Assessment of Building Operational Performance Using Data Mining Techniques: A Case Study

Cheng Fan; Fu Xiao

doi:10.1016/j.egypro.2017.03.270

Assessment of Building Operational Performance Using Data Mining Techniques: A Case Study

Cheng Fan, Fu Xiao

The Hong Kong Polytechnic University

Research output: Journal article publication › Conference article › Academic research › peer-review

19 Citations (Scopus)

Abstract

Today's buildings are not only energy intensive, but also information intensive. Massive amounts of operational data are available for knowledge discovery. Data mining (DM) has excellent ability in extracting insights from massive data. This paper performs a case study on the assessment of building operational performance using DM techniques. Typical DM techniques are compared and considerations for choosing specific DM techniques for the case study are presented. The methodology developed has been applied to analyze the data retrieved from a university building in Hong Kong. Useful insights have been obtained to identify typical operation patterns and energy conservation opportunities.

Original language	English
Pages (from-to)	1070-1078
Number of pages	9
Journal	Energy Procedia
Volume	111
DOIs	https://doi.org/10.1016/j.egypro.2017.03.270
Publication status	Published - 1 Mar 2017
Event	8th International Conference on Sustainability in Energy and Buildings, SEB 2016 - Turin, Italy Duration: 11 Sept 2016 → 13 Sept 2016

Keywords

Big Data
Building Automation System
Building Energy Conservation
Data Mining
Intelligent Building

ASJC Scopus subject areas

General Energy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1016/j.egypro.2017.03.270

http://hdl.handle.net/10397/103117

Cite this

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abstract = "Today's buildings are not only energy intensive, but also information intensive. Massive amounts of operational data are available for knowledge discovery. Data mining (DM) has excellent ability in extracting insights from massive data. This paper performs a case study on the assessment of building operational performance using DM techniques. Typical DM techniques are compared and considerations for choosing specific DM techniques for the case study are presented. The methodology developed has been applied to analyze the data retrieved from a university building in Hong Kong. Useful insights have been obtained to identify typical operation patterns and energy conservation opportunities.",

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AB - Today's buildings are not only energy intensive, but also information intensive. Massive amounts of operational data are available for knowledge discovery. Data mining (DM) has excellent ability in extracting insights from massive data. This paper performs a case study on the assessment of building operational performance using DM techniques. Typical DM techniques are compared and considerations for choosing specific DM techniques for the case study are presented. The methodology developed has been applied to analyze the data retrieved from a university building in Hong Kong. Useful insights have been obtained to identify typical operation patterns and energy conservation opportunities.

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KW - Building Automation System

KW - Building Energy Conservation

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ER -

Assessment of Building Operational Performance Using Data Mining Techniques: A Case Study

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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