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June 2018, Volume 5, Issue 2, pp 181–188 | Cite as

Research and Applications of Data Mining Techniques for Improving Building Operational Performance

Building Sustainability (N Nord, Section Editor)



This paper reviews the data mining (DM)-related research and applications at the building operation stage. It aims to summarize DM-based solutions for building energy management and reveal current research and development outcomes in analyzing massive building operational data using advanced DM techniques.


Previous studies mainly adopt DM techniques for two tasks, i.e., (1) predictive modeling; (2) fault detection and diagnosis. The knowledge discovered has been successfully utilized to facilitate the decision-making during building operations. Domain expertise play the dominant role in the knowledge discovery process, which limits the chance of discovering novel knowledge.


DM is a promising technology for the development of intelligent and automated building management systems. Despite encouraging results, more research efforts should be made in (1) exploring the usefulness of unsupervised DM, (2) developing generic analytic frameworks, and (3) analyzing unstructured and multi-relational data sets.


Big data Data mining Knowledge discovery Building operational performance Building energy management Intelligent building

This article is part of the Topical Collection on Building Sustainability



The authors gratefully acknowledge the support of this research by the Research Grant Council of the Hong Kong SAR (152181/14E) and the Natural Science Foundation of SZU (Grant No. 2017061).










Fan, C., Xiao, F. & Yan, C. Curr Sustainable Renewable Energy Rep (2018) 5: 181. https://doi.org/10.1007/s40518-018-0112-x



