采暖通风与空调系统中冷却塔传热效率的回归模型

优化采暖通风与空调(HVAC)系统的控制策略能够节能降耗,但是HVAC系统模型的高复杂度不利于在规定时间内实施对HVAC系统控制策略的优化.为降低模型复杂度,提出HVAC系统中冷却塔水侧传热效率的回归模型,并利用回归分析得到模型的系数.数值分析显示,冷却塔水侧传热效率的回归模型的计算时间大约是原始模型计算时间的1%,所得数值结果与原始模型的相对误差小于0.4%.     

基于热舒适度的办公建筑HVAC系统节能控制

建筑节能控制是一个满足舒适需求条件下的多目标优化问题,然而对于缺失运行数据的新建建筑,如何控制供暖、通风和空调(HVAC)系统达到既舒适又节能的效果是一个控制难题.针对这个问题,本文首先建立了新建建筑空间模型,然后对该模型进行能耗仿真分析,在此基础上,提出基于人员热舒适度的模糊控制算法,得出最优控制区间,从而在较低能耗水平情况下获得更长的热舒适天数,达到既节能又舒适的目标.基于人员热舒适度的节能控制对建筑HVAC系统绿色运行具有促进作用.     

通风空调系统最优控制模型的开发及应用

对某一通风空调(HVAC)系统进行了实验,利用实验结果采用多变量自回归的方法开发了适用于HVAC系统控制的数学模型,该数字模型用于带有前馈补偿的线性二次高斯控制(LQG),控制房间的温度和湿度,大大改善了控制性能,提高了HVAC系统的稳定性.     

数据驱动的冷却水塔建模与节能优化操作

针对冷却水塔的节能操作给出了一种数据驱动的建模与优化方法。首先,基于冷却水塔实际运行数据,应用非负绞杀变量选择方法给出一个自适应模型用于描述冷却水塔过程,该模型对于冷却水塔出口水温具有良好的预测精度。根据变量选择结果,分析了外界空气温度与湿度对冷却能力的影响。然后,提出了基于模型的冷却水塔风机的优化操作策略,并进行实验将之应用于冷却水塔的操作。研究结果显示,基于模型的优化操作具有较大的节能空间。     

面向制造环境的工业机器人节能轨迹规划

在制造环境中,工业机器人节能轨迹规划的实际应用存在2个问题:机器人动力学参数未知;现有节能轨迹规划方法无法保证结果的稳定性.因此,本文提出了面向制造环境的工业机器人节能轨迹规划,包括基于平行BP(backpropagation)神经网络的近似动力学辨识和基于凸优化(CO)的节能轨迹求解法.以UR3机器人为实验平台,近似动力学模型的均方根误差(RMSE)可收敛至2.05×10?3 N·m;且凸优化轨迹规划的求解稳定性优于现有的参数化轨迹规划.实验结果表明:本文提出的节能轨迹规划方案,能应对制造环境中机器人动力学参数未知的情况,同时保证轨迹规划结果的稳定性,因此更适用于制造环境中的工业机器人.     

变风量空调末端双闭环系统的模型辨识和仿真

研究变风量空调末端部分控制系统的节能优化问题时,对于末端系统的优化控制应以系统中被控的风阀和房间模型为基础.采用西安建筑科技大学变风量空调实验平台,对末端风阀被控对象采用闭环间接法送行辨识.利用LabView软件对外环温度控制器进行在线仿真设计,创造闭环辨识性条件,建立被控室温房间对象模型.最后,在Sumlink工具箱中用辨识模型进行末端双闭环控制系统的仿真.仿真结果表明,辨识出的模型精确度较高.用于末端节能优化控制研究中可提升控制性能,并为变风量空调节能优化控制提供了参考依据.     

基于闭塞区间的高速列车运行时间与节能协同优化方法

提出了一种高速列车运行时间与节能协同优化方法.针对由动态调度层、优化控制层、跟踪控制层组成的列车运行控制与动态调度一体化结构,设计了面向动态调度层和优化控制层的列车运行时间调整策略和节能速度位置曲线.基于高速铁路闭塞区间,建立了列车区间模型和列车速度曲线节能优化模型.利用模型预测控制方法对列车区间运行时间进行调整,优化列车总延误时间;根据调整后的区间运行时间设计列车运行优化速度位置曲线,减少列车运行能耗.仿真算例验证了设计的运行时间与节能协同优化策略的有效性.     

基于ZigBee的HVAC系统温湿度节点设计

由于有限的监测点,故HVAC系统无法实现优化控制,系统耗能大。本文进行了基于无线ZigBee协议的传感器节点的开发,并在此基础上组成现场检测与调节无线传感器网络。给出了优化的HVAC系统结构,终端节点硬件设计和软件设计,并针对设计中的一些常见问题,进行了详细的说明。所设计的终端节点具有检测和调节功能,协调器节点具有网关功能。无线节点安装容易,设置方便,可以简化系统的维护,适用于楼宇自动化领域温湿度监控。     

列车节能运行决策问题的模拟优化研究

轨道交通运输耗能巨大,研究列车节能操作运行具有重要的理论意义和实用价值。从节能角度出发,分析列车运行过程中的能量转换机制,建立单列车耗能最低优化模型、多列车节能优化模型及列车延误多目标优化控制模型,针对模型本身及其约束条件的复杂性,提出基于改进布谷鸟优化算法与动态搜索方法的“模拟优化”求解方法,对列车节能运行决策问题进行求解,并通过与其他同类算法的比较,阐述了所提方法的优越性。得到列车在不同运行工况下的最优节能运行控制策略,确定各情况下列车运行的最优速度距离曲线,结果符合实际情况。改进算法的搜索效率更高,研究思路与模型对于列车节能操作运行具有一定的借鉴意义,所提出的针对复杂优化模型的求解方法合理有效,适用性强,有一定的参考价值。     

面向多搬运任务的柔性制造车间多载具AGV节能路径规划

自动导引搬运车（automated guided vehicle,AGV）能够灵活、准确、高效地进行物料搬运,被广泛应用于柔性制造车间。多载具AGV能同时搬运多个工件,具有较强的搬运灵活性,其路径规划问题的复杂性和多约束性更强,求解难度更大。针对柔性制造车间多载具AGV节能路径规划问题,首先建立了以能耗和搬运距离为优化目标的AGV节能路径规划模型;然后,提出了一种改进Dijkstra算法和非支配排序遗传算法（non-dominated sorting genetic algorithm-II,NSGA-II）集成的多载具AGV节能路径规划方法;最后通过案例验证了模型的节能效果和求解方法的有效性。     

Effects of dead-band and set-point settings of on/off controllers on the energy consumption and equipment switching frequency of a residential HVAC system

Residential heating, ventilation and air conditioning (HVAC) systems generally employ simple on/off controllers to regulate the temperature of water and air in different subsystems. Selection of set-point of a controlled process and dead-band of controller affects the process regulation, energy consumption and actuator switching frequency. This article presents a calibrated model of a residential HVAC system. Temperature of two zones and buffer tank (BT) is regulated using on/off controllers. Non optimum controller settings result in poor regulation, higher energy consumption and higher equipment wear. The purpose of this article is to find optimum set-point and dead-band settings for on/off controllers in order to improve temperature regulation, reduce energy consumption and decrease equipment wear by reducing the switching frequency of HVAC equipment without scarifying thermal comfort of occupants.     

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.     

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.     

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。     

A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost

NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.     

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.     

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.     

Grey prediction on indoor comfort temperature for HVAC systems

This paper describes determination of indoor comfort temperature for efficiently Heating, Ventilating, and Air-Conditioning (HVAC) system under dynamical environment. Making occupants’ satisfaction on thermal comfort is still challenging by how the temperature setpoint of the fresh made-up air in conventional HVAC systems can be adapted properly (normally fixed) when surroundings changes in time. Essentially, being unknown ahead of time, the outdoor temperature is systematically predicted by grey prediction model in this work. The Adaptive Comfort Theory (ACT) model captures relation of the indoor comfort temperature to the outdoor temperature based on the survey data on thermal comfort in real occupants’ living environment. With the grey prediction model and the ACT model, the predicted indoor comfort temperature can be implemented as the comfort temperature reference for HVAC control systems. The experiment results show the viability of the proposed methodology for efficient HVAC control system.     

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。