考虑舒适度的制造环境中HVAC节能优化控制研究

针对制造系统中HVAC高能耗的问题,通过分析制造环境中的热量对HVAC与温度的影响,建立了考虑舒适度的HVAC节能优化模型,并运用模拟退火算法优化目标函数。实验结果表明,HVAC节能优化模型不仅降低了5.9%的能耗,而且室内温度范围在28~29℃,符合节能与舒适的双标准。     

基于粒子群优化的室内动态热舒适度控制方法

针对预测平均投票数(predicted mean vote,PMV)值在舒适区和节能区之间周期性交替变化的控制方法,提出了基于PMV的动态舒适度冷/热抱怨模型和能耗模型.基于此模型,根据用户设定的舒适和节能两者的协调关系,运用改进的多目标离散粒子群优化算法,得出动态舒适度控制系统输入参数的寻优方法.该方法只需实时测量热环境和居住者热感觉数据,不需建立热环境物理解析模型,普适性强.实验证明了上述控制方法的有效性,该方法可实现动态舒适度的最优控制.     

基于PMV和视觉舒适度的家庭能耗管理

以PMV和韦伯-费昔勒定律为基础,建立热舒适度和视觉舒适度的模型,得到各种场景下热舒适度和视觉舒适度的公式和曲线,并以此为依据,用于家庭温控和光控系统能耗管理系统研究,设计了基于PMV控制和视觉舒适度控制的家居节能系统。结果表明,这种以家庭成员的热感觉和视觉舒适为目标的控制系统,兼具节约能源和有益健康的两重收益。     

变风量空调系统房间舒适度温度优化设置

房间温度是变风量空调系统的重要控制量,对舒适度和空调能耗影响很大.房间温度的设置应该考虑室内人员不同的舒适度需求、空气品质和变风量空调系统的节能运行,是一种多目标优化的结果.本文分析了PMV舒适度性能指标,提出便于优化运算的实用舒适度公式.综合考虑舒适度、空气品质和能耗三方面的因素,提出变风量空调系统房间舒适温度优化设置的方法,详细说明优化原理和优化步骤.通过仿真验证了优化方法的有效性,为变风量空调系统房间温度提供了一种可行的设置方法.     

民用飞机供水热技术仿真研究

研究民机供水系统温度加热控制的优化问题,保证用水的舒适度.针对管道加热器是非线性、时变的控制对象,为实现即热、舒适节能,提出设计采用模糊/PID开关切换和自适应模糊PID控制的两种系统模型控制器,建立仿真模型,研究模糊控制器控制规则和参数优化设计,比较分析两种系统模型控制方法与基本PID控制方法的水温控制性能.仿真结果表明,利用模糊/PID开关切换控制的民机供水温度控制系统可达到供水即热,节能省水的效果.     

基于舒适节能控制的分体式空调建模与仿真

分体式空调在住宅和办公室应用非常广泛,耗费电能数量巨大。然而绝大部分空调并不具备最佳运行策略,在运行过程中存在着很大的电能浪费问题,因此采用合适的空调控制策略使空调运行在既节能又舒适的状态上就显得尤为重要。于是着眼于保障舒适性并降低房间空调器的能耗,首先分析空调运行状态,建立房间环境模型和分体式空调运行模型,然后通过按需启停和适当调整空调温度设定值等方法对空调进行控制,最后对仿真结果进行分析得出使室内环境维持舒适且节能的基于规则的最优运行控制策略。     

基于DDPG的冷源系统节能优化控制策略

针对传统冷源系统节能优化方式机理建模复杂,缺乏自我学习能力,优化速度较慢等问题,提出一种基于数据驱动和自我学习机制的冷源系统节能优化控制策略,设计冷源马尔可夫决策过程模型,并采用深度确定性策略梯度算法(DDPG)解决维数灾难与避免控制动作离散化问题.以夏热冬暖地区某大型办公建筑中央空调冷源系统为研究对象,对冷源系统控制策略进行节能优化,实现在满足室内热舒适性要求的前提下,减少系统能耗的目标.在对比实验中,DDPG控制策略下的冷源系统总能耗相比PSO控制策略和规则控制策略减少了6.47%和14.42%,平均室内热舒适性提升了5.59%和18.71%,非舒适性时间占比减少了5.22%和76.70%.仿真结果表明,所提出的控制策略具备有效性与实用性,相比其他控制策略在节能优化方面具有较明显的优势.     

采用预测控制的地铁节能优化控制算法

本文针对地铁列车自动运行系统(automatic train operation,ATO)一般运行情况以及晚点延迟发车情况下的节能问题,基于预测控制算法设计了地铁节能优化控制算法.利用预测控制算法的在线滚动优化特性,通过设计含有能量消耗趋势优化项的控制目标函数,控制算法能够针对节能目标实现快速动态调整.通过调节目标函数中各优化项权重的相对大小,节能算法可以在满足列车时间与路程运行指标的同时,达到降低能耗的目的.在MATLAB平台上利用真实车辆模型对提出的节能优化控制算法进行了仿真,在列车不延迟与延迟的情况下,算法都很好地平衡了跟踪目标与节能目标,为地铁能耗动态优化控制提供了可行方案.     

面向智慧建筑的热舒适度管理系统的设计

为了满足智慧建筑中以人为本的调控理念,同时有效地减少能源的损耗,本文设计开发了面向智慧建筑的热舒适度管理系统。该系统关注人在建筑空间中的热舒适度,利用物联网采集的环境数据与室内人员热感觉反馈数据形成热舒适需求模型,便于建筑物制定绿色高效的干预策略。     

多全热交换机的运行管理与状态监控系统的设计

创造健康、舒适、高效的室内环境是现代绿色建筑体现"以人为本"的主要目标之一,室内空气质量是室内环境中的关键问题。描述了在室内通过无线传感器检测技术,经ARM嵌入式系统的处理,控制全热交换机,并同时通过LonWorks总线监控多台全热交换机运行状态,从而有效控制室内空气质量并达到高效节能的目的。     

Predictive control of multizone heating,ventilation and air-conditioning systems in non-residential buildings

In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to heating, ventilation and air-conditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the predicted mean vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controller's internal models. A genetic algorithm allowed the optimization problem to be solved. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. So, the simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management.     

Low computational cost technique for predictive management of thermal comfort in non-residential buildings

In France, non-residential buildings account for a significant part of energy consumption. A large part of this consumption is due to HVAC (Heating, Ventilation and Air-Conditioning) systems, which are in most cases poorly handled. The present work deals with an efficient approach allowing energy consumption to be minimized while still ensuring thermal comfort. We propose a predictive control strategy for existing zoned HVAC systems and consider the PMV (Predicted Mean Vote) index as a thermal comfort indicator. In order to test this strategy, we modelled a non-residential building located in Perpignan (south of France) using the EnergyPlus software. The twofold aim is to limit the times during which the HVAC sub-systems are turned on and to ensure a satisfactory thermal comfort when people are working in the considered building. This predictive approach, computationally tractable, allows thermal comfort requirements to be met without wasting energy.     

Thermal comfort control using a non-linear MPC strategy: A real case of study in a bioclimatic building

The aim to maintain thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of an HVAC (heating, ventilation and air conditioning) system. This problem is being widely analyzed, since it has a direct effect on users’ productivity, and an indirect effect on energy saving. This paper presents a hierarchical thermal comfort control system with two layers. The upper layer includes a non-linear model predictive controller that allows to obtain a high thermal comfort level by optimizing the use of an HVAC system in order to reduce, as much as possible, the energy consumption. On the other hand, the lower layer is formed by a PID (proportional, integrative and derivative) controller with anti-windup function which is in charge of reach the setpoints calculated by the non-linear model predictive controller. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a bioclimatic building are included and commented.     

Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method

In the research domain of intelligent buildings and smart home, modeling and optimization of the thermal comfort and energy consumption are important issues. This paper presents a type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption. First, we propose a methodology to convert the interval survey data on thermal comfort words to the interval type-2 fuzzy sets (IT2 FSs) which can reflect the inter-personal and intra-personal uncertainties contained in the intervals. This data-driven strategy includes three steps: survey data collection and pre-processing, ambiguity-preserved conversion of the survey intervals to their representative type-1 fuzzy sets (T1 FSs), IT2 FS modeling. Then, using the IT2 FS models of thermal comfort words as antecedent parts, an evolving type-2 fuzzy model is constructed to reflect the online observed energy consumption data. Finally, a multiobjective optimization model is presented to recommend a reasonable temperature range that can give comfortable feeling while reducing energy consumption. The proposed method can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.     

大型公共建筑能耗动态模型的研究与应用

通过分析大型公共建筑能耗数据所具有的海量性、随机性、序列性等特征,建立了基于多元线性回归与一阶自回归算法的大型公共建筑能耗动态模型。研究了模型参数估计问题,结合F与χ2两种方法,理论验证了模型显著性与有效性;根据西安市某大型公共建筑能耗数据,实验验证了该模型的逼真性、可行性与强健性。因此,该模型能对既有与新建大型公共建筑能耗进行实时动态预测,解决了节能定量化研究、定额用能的“瓶颈”问题,为大型公共建筑能耗审计及等节能制度的实现提供科学指导。     

基于事件驱动深度强化学习的建筑热舒适控制

住宅暖通空调系统通常耗用大量能源,同时也极大地影响居住者的热舒适性。目前,强化学习广泛应用于优化暖通空调系统,然而这一方法需要投入大量时间和数据资源。为了解决该问题,提出了一个新的基于事件驱动的马尔可夫决策过程（event-driven Markov decision process,ED-MDP）框架,并在此基础上,提出了基于事件驱动的深度确定性策略梯度（event-driven deep deterministic policy gradient,ED-DDPG）方法,通过事件触发优化控制,结合强化学习算法求解最优控制策略。实验结果显示,与基准方法相比,ED-DDPG在提升学习速度和减少决策频率方面表现出色,并在节能和维持热舒适方面取得了显著成果。经过实验验证,该方法在优化住宅暖通空调控制方面展现出强大的鲁棒性和适应性。     

Integrating scheduling and control for economic MPC of buildings with energy storage

In this paper, we discuss Economic Model Predictive Control (E-MPC) in the context of buildings with active energy storage. In particular, we propose a strategy for the optimal control of building Heating, Ventilation and Air Conditioning (HVAC) systems with chilled water thermal energy storage (TES). Owing to the multiple time scale dynamic behavior of buildings, coupled with the need to account for potentially extended forecasts of disturbances (e.g., weather, energy prices), the implementation of a centralized E-MPC must consider a relatively long prediction horizon. In turn, this results in computational difficulties that impede on real-time implementation. Computational complexity is further increased by the presence of integer decision variables, related to on/off states and operating modes in the HVAC and TES systems. In response to these challenges, we introduce a novel hierarchical E-MPC framework based on (i) establishing the optimal operation of the TES by solving a dynamic scheduling problem in the slow time scale, and (ii) using a control scheme with a shorter horizon in the fast time scale, which addresses objectives related to maintaining the indoor air temperature within comfort bounds at all times during the day. A simulation case study concerning the operation of a TES system at the University of Texas Thermal Façade Laboratory is presented, showing excellent computational and control performance.