Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

The Value Insistence of the Cultural Production in the Big Data Era

The ultimate value of cultural production should be realizing human's comprehensive and free development,and deconstructing the ultimate value would result in human alienation.In the era of big data,every domain of human's social life,even the mode of thinking,has been transformed significantly.However,when the big data technology entirely penetrates the field of cultural production especially inducts the cultural production depending on the demand forecasting techniques,it would inevitably lead to a worry about value of the cultural production.This paper formulates that the cultural production's essence in the era of big data remains for the purpose of maximizing profit of commercial manipulation based on the modeling analysis of cultural production mechanism in the big data times.If the tendency is not corrected,the two main factors of cultural consumerism prevalence and the instrumental reason dictatorship will gradually deconstruct the ultimate value of cultural production and bring about the alienation of human being.For the sake of avoiding the trend,we should cope with two relationships:one is the people as a means and as a purpose;the other is the instrumental reason and the value rationality,finally giving rise to human's comprehensive and free development rather than human alienation.

Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well. Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. Recently, Big data is one of the most important topics in IT industry. Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data. New types of data have different and new challenges also. A lot of researches treat with big data challenges starting from Doug Laney’s landmark paper, during the previous two decades;the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact. The present paper highlights important concepts of Fifty-six Big Data V’s characteristics. This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.

中美人工智能技术创新的动态比较——基于人工智能技术创新大数据的多S曲线模型分析

基于基础研究、技术创新、科技布局、产业规模与技术进步5个维度人工智能技术创新大数据,创新性地构建了人工智能技术创新发展水平多指标测度体系及技术创新综合发展指数;根据技术创新综合发展指数,拟合绘制出人工智能技术创新周期S演化曲线,较为准确定位了中美人工智能技术创新发展所处位置;由5个维度在技术不同发展阶段的权重分布动态,比较出中美新技术之间的创新发展差距,分析影响中国人工智能新技术创新发展的主要因素。研究发现,美国日本等世界主要发达国家的人工智能技术已步入创新成熟期,中国正处于创新成长期后期,技术创新十分活跃;借助于后发优势,中国人工智能技术创新逐年综合发展指数已超过美国,展现出非常强劲的增长态势。为赢得未来人工智能技术创新竞争的胜利,中国应在人工智能技术创新体制和技术创新资源配置机制等方面不断完善,确保中国人工智能技术创新中长期的领先地位。

基于改进K-均值联合SVDD的电力数据异常检测方法

电力数据高维、非线性特点导致传统单一异常检测模型存在检测准确率低、误漏报率高的问题,为了解决此问题,提出一种最小熵K-均值和果蝇算法(fruit fly optimization algorithm,FOA)优化支持向量数据描述(support vector data description,SVDD)相结合的电力数据异常检测方法。首先提取波动性特征、变动性特征和趋势性特征并利用主成分分析(principal component analysis,PCA)进行特征变换从而实现对高维电力数据的降维表征,其次利用最小熵理论改进的K-均值算法对用电数据进行自适应聚类,将用电模式接近的用户划分为同一聚类,采样SVDD对每个聚类进行异常检测,为了提升SVDD模型聚类性能,利用改进后的变步长FOA算法对其优化,提升异常检测性能。基于某电网实测用电数据开展试验,结果表明所提方法相对于单一检测模型能够获得更高的检测准确率和更低的误漏报率。

Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

Smart cities make use of a variety of smart technology to improve societies in better ways.Such intelligent technologies,on the other hand,pose sig-nificant concerns in terms of power usage and emission of carbons.The suggested study is focused on technological networks for big data-driven systems.With the support of software-defined technologies,a transportation-aided multicast routing system is suggested.By using public transportation as another communication platform in a smart city,network communication is enhanced.The primary objec-tive is to use as little energy as possible while delivering as much data as possible.The Attribute Decision Making with Capacitated Vehicle(CV)Routing Problem(RP)and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research.For the optimum network selection,a Multi-Attribute Decision Making(MADM)method is utilized.For the sake of reducing energy usage,the Capacitated Vehicle Routing Problem(CVRP)is employed.To reduce the transportation cost and risk,Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used.Moreover,a mixed-integer programming approach is used to deal with the problem.To produce Pareto optimal solutions,an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is cre-ated.A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer.Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30%of less energy than the other systems.

基于SAIC方法的纵向数据模型平均

传统的SAIC模型平均所需运行时间随数据维数而呈现出阶乘级的增长,其预测精度也随之下降.本文基于传统SAIC模型平均法进行了改进,提出一类基于SAIC加权法的纵向数据模型平均法,使运算效率大幅提升,并且使预测效果拥有良好的稳定性.模拟实验结果表明,与传统方法相比,在预测残差平方和层面,本文提出的新模型在稳定性、精准性和运行速度方面均优于传统方法.

基于B/S架构的用户用能信息采集系统研究

为了提高用户用能信息采集能力,提出基于B/S架构的用户用能信息采集系统设计方法。通过物联网和RFID标签识别技术,构建用户用能信息采集系统的射频标签信息采集模块,采用B/S架构和总线传输协议实现用户用能信息采集的信息化传输和调度,实现用户用能信息采集。测试结果表明方法进行用户用能信息采集的智能性和准确性较好。

人口老龄化背景下体育消费的健康效应——基于全国消费大数据的实证证据

基于健康资本需求理论将体育消费视作一种健康投资,提出个体体育消费的增长可降低其医疗消费支出的假设;采用2018—2021年全国消费大数据,实证检验个体体育消费的健康效应。结果显示,体育消费占个体年度总消费的比例每提高1个单位,医疗消费占个体年度总消费的比例降低0.203个单位。新冠病毒感染疫情对体育消费的健康效应有所抑制但依然显著。进一步分析表明,体育消费的健康效应对35岁及以上人群的影响会随着年龄的增长明显增强,对65岁及以上老龄人口的影响效果最为突出。认为:在应对人口老龄化和医疗负担增加等重大挑战时,需要加快普及对体育消费的健康效应认知,大力促进全人群体育消费。

基于Spark Streaming的实时能耗分项计量系统

能耗分项计量能够准确、及时、有效地发现能源使用问题,形成和实现最有效的节能措施。能耗分项计量系统需要对各项能源使用量在不同粒度上进行统计,既有实时性的需求,又需要涉及到聚合、去重、连接等较为复杂的统计需求。由于数据产生快、实时性强、数据量大,所以很难统一采集并入库存储后再作处理,这便导致传统的数据处理架构不能满足需求。为此,提出基于Spark Streaming大数据流式技术构建一个实时能耗分项计量系统,对实时能耗分项计量的系统架构和内部结构进行了详细介绍,并通过实验数据分析了系统的实时数据处理能力。与传统架构不同,实时能耗分项计量系统在数据流动的过程中实时地进行捕捉和处理,一方面把捕捉到的异常信息及时报警到前端,同时把分类分项统计处理的结果保存到数据库,以便进行离线分析和数据挖掘,能有效地解决上述数据处理过程中遇到的问题。

大数据背景下社交平台伪健康信息传播影响因素研究

探究大数据背景下社交平台伪健康信息传播影响因素,为有关部门更好地制定伪健康信息管理策略提供参考与借鉴。整合S-O-R模型和MOA理论,引入个性化推荐这一新变量,构建社交平台伪健康信息传播影响因素模型,利用问卷调查和结构方程模型验证模型和假设的合理性。研究表明信息交换、健康关注度、信息质量、信息新颖和个性化推荐正向影响用户感知,进而正向影响用户伪健康信息传播行为,而健康信息素养负向影响用户伪健康信息传播行为,并据此提出管理社交平台伪健康信息传播的可行性建议。

基于Spark的水泥厂煤耗特性分析与实时诊断

针对国内水泥厂煤耗较高、数据利用率低的问题,对大数据、数据挖掘技术应用于水泥厂煤耗管控进行了研究。搭建大数据计算平台,使用Spark计算引擎采集并清洗水泥厂生产设备的过往稳态数据,建立了煤耗特性的数学模型;研究实时诊断算法并运行于大数据计算平台上;对实时采集生产设备的运行参数进行计算,得出各运行参数当前的数值,并进行数据可视化,指导水泥厂改良将会造成能耗偏差的异常参数,以降低煤耗、改善生产。

基于大数据分析技术的家电企业物流运营策略——以S家电企业为例

以S家电企业为例,分析了其目前物流信息消费的现状,提出从物流信息数据采集策略、企业数据仓库基础设施构建、基于大数据分析的智能物流平台构架等方面入手,充分挖掘物流信息的价值,并对大数据下物流信息消费为S公司带来的效益进行分析,对未来家电大数据物流信息消费前景进行了展望。

基于K-means聚类分析5G时代下的数字营销策略研究——以杭州旅游营销为例

为贯彻落实《关于加快推进5G产业发展的实施意见》,推动5G场景应用,推进5G网络与超高清视频、AR/VR、机器人等技术结合,提升消费者在文化娱乐、在线购物、智能出行等场景的体验等,本文根据数字时代游客行为的3个子层面,对来杭游客的行为和偏好进行K-means聚类分析,将赴杭游客的偏好聚类为个性化推送敏感型、社交媒体和自媒体主动获取信息型、科技交互体验型、依赖出行路径定制+定位推送项目型四类,并以此构建为前端-消费数据挖掘与云计算、中端——数字媒介营销整合、后端——升级云端大数据服务信息平台组成的5G旅游数字营销体系,同时基于杭州旅游休闲产业、产品的特点策划详细方案,为各地的旅游管理、宣传部门和旅游商家提供适应5G时代数字营销的参考策略。

基于大数据分析的智能用电非侵入式负荷分解模型

为准确从电网电流数据中分解出单独的负荷叠加信号,基于大数据分析构建一种新的智能用电非侵入式负荷分解模型。应用大数据分析技术建立时间序列数据集,确定时间区域断点均值,并在大数据内部提取非侵入式负荷信号,通过迭代计算提取智能用电设备运行状态。根据线性解码计算结果构建负荷分解数学模型,分析负荷电流信号独立性,检测电流信号和负荷电流信号的近似系数,实现分解操作。实验结果表明,所构建模型分解后的电流与负荷的电流信号之间的相关系数达到0.9999,加快了收敛速度,保证分解效果。

Total Electricity Consumption Forecasting Based on Temperature Composite Index and Mixed-Frequency Models

The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.

基于大数据分析下最低能耗设计的新能源环卫车技术应用

随着国家颁布的各项推动新能源汽车发展政策的落地,以及近几年新能源汽车技术突飞猛进的发展,从政策和技术层面进行双向驱动,使得环卫车逐步由传统燃油车向新能源车转变。作为商用车客户最关心的经济效益,如何尽可能降低整车能耗成为各家环卫企业高度重视的问题。以某18t环卫车底盘开发为例,基于后台数以百辆的环卫车运行数据,分析车辆行驶速度占比、能耗分布占比等,从车辆硬件、软件两方面进行优化,以便尽可能地降低车辆的运行能耗,从而为客户创造更大的经济效益。

Planning of electric vehicle charging infrastructure under the Background of Big Data

Today, the problems of power consumption and environmental pollution caused by road trafficare very serious. Automobiles produce large amounts of carbon monoxide and nitrogen, dust,and particles, which are the primary user of the fuel. Unconventional design methods will preventthe international process of replacing fuel-filled vehicles with electric vehicles. The promotionand adoption of electric vehicles can effectively alleviate these problems, but the planning ofelectric vehicle charging facilities needs to be resolved as soon as possible. The state-of-the-artmodeand power model of public charging equipment proposed in this paper can solve the problemof positioning charging equipment in large-scale transmission networks and research onrelated charging materials and location planning and energy model design methods that arebeneficial to the future development.

storm平台下工作节点的内存电压调控节能策略

针对传统大数据流式计算平台节能策略并未考虑数据处理及传输的实时性问题,首先根据数据流处理的特点与storm集群的结构,建立有向无环图、实例并行度、任务资源分配与关键路径模型。其次结合拓扑执行关键路径与系统性能的分析,提出一种storm平台下工作节点的内存电压调控节能策略(WNDVR-storm,energy-efficient strategy for work node by dram voltage regulation in storm),该策略针对是否有工作节点位于拓扑执行的非关键路径上设计了2种节能算法。最后根据系统数据处理及传输的制约条件确定工作节点CPU使用率与数据传输量的阈值,并对选定的工作节点内存电压做出动态调整。实验结果表明,该策略能有效降低能耗,且制约条件越小节能效率越高。