Building system performance diagnosis and optimization based on data mining techniques

XIAO Fu; FAN Cheng; WANG Shengwei

doi:10.3969/j.issn.0438-1157.2014.z2.027

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Building system performance diagnosis and optimization based on data mining techniques

更新时间：2025-12-10

- Building system performance diagnosis and optimization based on data mining techniques
- CIESC Journal Issue z2, Pages: 181-187(2014)
- 作者机构：
  
  香港理工大学屋宇设备工程学系, 香港九龙
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2014.z2.027
  CLC： U12
- Online First：30 December 2014，
  
  Published：30 December 2014
- 稿件说明：
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XIAO Fu, FAN Cheng, WANG Shengwei. Building system performance diagnosis and optimization based on data mining techniques[J]. CIESC Journal, 2014, (z2): 181-187.
DOI：

XIAO Fu, FAN Cheng, WANG Shengwei. Building system performance diagnosis and optimization based on data mining techniques[J]. CIESC Journal, 2014, (z2): 181-187. DOI： 10.3969/j.issn.0438-1157.2014.z2.027.

摘要

现代建筑(特别是公共建筑和商业建筑)中普遍安装了先进的建筑自动化系统(BAS)

用于自动监测和控制各种建筑系统。BAS中存储着庞大的建筑实际运行数据

但是这些数据很少得到充分的利用。本文旨在研究新兴的数据挖掘技术并将其应用于分析处理庞大的BAS数据

从而发现知识

并将知识应用于提高建筑系统性能。通过分析建筑领域内常用的诊断和优化方法

总结建筑性能诊断和优化通常所需的各种类型的知识

进而建立数据挖掘获得的典型知识与建筑专业知识的普适关联。同时

本研究着力于建立一套可靠而灵活的基于数据挖掘技术的建筑运行数据分析和应用框架。该框架可兼容不同的数据挖掘算法

并在不同建筑的BAS中实施。报告了利用该框架分析香港最高建筑环球贸易广场的BAS数据的案例。结果显示数据挖掘技术在识别建筑典型能耗模式、建立鲁棒能耗预测模型

诊断制冷空调系统运行状况方面表现优异

在处理复杂庞大的建筑运行数据上具有很高的应用价值。

Abstract

Buildings are becoming not only energy-intensive

but also information-intensive. Today's building automation system (BAS) has provided an enormous amount of data about the actual building operation. Valuable insights could be gained from such data. However

due to the data complexity and the lack of advanced analytic tools

only limited and rather simple applications have been found. Data mining (DM) is a promising technology which has great efficiency and effectiveness in discovering hidden knowledge from massive data sets. This study investigates the utilization of DM in analyzing massive BAS data for enhancing building energy efficiency. The DM-related research in the building field is firstly reviewed and then the challenges of practical applications are discussed. A generic DM-based analysis framework is proposed. The framework is applied to analyze the building operational data retrieved from the tallest building in Hong Kong. Two case studies are presented to show the capability of DM in developing robust energy prediction models

identifying building operating behaviors

and evaluating operational performance. The results show that

together with domain knowledge

could be very powerful in the knowledge discovery in massive building operational data and valuable for enhancing the building energy efficiency.

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