Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.

An Energy-Saving Control Strategy on Central Air-Conditioning System

An energy-saving control strategy based on predictive control for central air-conditioning systems is proposed in this paper. The cold load model is developed to describe the dynamic characteristics of temperature control systems, and then parameters in the cold load model and in the central air-conditioning system model are estimated. Generalized predictive control (GPC) is used to establish an optimization model to minimize the consumption of energy and the control error of temperature. The simulated annealing (SA) algorithm, combined with quadratic programming, is adopted to solve the optimal problem. Contrasted with the simulation of traditional PID control, the results prove the effectiveness of this proposed strategy.

Optimum Energy Management of the Central Air-Conditioning System in Intelligent Buildings

An optimum energy saving scheduling strategy of the central air conditioning system in an intelligent building (IB) was proposed. Based on the system analysis a set of models of the central air conditioning system was established. The periodically autoregressive models (PARM) based on genetic algorithms (GA) were used to predict the next day’s cold load. The improved genetic algorithms (IGA) with stochastic real number coding were used to finish the optimum energy saving scheduling of the system. The simulation results for the building of the Liangmahe Plaza show that the proposed strategy can save energy up to about 24 5%.

Energy-Efficient Joint Content Caching and Small Base Station Activation Mechanism Design in Heterogeneous Cellular Networks

Heterogeneous cellular networks(HCNs), by introducing caching capability, has been considered as a promising technique in 5 G era, which can bring contents closer to users to reduce the transmission delay, save scarce bandwidth resource. Although many works have been done for caching in HCNs, from an energy perspective, there still exists much space to develop a more energy-efficient system when considering the fact that the majority of base stations are under-utilized in the most of the time. Therefore, in this paper, by taking the activation mechanism for the base stations into account, we study a joint caching and activation mechanism design to further improve the energy efficiency, then we formulate the optimization problem as an Integer Linear Programming problem(ILP) to maximize the system energy saving. Due to the enormous computation complexity for finding the optimal solution, we introduced a Quantum-inspired Evolutionary Algorithm(QEA) to iteratively provide the global best solution. Numerical results show that our proposed algorithm presents an excellent performance, which is far better than the strategy of only considering caching without deactivation mechanism in the actual, normal situation. We also provide performance comparison amongour QEA, random sleeping algorithm and greedy algorithm, numerical results illustrate our introduced QEA performs best in accuracy and global optimality.

Analysis and Modeling of the Central Air-Conditioning System in Intelligent Buildings

The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.

Effectiveness of PMV-Based Controls for Commercial Buildings in the USA

Using an accurate energy model,this paper simulated existing commercial buildings to compare PMV-based comfort control with conventional thermostatic control and conduct a sensitivity analysis using the Design of Experiments method with monitored energy consumption data.This analysis was applied considering the impact of building envelope characteristics including thermal insulation level of exterior walls and air leakage rate on the ability of both control options to maintain indoor thermal comfort while minimizing cooling energy consumption.The analysis findings indicated that PMV-based comfort control is viable for striking a middle ground between thermal comfort and heating energy consumption with a PPD(predicted percentage of dissatisfied)level at 8%for any combination of walls’R-value and air infiltration rate while temperature-based controls result in an unacceptable indoor thermal comfort performance especially for low R-values regardless of the air infiltration rate with PPD level reaching over 76%.Also,the results show that in a comfort-controlled space,the average radioactive temperature and occupant-related features like metabolic rate and clothing level have a far bigger effect on energy use than other parameters like relative humidity.

Application of Dual Throttling Air-Conditioning System to Explosion-Proof Frequency Converter

An explosion-proof dual throttling air-conditioning system was put forward to solve the heat dissipation and internal dewing problems of explosion-proof frequency converter in the underground coal mine. This study investigated the feasibility and benefits of explosion-proof dual throttling cooling and dehumidification air-conditioning system applied to the explosion-proof frequency converter. The physical model of dual throttling air-conditioning system was established and its performance parameter was described by mathematical method. The design calculation of the system has also been done. The experimental result showed that the system reached the steady state at the refrigeration mode after running 45 min, and the maximum internal temperature of the flame-proof cavity was 31.0 ℃. The system reached the steady state at the dehumidification mode after running 37 min. The maximum internal relative humidity and temperature of the flame-proof cavity were 33.4% and 36.3 ℃, respectively. Therefore, the proposed system had excellent ability of heat dissipation and avoided internal dewing. Compared with water cooling system, it was more energy-saving and economical. The airflow field of dual throttling air-conditioning system was also studied by CFD simulation. It was found that the result of CFD numerical simulation was highly consistent with the experimental data.

地铁车站月平均气温随运营年限演化特性

建立了地铁车站空气热平衡理论模型,基于区间隧道空气温度和车站围岩土体蓄放热量,得到车站月平均气温在运营初期、中期和远期的逐年演化特性。研究结果表明:随着地铁车站运营年限的增长,月平均气温呈现逐年上升趋势,运营第15年较第1年,车站月平均气温在1月温升最低为3.1℃,8月温升最高为10.6℃;在现有地铁行车密度随运营年限增大的运行模式下,地铁车站运营初期行车密度仅为15对/h,夏季车站最高气温出现在第5年,为26.4℃,远低于夏季站厅站台空调设计温度平均值29℃,所以运营初期车站空调系统提供的冷量远大于车站的冷负荷需求,初期车站空调系统存在一定的节能空间。

基站空调在640层CT数据机房的使用效果

目的探讨基站空调在640层CT数据机房的使用效果。方法640层CT正常开机时,分别记录并比较基站空调和普通空调开启后数据机房温度由28℃降至24℃所需时间、正常运行必需后勤维护项目及次数、整体价格。结果室外温度为-5~30℃时,两种空调达到设定温度所需时间比较,差异无统计学意义(P>0.05),后勤维护项目次数相同;室外环境温度为31~42℃时,基站空调机房降温时间短于普通空调(P<0.05);普通空调需完成后勤维护项目一2次和项目二1次,基站空调需完成项目一2次;在室外环境温度为-6~-15℃时,普通空调故障停机,基站空调达到设定温度需要(12.4±1.5)min;基站空调整体价格高于普通空调;基站空调的整机性价比、操作使用、降温效率、故障报警和环境影响评分高于普通空调(P<0.05)。结论基站空调虽整体价格偏贵,但制冷速度快、可靠性高、维护简单,更适用于640层CT数据机房。

地铁站空调水系统节能控制研究

为了降低空调水系统的能耗,研究广州某地铁站的空调水系统节能控制策略。使用设备厂商提供的样本数据进行参数拟合,建立较为准确的设备模型,利用Trnsys软件搭建地铁站空调水系统模型。针对“大流量、小温差”现象,提出定温差控制策略,以降低水系统的能源消耗。结果显示:与定流量水系统相比,定温差控制策略下地铁站空调水系统节能率达到25.86%;在整个供冷季,相对于常规7℃的供水温度定温差控制策略,变冷冻水供水温度定温差控制策略的空调水系统节能约3.40%。

电动车废热辅助空调系统的研究

针对目前电动车空调存在的节能性和低温适应性等问题,提出了一种电动车废热辅助热泵空调系统。该系统将多变频器协调控制技术、双级压缩循环技术、废热辅助热泵技术有机融合,通过仿真分析研究系统在不同运行状态下的系统性能。仿真结果表明:协调调节低压级压缩机和风扇电机的频率,可实现系统制冷/制热量和能效比的优化控制;合理设置单双级压缩的温度切换点,能提升制热性能、保证系统在低温工况下的可靠运行;利用废热辅助空气源热泵技术,能实现系统的节能。研究结果为减小空调系统对电动车续航里程的影响、推动电动车的普及提供了参考。

某调度通信楼空调系统节能减碳改造设计实践

重点介绍了某调度通信楼空调系统的节能减碳改造设计,通过综合运用余热回收、可再生能源、固体蓄热、高效制冷机房等多项技术,构建了安全可靠、绿色低碳、高效节能的空调系统。采用HDY-SMAD空调负荷计算及分析软件模拟了建筑物全年的空调能耗,并对优化前后空调系统的运行能耗和碳排放进行了测算。结果显示,优化方案空调系统全年节约电量100万kW·h,全年碳减排573 t,节能减碳效果显著。

北京某大型商场空调系统节能改造方案分析

大型商场人流量大、散热设备多、空调系统运行时间长,有较大节能潜力。调研了北京某大型商场空调系统的能耗情况,针对其运行过程存在的问题,提出了一套基于空调系统节能控制技术的综合节能改造方案。研究表明,该空调系统节能改造后,年节电量为140.56万k Wh,每年节省电费160.24万元;节热量为601.82GJ,每年节省供热费用5.95万元,节能效果与经济效益较好,可为北京地区类似公建节能改造提供借鉴和参考。

基于航班信息确定旅客数模型联动空调节能技术研究

针对机场航站楼内空调系统能耗高且节能潜力巨大的特点,提出一种基于航班信息动态预测分时段分区旅客数,进而联动优化空调节能控制的新方法。新方法研究重点在于航站楼空调调控分区的划分、航班信息有效数据的提取、分时段分区旅客数模型的构建、节能档位控制的实现策略与步骤。结合华南地区某支线机场航站楼冷源及空调末端控制的实际测试数据进行模拟分析,结果表明,在保障旅客舒适度的基础上,通过分区旅客数精准调控空调系统运行模式,实现了能耗较大幅度的降低,节能成效显著,为航站楼空调节能技术提供了新的思路,为同类型其他机场提供了参考。

中央空调系统制冷机组节能优化

空调系统是公共建筑中重要的基础配套设施,其能源消耗占据较大比重,在节能降耗方面存在巨大潜力。为实现在满足冷负荷需求的条件下达到中央空调系统总运行能耗最小的目标,建立了中央空调系统制冷机组能耗模型,并将室外湿球温度和实时的冷负荷值作为空调系统能耗模型的输入条件,分别利用粒子群算法和改进粒子群算法获得在不同负荷需求时空调系统对应的最佳工作点,实现中央空调系统运行优化节能的效果。

纺织空调绿色生产技术途径

探讨纺织空调实现绿色生产的途径和方法。针对纺织空调运行能耗高、耗水量大的问题,以创建绿色工厂为目标,从提高设备能效、优化空调热湿处理方法、加强运行调试与能源管理等方面提出具体要求和措施,并提出了细纱车间热分流、热回收、送回风系统低能耗运行的方法。认为:做好纺织空调节能降耗工作有助于实现纺织车间绿色生产。

基于AI大数据的无线基站节能系统的设计与应用

为应对2G/3G/4G无线基站在运营商日常运营中高能耗开销及不断上升的电费支出问题,文章提出了一种基于AI大数据技术的无线RAN基站节能系统。该系统旨在针对全网各种场景和不同设备,通过对现有网络配置的深入梳理与调整优化,以及节能功能的智能部署和优化,并对现网站点的建模寻找最优门限值,最后通过AI大数据的持续优化迭代,持续提升节能效益,增强系统的稳定性。在满足用户业务需求的前提下,不断优化无线基站的节能功能,从而显著降低设备功耗,大幅度降低运营商的节能支出。通过实验结果表明,该系统部署到运营商网络中的节能效果十分明显,相比部署该系统之前功耗降幅接近12%,远远高于传统节能手段的增益效果。

溶液调湿空调在医药厂房工程应用的碳减排潜力分析

众多医药生产车间要求低湿环境,需要送风含湿在7 g/kg甚至5 g/kg以下,常规7℃冷冻水驱动的冷冻除湿方式难以完成上述除湿任务,因而医药厂房通常采用转轮除湿系统对空气进行湿处理以满足环境热湿控制需求。然而,转轮系统所需再生温度较高,通常使用高温蒸汽或电加热器作为再生热源,限制了系统的低碳化运行。溶液空调系统具有利用低品位冷热源的优势,但现有研究对于溶液空调技术在医药建筑中应用可行性研究尚不充分,无法揭示其在医药建筑应用的节能减排潜力。因此,现场测试了溶液空调系统在某试点医药厂房的实际应用效果,对比了其与常规转轮系统的性能差异并阐明差异原因。示范工程应用结果表明,一次回风系统送风含湿量在6 g/kg左右时,溶液系统可以将再生热源温度从100℃以上降至50℃以下,相较于常规转轮系统的节能比率为15%~45%,典型工况下的碳减排率为20.7%。以期为医药建筑低碳化发展提供经验参考与数据支撑。

数据中心间接蒸发冷却复合空调系统的节能运行分析

结合上海地区的气候条件,采用TRNSYS软件,通过设定数据中心机房条件模拟出全年所需冷负荷随时间的变化规律,确定设备选型,并基于间接蒸发冷却复合空调系统主要设备的数学模型搭建了仿真平台。通过对空调系统运行模式的切换,模拟得到3种运行模式下的送风状态图及运行特性。结果表明,冬季应采用能耗更低的干模式,过渡季采用湿模式,夏季采用混合模式。对比常规机械制冷系统,复合空调系统的全年耗电量为352.6万kW·h,节能率高达34.8%。

图智能AI技术在基站流量预测中的探索与实验

5G网络以其高速率、广泛连接和低时延为新技术领域提供支持,但面临着严峻的能耗问题。研究了AI技术在提升基站能效方面的应用,提出了基于图神经网络的流量预测方法,考虑了流量数据的空间关联和时间依赖性。该方法结合图卷积网络和时序卷积模块,优化了基站流量分布,显著提升了流量预测准确性。准确的流量预测能够为基站关停策略提供科学依据,从而有效降低能耗,提升能源效率,减少成本,促进可持续发展。