Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43506
Title: Temporal knowledge discovery in big BAS data for building energy management
Authors: Fan, C
Xiao, F 
Madsen, H
Wang, D 
Keywords: Big data
Building automation system
Building energy management
Temporal knowledge discovery
Time series data mining
Issue Date: 2015
Publisher: Elsevier
Source: Energy and buildings, 2015, v. 109, p. 75-89 How to cite?
Journal: Energy and buildings 
Abstract: With the advances of information technologies, today's building automation systems (BASs) are capable of managing building operational performance in an efficient and convenient way. Meanwhile, the amount of real-time monitoring and control data in BASs grows continually in the building lifecycle, which stimulates an intense demand for powerful big data analysis tools in BASs. Existing big data analytics adopted in the building automation industry focus on mining cross-sectional relationships, whereas the temporal relationships, i.e., the relationships over time, are usually overlooked. However, building operations are typically dynamic and BAS data are essentially multivariate time series data. This paper presents a time series data mining methodology for temporal knowledge discovery in big BAS data. A number of time series data mining techniques are explored and carefully assembled, including the Symbolic Aggregate approXimation (SAX), motif discovery, and temporal association rule mining. This study also develops two methods for the efficient post-processing of knowledge discovered. The methodology has been applied to analyze the BAS data retrieved from a real building. The temporal knowledge discovered is valuable to identify dynamics, patterns and anomalies in building operations, derive temporal association rules within and between subsystems, assess building system performance and spot opportunities in energy conservation.
URI: http://hdl.handle.net/10397/43506
ISSN: 0378-7788
EISSN: 1872-6178
DOI: 10.1016/j.enbuild.2015.09.060
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