Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement

Fu Xiao; Shengwei Wang; Cheng Fan

doi:10.1109/SMARTCOMP.2017.7947023

Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

12 Citations (Scopus)

Abstract

This paper aims to explore the potential application of advanced DM techniques for effective utilization of big building operational data. Case studies of mining the operational data of an institutional building for cooling load prediction and operation performance improvement is presented. Deep learning-based prediction techniques, decision tree and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for forecasting 24-hour ahead building cooling load profiles, identifying typical building operation patterns and spotting energy conservation opportunities.

Original language	English
Title of host publication	2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
Publisher	IEEE
ISBN (Electronic)	9781509065172
DOIs	https://doi.org/10.1109/SMARTCOMP.2017.7947023
Publication status	Published - 12 Jun 2017
Event	2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 - Hong Kong, Hong Kong Duration: 29 May 2017 → 31 May 2017

Conference

Conference	2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
Country/Territory	Hong Kong
City	Hong Kong
Period	29/05/17 → 31/05/17

Keywords

Big building operational data
Building cooling load
Building energy efficiency
Data mining
Deep learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

UN SDGs

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

More information

10.1109/SMARTCOMP.2017.7947023

http://hdl.handle.net/10397/103089

Cite this

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title = "Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement",

abstract = "This paper aims to explore the potential application of advanced DM techniques for effective utilization of big building operational data. Case studies of mining the operational data of an institutional building for cooling load prediction and operation performance improvement is presented. Deep learning-based prediction techniques, decision tree and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for forecasting 24-hour ahead building cooling load profiles, identifying typical building operation patterns and spotting energy conservation opportunities.",

keywords = "Big building operational data, Building cooling load, Building energy efficiency, Data mining, Deep learning",

author = "Fu Xiao and Shengwei Wang and Cheng Fan",

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doi = "10.1109/SMARTCOMP.2017.7947023",

language = "English",

booktitle = "2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017",

publisher = "IEEE",

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note = "2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 ; Conference date: 29-05-2017 Through 31-05-2017",

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Xiao, F , Wang, S & Fan, C 2017, Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement. in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017., 7947023, IEEE, 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, Hong Kong, 29/05/17. https://doi.org/10.1109/SMARTCOMP.2017.7947023

Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement. / Xiao, Fu ; Wang, Shengwei; Fan, Cheng.
2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017. IEEE, 2017. 7947023.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement

AU - Xiao, Fu

AU - Wang, Shengwei

AU - Fan, Cheng

PY - 2017/6/12

Y1 - 2017/6/12

N2 - This paper aims to explore the potential application of advanced DM techniques for effective utilization of big building operational data. Case studies of mining the operational data of an institutional building for cooling load prediction and operation performance improvement is presented. Deep learning-based prediction techniques, decision tree and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for forecasting 24-hour ahead building cooling load profiles, identifying typical building operation patterns and spotting energy conservation opportunities.

AB - This paper aims to explore the potential application of advanced DM techniques for effective utilization of big building operational data. Case studies of mining the operational data of an institutional building for cooling load prediction and operation performance improvement is presented. Deep learning-based prediction techniques, decision tree and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for forecasting 24-hour ahead building cooling load profiles, identifying typical building operation patterns and spotting energy conservation opportunities.

KW - Big building operational data

KW - Building cooling load

KW - Building energy efficiency

KW - Data mining

KW - Deep learning

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U2 - 10.1109/SMARTCOMP.2017.7947023

DO - 10.1109/SMARTCOMP.2017.7947023

M3 - Conference article published in proceeding or book

BT - 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017

PB - IEEE

T2 - 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017

Y2 - 29 May 2017 through 31 May 2017

ER -

Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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