heritage.However,due to various reasons,there are many loopholes in the protection of these building equipment.This paper examines the development of building equipment in Shanghai during the Northern Warlord Period,using water supply,drainage,and heating,ventilation,and air conditioning(HVAC)systems as examples.Through historical context analysis,it summarizes this development from a social-spatial perspective,infers the reasons behind it,and analyzes the importance of preserving such equipment,considering both past and present viewpoints.In this research,the importance of protecting historical building equipment is emphasized,which aims to give people a deeper understanding of their cultural value,and suggests that scholars conduct more practical research on their protection.

浅谈HVAC系统对住院患者健康的影响

论述了医院建筑供暖、通风和空调系统(HVAC)相关的室内环境参数对住院患者的影响。医院作为综合性的大型医疗场所,存在多种疾病源并存的风险,同时易感人群高度集中。对于空气暴露下患者的治疗及预后情况,感染源的风险防范,以及气候条件对于患者的不利影响都是医院建筑功能使用过程中面临的十分严峻的问题。综合工程项目经验,建筑HVAC系统的应用是一种对医疗环境很有效的适应措施,可以减轻环境风险以及气候变化对卫生医疗保健系统的不利影响。

基于先验知识的HVAC系统深度Q网络控制方法

强化学习方法在近年来被逐渐尝试应用于工程控制之中,并且展示出其强大的控制能力和潜力。强化学习算法虽然具有较好的控制性能,但是其控制策略的收敛需要建立在一段时间后的训练上,而这段时间在工程应用上可能会造成一些浪费。为了解决这一问题,提出了基于先验知识的DQN对暖通空调(HVAC)系统中的冷却水系统进行控制,将先验知识引入DQN算法的前期训练中,以减少训练时间,加速收敛,节约成本。基于先验知识的DQN算法不仅能够对系统进行有效的控制,并且能够比DQN更早地实现节能。

住院病房的暖通分析与基于SEIR模型疾病传染分析

基于CFD软件对住院病房进行暖通分析。通过建立物理模型和数学模型的方法,分析温度云图、气流组织云图以及污染物扩散云图总结规律。从暖通的角度出发利用SEIR模型在MATLAB软件中对比无防控措施下病毒传播与有防护措施下病毒传播人数感染情况,从而以小窥大反应暖通专业在控制疾病传播上有一定作用。

建筑区域广义储能资源的刻画与设计方法

面向碳中和目标,发展以风电、光电等可再生能源为主导的新型电力系统对于储能提出重大需求。建筑作为电力需求侧的主要部门之一,蕴含巨大储能潜力,不仅可实现自身柔性用能,还能主动参与外部电力系统调蓄。该文提出一种“等效电池模型”,定义等效充电功率、等效放电功率、等效储电量和等效综合效率,刻画建筑区域内3类最主要的虚拟储能资源(包括电动汽车+智能充电桩、暖通空调系统+建筑热容、可储能电器设备),并通过文献、实测和模拟数据给出典型工况下上述3类资源的关键参数取值范围。基于上述模型,提出建筑区域广义储能(包括传统储能和虚拟储能)的设计方法,实现减少柔性用能建筑的储能电池容量,并以典型办公建筑和住宅建筑为案例进行计算分析。提出的模型及方法可为城市能源系统的低碳设计规划提供有力支撑。

暖通空调给排水管道的表面改性与性能研究

为提升暖通空调铜合金排水管的表面性能,采用化学镀的方法在铜合金排水管表面制备了Ni-P/TiN镀层,研究镀液中TiN质量浓度对化学镀层物相组成、表面及截面形貌、硬度和耐磨性能的影响。结果表明,无镀层的铜合金基体主要物相为CuZn和CuZn5,表面镀层主要由非晶Ni组成;在基础镀液中适当添加TiN会形成镀层质量较好的含有胞状颗粒的镀层,而过量的TiN添加会使得镀层质量恶化。相较铜合金基体,表面镀层的硬度明显更高,且表面镀层硬度会随着镀液中TiN质量浓度的增加而增大;当TiN质量浓度为10 g/L时,表面镀层的硬度和磨损率分别为633 HV0.1和0.93×10^(-8)g/(r·N),具有相对较好的耐磨性,此时的磨损机制主要为氧化磨损。

新型模糊PID控制及在HVAC系统的应用

为了推广模糊控制器在非线性系统中的应用,提出一种利用PID控制器的参数优化和调节模糊控制器的新型设计方法.通过模糊控制器的结构分析建立与PID控制之间的精确解析关系之后提出基于PID控制增益因子的模糊控制器设计算法,然后利用改进的变论域思想进一步优化模糊控制器设计参数.将其应用于暖通空调(HVAC)系统的节能控制中并与常规PID控制器相比较,仿真和实验结果表明这种模糊控制器具有超调量小、跟踪迅速、鲁棒性强等优越的控制性能.