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

  • Cheng Fan
  • Fu Xiao
  • Chengchu Yan
Building Sustainability (N Nord, Section Editor)
  • 97 Downloads
Part of the following topical collections:
  1. Topical Collection on Building Sustainability

Abstract

Purpose of Review

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.

Recent Findings

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.

Summary

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.

Keywords

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

Notes

Acknowledgements

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).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Construction Management and Real EstateShenzhen UniversityShenzhenChina
  2. 2.Department of Building Services EngineeringThe Hong Kong Polytechnic UniversityHong KongHong Kong, China
  3. 3.College of Urban ConstructionNanjing Tech UniversityNanjingChina

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