面向自动需求响应的智能家居大数据处理技术研究
发布时间：2019-02-24 11:42
【摘要】：目前电网负荷峰谷差越来越大,如何削峰平谷提高用电效率,对智能电网提出新挑战。需求响应通过引导用户合理用电,来缓解高峰时的电网压力,实现削峰平谷并提高电力资源利用率。在用户侧响应终端中,智能家居作为大功率负荷,对电呈柔性需求,其用电功率可以动态调整,合理调节其用电功率可以缓解电网压力。但智能家居种类多、数量大、且分散,难以管理,需要构建一个平台将智能家居聚合起来,实现对智能家居的统一控制和管理。将不同厂商的不同设备聚合起来会面临诸多问题,如通讯协议的不一致、高并发系统设计、TB级数据存储和处理等。本论文以智能家居为研究对象,根据自动需求响应系统的业务需要,设计了需求响应能力、聚合商认购能力、节电效果评估和用户行为分析等模型,以及这些模型的分布式解决方案。这些模型需要分析和挖掘TB级的历史数据,采用传统方式无法满足业务对空间和时间上的要求,因此本论文提出并实现智能家居大数据平台。该平台为自动需求响应系统提供大数据技术支持,由需求响应终端系统、业务数据处理系统和可视化系统三个子系统组成。需求响应终端系统采用MINA开发,支持高并发网络通讯,并将采集到的数据交给Kafka缓存起来,解决数据接收和处理速度的不一致。业务数据处理系统基于Lambda架构理念实现,整合了Hadoop和Spark;利用Spark从Kafka获取实时数据流,做实时预测分析;使用MapReduce实现并行化KNN算法,根据用户的用电记录,对用户分类;使用MapReduce实现认购能力模型和节电效果评估模型的分布式算法。可视化系统调用数据处理后的结果,为数据的查询和展示提供可视化界面。
[Abstract]:At present, the load peak and valley difference of power network is more and more big. How to cut peak and level valley to improve power efficiency is a new challenge to smart grid. The demand response can relieve the pressure of the power grid at the peak by guiding the user to use electricity reasonably, realize the peak cutting and level valley, and improve the utilization ratio of power resources. In the user-side response terminal, smart home is regarded as a high-power load, and it has flexible demand for electricity, its power can be dynamically adjusted, and its power can be adjusted reasonably to relieve the pressure of power grid. However, there are many kinds of smart home, large quantity, scattered and difficult to manage, so it is necessary to build a platform to aggregate smart home to realize the unified control and management of smart home. The aggregation of different devices from different manufacturers will face many problems, such as inconsistent communication protocols, high concurrent system design, TB level data storage and processing, and so on. In this paper, the smart home as the research object, according to the business needs of the automatic demand response system, design the demand response ability, aggregator subscription ability, power saving effect evaluation and user behavior analysis model. And distributed solutions for these models. These models need to analyze and mine the historical data of TB level, and the traditional way can not meet the requirements of space and time. Therefore, this paper proposes and implements big data platform of smart home. The platform provides big data technical support for the automatic demand response system, which consists of three subsystems: the demand response terminal system, the business data processing system and the visual system. The demand response terminal system is developed with MINA to support high concurrency network communication, and the collected data is cached by Kafka to solve the inconsistency between data receiving and processing speed. The business data processing system is realized based on Lambda architecture, which integrates Hadoop and Spark; to obtain real-time data stream from Kafka and use Spark to do real-time prediction and analysis, uses MapReduce to realize parallel KNN algorithm, classifies users according to user's power consumption record. MapReduce is used to realize the distributed algorithm of subscription ability model and power saving effect evaluation model. The visualization system calls the results of data processing and provides a visual interface for the query and display of data.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;TU855
[Abstract]:At present, the load peak and valley difference of power network is more and more big. How to cut peak and level valley to improve power efficiency is a new challenge to smart grid. The demand response can relieve the pressure of the power grid at the peak by guiding the user to use electricity reasonably, realize the peak cutting and level valley, and improve the utilization ratio of power resources. In the user-side response terminal, smart home is regarded as a high-power load, and it has flexible demand for electricity, its power can be dynamically adjusted, and its power can be adjusted reasonably to relieve the pressure of power grid. However, there are many kinds of smart home, large quantity, scattered and difficult to manage, so it is necessary to build a platform to aggregate smart home to realize the unified control and management of smart home. The aggregation of different devices from different manufacturers will face many problems, such as inconsistent communication protocols, high concurrent system design, TB level data storage and processing, and so on. In this paper, the smart home as the research object, according to the business needs of the automatic demand response system, design the demand response ability, aggregator subscription ability, power saving effect evaluation and user behavior analysis model. And distributed solutions for these models. These models need to analyze and mine the historical data of TB level, and the traditional way can not meet the requirements of space and time. Therefore, this paper proposes and implements big data platform of smart home. The platform provides big data technical support for the automatic demand response system, which consists of three subsystems: the demand response terminal system, the business data processing system and the visual system. The demand response terminal system is developed with MINA to support high concurrency network communication, and the collected data is cached by Kafka to solve the inconsistency between data receiving and processing speed. The business data processing system is realized based on Lambda architecture, which integrates Hadoop and Spark; to obtain real-time data stream from Kafka and use Spark to do real-time prediction and analysis, uses MapReduce to realize parallel KNN algorithm, classifies users according to user's power consumption record. MapReduce is used to realize the distributed algorithm of subscription ability model and power saving effect evaluation model. The visualization system calls the results of data processing and provides a visual interface for the query and display of data.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;TU855
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