A framework for knowledge discovery in massive building automation data and its application in building diagnostics
A framework for knowledge discovery in massive building automation data and its application in building diagnostics
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DOI：
10.1016/j.autcon.2014.12.006
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发表时间：
2015-02-01
影响因子：
10.3
通讯作者：
Yan, Chengchu
中科院分区：
文献类型：
--
作者：
Fan, Cheng;Xiao, Fu;Yan, Chengchu
楼宇自动化系统(BAS)在当今的楼宇运行中扮演着重要的角色。BAS中存储了大量的楼宇运营数据，但由于缺乏强大的数据分析工具，这些数据很少得到有效利用。数据挖掘是发现隐藏在海量数据中的知识的一种很有前途的技术。提出了一种基于数据挖掘技术的海量BAS数据中知识发现的通用框架。该框架是考虑到BAS数据的低质量和复杂性、先进数据挖掘技术的多样性以及数据挖掘技术发现的知识与建筑领域领域知识的集成而专门设计的。该框架主要由数据挖掘、数据分割、知识发现和后挖掘四个阶段组成。应用该框架对香港最高建筑的BAS数据进行了分析。采用方差分析(ANOVA)方法确定对总功耗影响最显著的时间变量。然后利用聚类分析从能耗的角度识别出典型的运行模式。已经确定了八种操作模式，因此整个BAS数据被划分为八个子集。针对BAS数据多为数值型的特点，采用定量关联规则挖掘(QARM)方法进行知识发现。为了提高后挖掘阶段的效率，提出了两个索引来快速方便地识别和利用QARM发现的潜在有趣规则。所发现的知识被成功地用于理解建筑运行行为、识别非典型运行状态和检测故障状态。(C)2014爱思唯尔B.V.保留所有权利。
Building Automation System (BAS) plays an important role in building operation nowadays. A huge amount of building operational data is stored in BAS; however, the data can seldom be effectively utilized due to the lack of powerful tools for analyzing the large data. Data mining (DM) is a promising technology for discovering knowledge hidden in large data. This paper presents a generic framework for knowledge discovery in massive BAS data using DM techniques. The framework is specifically designed considering the low quality and complexity of BAS data, the diversity of advanced DM techniques, as well as the integration of knowledge discovered by DM techniques and domain knowledge in the building field. The framework mainly consists of four phases, i.e., data exploration, data partitioning, knowledge discovery, and post-mining. The framework is applied to analyze the BAS data of the tallest building in Hong Kong. The analysis of variance (ANOVA) method is adopted to identify the most significant time variables to the aggregated power consumption. Then the clustering analysis is used to identify the typical operation patterns in terms of power consumption. Eight operation patterns have been identified and therefore the entire BAS data are partitioned into eight subsets. The quantitative association rule mining (QARM) method is adopted for knowledge discovery in each subset considering most of BAS data are numeric type. To enhance the efficiency of the post-mining phase, two indices are proposed for fast and conveniently identifying and utilizing potentially interesting rules discovered by QARM. The knowledge discovered is successfully used for understanding the building operating behaviors, identifying non-typical operating conditions and detecting faulty conditions. (C) 2014 Elsevier B.V. All rights reserved.