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Current Sustainable/Renewable Energy Reports
Erschienen in: Current Sustainable/Renewable Energy Reports 2/2018

16.04.2018 | Building Sustainability (N Nord, Section Editor)

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

verfasst von: Cheng Fan, Fu Xiao, Chengchu Yan

Erschienen in: Current Sustainable/Renewable Energy Reports | Ausgabe 2/2018

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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.
Vorheriger Artikel Future Trends in District Heating Development
Nächster Artikel Utilization of Ground Heat Exchangers: a Review

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Metadaten
Titel
Research and Applications of Data Mining Techniques for Improving Building Operational Performance
verfasst von
Cheng Fan
Fu Xiao
Chengchu Yan
Publikationsdatum
16.04.2018
Verlag
Springer International Publishing
Erschienen in
Current Sustainable/Renewable Energy Reports / Ausgabe 2/2018
Elektronische ISSN: 2196-3010
DOI
https://doi.org/10.1007/s40518-018-0112-x

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