采用室内热舒适性控制的变风量空调系统节能控制研究

在对变风量空调系统及控制系统分析的基础上,利用ＤＤＣ控制器可采集多点和多种信号的优点,提出采用室内热舒适性控制取代室内温度控制的控制方案。仿真试验结果证明,同常规的室内温度控制方案相比,室内热舒适性控制方案可以较好地改善室内的热舒适性,同时,在保证室内热舒适性前提下,采用室内热舒适性控制方案不仅能够保证控制的稳定性,而且有较好的节能作用。     

室内自然通风环境热舒适性研究

通过建立室内自然通风模型,研究了系统在不同温度、不同进风口风速及不同外窗开度情况耦合工况下,人体热舒适感受能够承受的温度上限和空气流速与外窗开度的适用范围。结果表明,室内热舒适性会随室外温度升高而明显恶化,温度达28.5℃时超过热舒适性指标国家标准推荐值;各因子中,风速及温度对室内热舒适性影响比重相对较轻,外窗开度的影响最大;20%为外窗开度下限,随开度的增大室内热舒适性增强;风速增加带来的不适"吹风感"也需考虑,该个体差异性感受会使得室内热舒适性迅速下降。     

严寒地区近零能耗建筑围护结构热湿传递对室内舒适性影响的研究

近零能耗建筑围护结构是复合多孔介质,研究水分透过墙体对建筑物内部环境的影响机理。以沈阳市某近零能耗建筑为例,采用WUFI-Plus软件对建筑内环境参数进行模拟,比较了考虑热湿传递与不考虑热湿传递对该建筑室内舒适性的影响。结果表明:建筑围护结构的热湿传递作用对室内相对湿度情况影响明显,应在空调设备、系统选型和环境预测分析时加以考虑,以保证室内舒适性。     

太阳能供暖系统的室内舒适性研究

以太阳能供暖系统的室内舒适性为研究对象,首先建立系统各部分的动态模型,其次运用动态仿真的方法结合室内舒适性的特点和要求提出合理的室内最佳空气温度及室内供回水温度,最后探讨和分析影响系统运行的因素;仿真结果揭示出服装热阻、低温地板的供回水温度、系统流量等对室内的舒适性和系统运行稳定性的影响。本研究对我国建筑室内环境热舒适的研究和评价标准的改进具有参考价值,并可为以后的研究提供参考和理论支持。     

多孔太阳墙结构热特性及应用分析

结合多孔太阳墙建筑,运用数值模拟方法分析了不同太阳墙结构对室内送风参数的影响,比较了太阳能房间与普通房间在工作区范围内的热舒适性,并对太阳能房间的窗墙面积比进行了优化改进。研究表明:对太阳墙的设计优化,增大了太阳墙面积,提高了室内热舒适性;改进后房间窗墙比为0.303,窗地比为0.2,符合节能建筑要求。     

夏季自然通风对农宅室内热环境影响的研究

以山西省运城地区农家住宅为研究对象,叙述了进行室内热环境参数现场测试的过程,分析了不同自然通风方案对室内能耗、舒适性及人工调节便捷性的影响特征,提出了运城地区夏季室内自然通风的理想调节方式。     

小型会议室夏季空调气流组织及热舒适性研究

利用计算流体力学的方法，通过Airpak软件对3种气流组织形式（同侧送排风、异侧送排风和置换通风）下小型会议室夏季室内热环境进行数值模拟，得到不同气流组织形式下的室内温度、速度、PMV值和PPD值分布，对比分析不同气流组织的人员热舒适性。结果显示：室内供冷工况下，会议室采用置换通风形式满足的热舒适等级更高，且室内的气流速度波动较小，能够较好地满足人体热舒适要求，是理想的气流组织形式。     

高大空间中太阳辐射对热舒适的影响及室内参数设计

选取航站楼和客站中典型的高大空间,实测夏季太阳辐射的分布情况;根据太阳辐射对人体表面的传热量,采用等效辐射温度来定量刻画太阳辐射对室内人员热舒适的影响,进而利用该参数提出适用于高密度太阳辐射环境的室内热环境设计方法。以典型高大空间为例,比较常规全空气空调方式和辐射地板供冷方式在太阳辐射情况下适宜的室内设计参数。通过分析和实测表明,辐射地板供冷能有效处理高密度太阳辐射热量、提高室内人员热舒适性。     

严寒地区居住建筑气密性与室内热舒适性模拟研究

为完善适用于我国严寒地区居住建筑气密性与室内热舒适之间的关系,以沈阳市某小区一户住宅为模拟对象,对严寒地区居住建筑气密性与室内热舒适之间的关系进行探究。模拟分为冬季工况、夏季工况和过渡季工况,针对住宅内南北侧典型房间室内热舒适随气密性等级不同的变化情况进行模拟分析。研究发现,不同季节条件下,室内热舒适度随气密性变化情况不同,其中以冬季最为明显,过渡季次之,夏季建筑气密性的提升对室内热舒适的影响相对较小。     

兰州市某高校学生公寓春季热舒适调查研究

为探究高校学生公寓春季室内热舒适状况,采用现场测试与问卷调查相结合的方法对兰州市某高校14间学生公寓室内热环境状况进行了现场调查研究,共获得181份有效人体热反应样本。运用统计分析法对受试者的热感觉、衣服热阻与操作温度进行了回归分析。结果表明,春季公寓内学生着装的平均服装热阻为0.689clo,90.1%的学生对室内20.4℃的平均温度表示接受;实测热中性温度为17.8℃,预测热中性温度为19.8℃,所期望的室内温度为18.7℃;80%的学生可接受的操作温度范围是17.7~22.1℃,其热接受温度下限比同属寒冷地区西安市的高3.2℃。该研究结果可为兰州高校学生公寓室内热环境的控制和制定其室内热舒适标准提供参考。     

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.     

Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.     

Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic

Saving consumable energy and maintaining the thermal comfort level are two main topics in the heating, ventilating and air conditioning (HVAC) control field. The reliability of the controller is important as well. This paper proposes a least enthalpy estimator (LEE) that combines the definition of thermal comfort level and the theory of enthalpy into a load predicting way to provide timely suitable settings for a fan coil unit (FCU) fuzzy controller used in HVAC. According to the settings, including temperature and relative humidity, the fuzzy controller can make decisions and adjust the output of the FCU system. From actual experiments, the LEE-based FCU fuzzy controller can achieve the requirements of the FCU control system such as thermal comfort, energy efficiency and reliability.     

System optimization for HVAC energy management using the robust evolutionary algorithm

For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation–optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of HVAC engineering optimization problems.     

室温传感器偏差故障对武汉地区暖通空调系统的影响研究

暖通空调系统的高效节能运行高度依赖于传感器测量的准确性。在传感器全寿命运行周期中,不可避免发生各种故障,影响其准确性。为探究传感器故障对不同暖通空调系统的影响,文章以室温传感器偏差故障为例,针对武汉地区某办公建筑,同时开展地源热泵和"冷水机组+锅炉"两种暖通空调系统形式的能耗建模,对比分析-5℃~+5℃偏差故障对两种系统运行能耗、工作性能及室内热舒适性的影响差异。结果表明:室温传感器故障的偏差幅值方向对两种系统运行能耗、工作性能及室内热舒适性的影响规律不同。其中,地源热泵系统能耗受室温传感器偏差故障影响相对更小。     

粒子群优化的模糊控制器设计

为避免模糊控制器设计中参数的复杂调试,并使其获得最佳控制性能,应用新颖的粒子群优化算法对模糊控制器参数进行优化设计。针对常规模糊控制器稳态精度欠佳的弱点,采用模糊控制与PID控制相结合的双模控制以有效消除静态偏差。通过对具有严重参数不确定性、多扰动以及大迟延的电厂主蒸汽温度被控对象的仿真研究,表明粒子群算法寻优速度快,计算量小,对模糊控制器参数的优化设计是非常有效的,使得主汽温控制系统在不同负荷下均获得很好的调节品质。图6表2参8     

模糊控制理论在电厂空调控制系统中的应用研究

阐述了一种混合参数自调整模糊控制方法,以及基于该控制策略在山西永济电厂HVAC项目中的实际应用,介绍了整个系统的硬件配置、系统特点以及应用效果。     

Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control

This research accounts for the outcome of a major cloud-based smart dual fuel switching system (SDFSS) project, which is a dual-fuel integrated hybrid heating, ventilation, and air conditioning (HVAC) system in residential homes. The SDFSS was developed to enable optimized, flexible, and cost-effective switching between the natural gas furnace and electric air source heat pump (ASHP). In order to meet the optimal energy consumption requirements in the house and provide thermal comfort for the residents, various high-quality sensors and meters were installed to record multiple data points inside and outside the house. The performance of the system was monitored in the long term, which is a common practice in energy monitoring projects. Outdoor temperature data plays the most crucial role in operating HVAC systems and also is a key variable in the decision-making algorithm of the SDFSS controller. Therefore, this study introduces an innovative and unique approach to obtain the outdoor temperature that could potentially replace high precision sensors with a data-driven model utilizing weather station data at a time resolution of 2 minutes and 1 hour. In this work, a series of artificial neural network algorithms were developed, optimized, and implemented to predict the outdoor temperature with an average of 0.99 coefficient of correlation (R), 1.011 mean absolute error (MAE), and 1.315 root mean square error (RMSE). It has been demonstrated that the developed ANN is a reliable and powerful tool in predicting outdoor temperature. Thus, the proposed model is strongly suggested to be implemented as an alternative to temperature sensors in hybrid energy systems or similar systems requiring accurate ambient temperature measurements.     

Predictive control of solid oxide fuel cell based on an improved Takagi-Sugeno fuzzy model

Thermal management of a solid oxide fuel cell (SOFC) stack essentially involves control of the temperature within a specific range in order to maintain good performance of the stack. In this paper, a nonlinear temperature predictive control algorithm based on an improved Takagi-Sugeon (T-S) fuzzy model is presented. The improved T-S fuzzy model can be identified by the training data and becomes a predictive model. The branch-and-bound method and the greedy algorithm are employed to set a discrete optimization and an initial upper boundary, respectively. Simulation results show the advantages of the model predictive control (MPC) based on the identified and improved T-S fuzzy model for an SOFC stack.     

Control strategy and experimental study on a novel defrosting method for air-source heat pump

A new defrosting method – the sensible heat defrosting method, aiming at shelving the various disadvantages of the conventional reverse cycle defrosting was proposed in this paper. The mechanism and process of this method was analysed. To guarantee the reliability, the self-organizing control algorithm with self-learning function was introduced based on the cardinal fuzzy control algorithm. Moreover, the control strategy was enacted; the corresponding self-organizing fuzzy control system was developed; the Micro Controller Unit (MCU) based control unit was accomplished; and the experimental study was conducted to investigate the sample machine of air-source heat pump system. The results of the experiments showed that the self-organizing control algorithm has good control characteristic and effect. On one hand, the adverse shock from the conventional reverse cycle defrosting to the refrigeration system could be avoided through this proposed method; on the other hand, the “oil rush” could also be eliminated. Besides, the thermal comfort could be greatly improved since the temperature fluctuation range of the supplied water is narrowed by applying this new method in practice.