基于强化学习的地铁站空调系统节能控制

地铁站空调系统能源消耗较大,传统控制方法无法兼顾舒适性和节能问题,控制效果不佳,且目前地铁站空调控制系统均是对风系统和水系统单独控制,无法保证整个系统的节能效果.鉴于此,提出基于强化学习的空调系统节能控制策略.首先,采用神经网络建立空调系统模型,作为离线训练智能体的模拟环境,以解决无模型强化学习方法在线训练收敛时间长的问题;然后,为了提升算法效率,同时针对地铁站空调系统多维连续动作空间的特点,提出基于多步预测的深度确定性策略梯度算法,设计智能体框架,将其用于与环境模型进行交互训练;此外,为了确定最佳的训练次数,设置了智能体训练终止条件,进一步提升了算法效率;最后,基于武汉某地铁站的实测运行数据进行仿真实验,结果表明,所提出控制策略具有较好的温度跟踪性能,能够保证站台舒适性,且与目前实际系统相比能源节省约17.908%.     

现用暖通空调节能设计措施探讨

随着科技的进步及能源短缺问题的突出,暖通空调节能设计引起普遍关注。暖通空调节不仅有控制建筑内空气湿度、气流速度与温度的优势,为人们创造舒适的居住与办公环境,也存在着能耗大,环境污染等弊端。因此,在我国能源紧张以及节能降耗政策的推动下,应不断优化与完善暖通空调节能设计,在不影响人们居住与办公舒适度的前提下降低暖通空调能源消耗。文章以当前暖通空调系统设计现状及其节能设计方向为基础,遵循暖通空调节能设计原则,提出了现用暖通空调节能设计的几点措施,以供同仁参考。     

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.     

零能耗小屋能源管理与智能监控系统的设计与实现

针对2013中国国际太阳能十项全能竞赛比赛规则及零能耗建筑的特点,结合比赛地点山西大同的气候数据,给出了厦门大学参赛作品,零能耗小屋Sunny Inside的能源管理与智能监控系统设计方案.以光伏建筑一体化理论为基础,设计15.25kW光伏并网发电系统,并用相关软件对能耗平衡进行了模拟验证.以三菱PLC为核心,构建了能源管理与智能监控系统,分析了不同条件下空调、相变及遮阳系统的控制策略,并给出具体实现方法.     

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.     

基于ATmega128的通信基站智能节能窗控制系统设计

介绍了一种采用ATmega128单片机设计的通信基站智能节能窗控制系统,通过传感器对通信基站的内外环境变量进行实时取样,让节能窗与空调联动工作,从而实现节能效果。实际应用表明,该系统在通信基站、机房等建筑内能明显减少空调运行时间,降低能耗。     

云环境下能耗感知的公平性提升资源调度策略

针对云计算环境下大量的能源消耗和一些用户非法占有计算资源的问题,为了节约资源,同时提高系统的公平性,使所有用户能合理地使用数据中心的所有资源,提出一种基于能耗感知的节能公平的资源调度算法（FGRSA）。该算法能高效调度各种类型的资源使所有资源使用量达到一个相对的公平。通过CloudSim仿真平台,对提出的调度策略进行仿真实验。实验结果表明,与Greedy算法和Round Robin算法相比,FGRSA能够在大幅度降低系统能耗的同时,保证各类型资源的公平使用。     

基于数据分析的楼宇智能节电管理系统

城市中高层楼宇增加是城市内用电量上升的重要因素之一。使用计算机系统对建筑执行能耗量化管理从而控制降低建筑运营过程中所消耗的能量,降低建筑的运营成本,提高能源使用效率,已经成为用户最为关注的问题。已有的楼宇节电管理系统收集建筑内部的信息并通过启发式规则对楼宇的照明、动力、通风、空调、安防等系统进行协调控制及整合,以期达到节能的目的。这类型系统的缺点在于（1）大部分楼宇缺少专职的节电管理机构,没有相关的电能使用及管理的方法。在各个楼宇与电力公司之间还没有形成一个有效的信息共享链,内外信息共享能力差,存在信息孤岛现象（2）系统中的节电模型没有学习过程,不能根据建筑情况的变化,自动调整节电模式。面对这样的情况,该文提出一个基于数据分析的建筑楼宇智能节电系统,该系统（1）使用正态分布拟合用户用能习惯,分析得到用户能耗较高的关键设备,发现设备的耗能过高原因;（2）建立双向交换子系统实现楼宇与电力公司之间的信息交换,并根据电力公司生产计划及时调整楼宇用能策略。在天津动漫大厦实际运行结果显示,该文设计的智能节电系统可以有效的节省楼宇中的电力损耗。     

基于数据分析的楼宇智能节电管理系统

城市中高层楼宇增加是城市内用电量上升的重要因素之一。使用计算机系统对建筑执行能耗量化管理从而控制降低建筑运营过程中所消耗的能量,降低建筑的运营成本,提高能源使用效率,已经成为用户最为关注的问题。已有的楼宇节电管理系统收集建筑内部的信息并通过启发式规则对楼宇的照明、动力、通风、空调、安防等系统进行协调控制及整合,以期达到节能的目的。这类型系统的缺点在于(1)大部分楼宇缺少专职的节电管理机构,没有相关的电能使用及管理的方法。在各个楼宇与电力公司之间还没有形成一个有效的信息共享链,内外信息共享能力差,存在信息孤岛现象(2)系统中的节电模型没有学习过程,不能根据建筑情况的变化,自动调整节电模式。面对这样的情况,该文提出一个基于数据分析的建筑楼宇智能节电系统,该系统(1)使用正态分布拟合用户用能习惯,分析得到用户能耗较高的关键设备,发现设备的耗能过高原因;(2)建立双向交换子系统实现楼宇与电力公司之间的信息交换,并根据电力公司生产计划及时调整楼宇用能策略。在天津动漫大厦实际运行结果显示,该文设计的智能节电系统可以有效的节省楼宇中的电力损耗。     

强化学习A3C算法在电梯调度中的建模及应用

为让电梯调度算法在电梯电力能耗、用户乘梯体验和算法适应性方面具备更好表现,在目前主流的电梯调度算法基础之上,提出对调度环境、电梯行为和调度目标3个方面进行统一建模的基于强化学习A3C的电梯智能调度算法.让调度电梯在不断地和环境交互学习过程中逐渐学习得到最优电梯调度策略,与基于具体环境建模的相关电梯调度算法进行对比实验,...     

Net-zero energy building clusters emulator for energy planning and operation evaluation

The emergence of smart grids, Net-zero energy buildings, and advanced building energy demand response technologies continuously drives the needs for better design and operation strategies for buildings and distributed energy systems. It is envisioned that similar to micro-communities in a human society, neighboring buildings will have the tendency to form a building cluster, an open cyber-physical system to exploit the economic opportunities provided by smart grids and distributed energy systems. To realize this building cluster envision, it requires better urban energy planning and operation control strategies to determine which type of buildings should be clustered and what operation strategies should be implemented to fully utilize the potential in load aggregation, load shifting, and resource allocation. However, most of the current tools are focusing on single buildings or devices, which are not suitable for building cluster studies. To this end, this study proposes to develop a Net-zero building cluster emulator that can simulate realistic energy behaviors of a cluster of buildings and their distributed energy devices as well as exchange operation data and control schemes with real-world building control systems. The developed emulator has the flexibility to integrate with different buildings and distributed energy systems to study the performance of this building cluster to propose suggestions in urban energy planning and operation. To show the application of this emulator, a proof-of-concept demonstration is also presented in this paper.     

数据仓库在城市公共建筑能耗管理中的应用

当前众多城市公共建筑能耗监测系统中已收集了大量的建筑能耗数据。针对这些数据源存在的各自独立而且分散,不能够提供全局的数据分析环境,不能够有效支持建筑能耗的评估与建筑节能的研究等问题,提出将数据仓库技术应用于城市公共建筑能耗监管系统的解决方法。通过对建筑能耗监测系统的研究,以及对建筑能耗管理的应用需求的调研,建立城市级公共建筑能耗数据仓库的多维数据模型,对主题设计、指标设计和维度模型设计进行了探讨,并在实验阶段已成功构建了某高校公共建筑能耗数据仓库的实例。实验结果表明,该方法能够有效地为建筑能耗的管理与决策提供良好的数据分析环境。     

The user side of sustainability: Modeling behavior and energy usage in the home

Society is becoming increasingly aware of the impact that our lifestyle choices make on energy usage and the environment. As a result, research attention is being directed toward green technology, environmentally-friendly building designs, and smart grids. This paper looks at the user side of sustainability. In particular, it looks at energy consumption in everyday home environments to examine the relationship between behavioral patterns and energy consumption. It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data. Next, it describes a method to correlate home-based activities with electricity usage. Finally, it describes how this information could inform users about their personal energy consumption and to support activities in a more energy-efficient manner. These approaches are validated by using real energy data collected in a set of smart home testbeds.     

Computational analysis of wind interactions for comparing different buildings sites in terms of natural ventilation

Nowadays building designers have to face up to new strategies to achieve the best sustainable building designs. Well planned natural ventilation strategies in building design may contribute to a significant reduction on building’s energy consumption. Natural ventilation strategies are conditioned to the particular location of each building. To improve natural ventilation performance of a building, the analysis of the influence of the location and the surrounding buildings on wind flow paths around the design building is a must. New computational tools such as Computational Fluid Dynamics (CFD) are particularly suited for modelling outdoor wind conditions and the influence on indoor air conditions prior to building construction. Hence, reliable methodologies are necessary to support building design decisions related to naturally ventilated buildings prior to construction.This paper presents a case study for the selection of the best future building location attending to natural ventilation behaviour inside the building, conditioned by different evolving environment. A validated CFD model is used to represent outdoor and indoor spaces. The methodology explains how to qualitatively and quantitatively analyze wind paths around and through a building to quantify the natural ventilation performance. The best location, from two real possible solutions, is then selected.     

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.     

Research opportunities arising from control and optimization of smart buildings

Control and optimization plays an important role in automation science and engineering for buildings. The area is important due to the growing threat of global warming effects to the sustainable development of human society as a whole indicated by the Paris Climate Treaty and the fact the buildings account for about 40% of total energy consumption in US, for example [1]. Effective control and maintenance of Heating, ventilation and air conditioning (HVAC), elevators, sensing devices, and other assets in building systems is extremely challenging since they are running under uncertain environments (such as continuously changing weather, human behaviors, and random contingencies). Uncertainty increases the dimensions of control strategies or maintenance polices since time invariant ones cannot adapt a dynamic environment. Uncertainty also increases the time to evaluate performances and the feasibility of control strategies or maintenance polices....     

面向云计算环境的能耗测量和管理方法

云计算引领了计算机科学的一场重大变革,但与此同时,也不可避免地带来了日益凸显的能源消耗问题,因此,云计算能耗管理成为近几年的研究热点.云计算系统的能耗测量和管理直接关系到云计算的可持续发展,能耗数据不仅关系到能耗模型的建立,而且也是检验云计算资源调度算法的基础.为此,在广泛研究现有能耗测量方法的基础上,归纳总结了当前云计算环境的4种能耗测量方法:基于软件或硬件的直接测量方法、基于能耗模型的估算方法、基于虚拟化技术的能耗测量方法、基于仿真的能耗评估方法,并分析和比较了它们的优势、缺陷和适用环境.在此基础上,指出了云计算能耗管理的未来重要研究趋势:智能主机电源模块、面向不同类型应用的能耗模型、混合任务负载的能耗模型、可动态管理的高效云仿真工具、动态异构分布式集群的能耗管理、面向大数据分析处理和任务调度的节能方法以及新能源供电环境下的节能规划,为云计算节能领域的研究指明了方向.     

Leveraging operational technology and the Internet of things to attack smart buildings

In recent years, the buildings where we spend most part of our life are rapidly evolving. They are becoming fully automated environments where energy consumption, access control, heating and many other subsystems are all integrated within a single system commonly referred to as smart building (SB). To support the growing complexity of building operations, building automation systems (BAS) powering SBs are integrating consumer range Internet of things (IoT) devices such as IP cameras alongside with operational technology (OT) controllers and actuators. However, these changes pose important cybersecurity concerns since the attack surface is larger, attack vectors are increasing and attacks can potentially harm building occupants. In this paper, we analyze the threat landscape of BASs by focusing on subsystems which are strongly affected by the advent of IoT devices such as video surveillance systems and smart lightining. We demonstrate how BAS operation can be disrupted by simple attacks to widely used network protocols. Furthermore, using both known and 0-day vulnerabilities reported in the paper and previously disclosed, we present the first (at our knowledge) BAS-specific malware which is able to persist within the BAS network by leveraging both OT and IoT devices connected to the BAS. Our research highlights how BAS networks can be considered as critical as industrial control systems and security concerns in BASs deserve more attention from both industrial and scientific communities. Even within a simulated environment, our proof-of-concept attacks were carried out with relative ease and a limited amount of budget and resources. Therefore, we believe that well-funded attack groups will increasingly shift their focus towards BASs with the potential of impacting the live of thousands of people.

     

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.     

Parallel Building: A Complex System Approach for Smart Building Energy Management

These days’ smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5G, the ACP theory (i.e., artificial systems, computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.