【作者】 赵娜；

【导师】 贾磊；

【作者基本信息】 山东大学 ， 检测技术与自动化装置， 2008， 硕士

【摘要】 随着全球能源日益紧张以及能源价格的不断上涨,人们对于节能降耗的实施力度给予了高度的重视。作为节能降耗的主要目标,建筑节能成为了当今普遍关注的热点问题。如何提高智能建筑中的能源利用效率,合理有效地利用能源,推动建筑节能显得十分重要。目前,在智能建筑中采取的比较广泛的节能手段就是利用楼宇自控系统(BAS)来控制中央空调系统等,虽然在整个智能建筑的控制管理、节能降耗方面取得了一定的成就,但由于BAS是分设备来进行状态的调节,往往各设备不是处于最佳工况,而且可能还会出现牺牲某些设备的运行状态来获取别的参数改善的情况,这样就缺乏一种立足于全局的管理控制措施。从系统的角度来看,节能降耗方面的效果不是很理想。为此,本文在分析原有智能建筑控制方面不足的基础上,借鉴楼宇自控系统的成功经验,从全局提高节能措施的可操作性入手,针对智能建筑中的主要能耗设备中央空调系统和照明系统的节能降耗问题,从理论和实践两个方面,开发了一种以能量传输效率与能量利用效率为基础的能效分析系统。本文主要研究了数据仓库技术,并将其应用在所开发的能效分析系统的数据处理模块中。对数据挖掘技术做了初步的研究,并对于将数据挖掘技术应用于系统中的可能性进行了探索,本文所作主要工作如下:1、针对现有智能建筑介绍了一种基于能量传输效率和能量利用效率的能效理论。新能效理论指出:建筑物能量评价体系不仅包括传统的能量传输效率,还包括能量利用效率。能量利用效率与建筑物中HVAC系统、照明系统的管理和运行模式有关。阐述了能量传输效率的计算方法,介绍能量利用效率的概念及其检测方法,在能效理论的基础上提出能效分析系统的整体设计方案。2、针对寿光商务小区能效分析系统做了详细分析,开发出较为通用的能效分析软件。从系统的体系结构和功能方面,较为详细地分析并解决了设计和软件实现过程中一些关键性技术问题。3、在能效分析系统中央数据服务器中引入数据仓库技术,利用数据仓库技术进行数据的清洗、数据格式的转换、主题数据仓库的建立等,建立了多维数据集和转换后的关系数据库,为后续数据挖掘做好准备。4、以具体挖掘工具SQL Server为例,简单介绍了数据挖掘的操作,同时对数据挖掘算法做了初步了解,为后续深入研究挖掘算法,并从中得出决策分析以支持系统进行更好的能量调度与管理做准备。最后,在总结以前工作的基础上,对于所开发的能效分析系统存在的缺点和今后工作中需要进一步探索和研究的问题进行了分析。更多还原

【Abstract】 Facing the increasingly tense of the global energy and the rising energy prices, people attaches great importance to the intensity of the implementation of saving energy. AS the main target of saving energy, building energy conservation becomes a current hot issue of common concern. How to improve the Intelligent Building energy efficiency, rationally and effectively use the energy, it is important to promote energy-saving construction. For example, building automation systems (BAS) are widely employed in public intelligent buildings to monitor and control the operation of HVAC, which make progress in control management and energy savings. But the BAS monitor the system according the equipments, which can lead the equipments not all operation in the best condition. But there may be some equipment expense of the operational status of other parameters to obtain the improvement of the situation, which lack of a foothold in the overall management and control measures. From the system’s point of view, the energy saving effect is not very good.Based on the efficiency of energy transmission and energy usage, a new method of measurement and evaluation of building energy efficiency is described in this thesis. A lot of building energy management strategies on HVAC and lighting system is investigated in order to reduce the energy consumption in intelligent buildings. The probability and the principle of building energy efficiency management systems are systematically introduced based on the energy efficiency theory.This thesis studies the data warehouse technology and its application in the development of the energy management system of data processing module. A preliminary study is done on the Data Mining techniques and explore the possible application of data mining technology in the system.The thesis’s main tasks are as follows:In section I, a new concept of energy efficiency is introduced in detail by analyzing the existent energy management systems. The evaluation of energy efficiency is composed of energy transmission efficiency and energy usage efficiency. Building energy usage efficiency is connected with modes of operation and management. On the energy transfer efficiency calculation method, introduced the concept of energy efficiency. The concept of energy efficiency and its detection method is introduced. Proposed the design overall energy management system in the theory of energy efficiency.In section II, done a detailed analysis of Shouguang business district energy management systems, and developed the common energy analysis software. From the system architecture and functionality, more detailed analysis and solution design is done, and a number of key technical issues are solved in the software implementation process.In section III, developed the data warehouse technology in the central data server of the energy analysis system. Using the data warehouse technology for data cleansing, data format conversion, the establishment of data warehouse theme, etc. The establishment of multi-dimensional data sets and the converted relational database are prepared for the follown data mining.In section IV, take the mining tools specific to SQL Server as an example, and briefly introduce the operation of data mining. Do a preliminary understanding of the data mining algorithms. Prepare for an in-depth follow-up study on mining algorithms, and attach the system for better control and management measure.Finally, in conclusion on the basis of previous work, the development of the energy management system and the future of the shortcomings of the work of the need for further exploration and study of the issue.更多还原