Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80920
Title: Discovering complex knowledge in massive building operational data using graph mining for building energy management
Authors: Fan, C
Song, M
Xiao, F 
Xue, X
Keywords: Building automation system
Building operational performance
Data mining
Graph mining
Knowledge discovery
Issue Date: 2019
Publisher: Elsevier
Source: Energy procedia, 2019, v. 158, p. 2481-2487 How to cite?
Journal: Energy procedia 
Abstract: Discovering useful knowledge from massive building operational data is considered as a promising way to improve building operational performance. Conventional data analytics can only handle data stored in a single two-dimensional data table, while lacking the ability to represent and analyze data in complex formats (e.g., multi-relational databases). Graphs are capable of integrating and representing various types of information, such as spatial information and affiliations. The knowledge discovery based on graph data can therefore be very helpful for revealing complex relationships in building operations. This study proposes a novel methodology for analyzing massive building operational data using graph-mining techniques. Two problems are specifically addressed, i.e., graph generation based on building operational data and knowledge discovery from graph data. The methodology has been applied to analyze the building operational data retrieved from a real building in Hong Kong. The research results show that the knowledge obtained is valuable to characterize complex building operation patterns and identify atypical operations.
Description: 10th International Conference on Applied Energy, ICAE 2018, Hong Kong, 22-25 August 2018
URI: http://hdl.handle.net/10397/80920
EISSN: 1876-6102
DOI: 10.1016/j.egypro.2019.01.378
Rights: © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy.
The following publication Fan, C., Song, M., Xiao, F., & Xue, X. (2019). Discovering Complex Knowledge in Massive Building Operational Data Using Graph Mining for Building Energy Management. Energy Procedia, 158, 2481-2487 is available at https://doi.org/10.1016/j.egypro.2019.01.378
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