Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data

Purpose Effective use of post-occupancy evaluation (POE) data – quantitative physical measurements and qualitative occupants’ perceptions are limited due to practical challenges and research gaps. Although building information modelling (BIM) has enabled a paradigm shift in the architecture, engineering and construction (AEC) industries, its use in facility management (FM) is still infancy. Limited research has used building performance data to enable changes to BIM models for the benefit of FM. This paper aims to propose the innovative process to collect and contextualize two fragmented types of POE data sets by filling methodological gap in POE research. Moreover, it presents innovative modelling techniques to facilitate BIM as a more effective platform to visualize such currently fragmented data sets in real-time while enabling a decision-making model to benefit facility managers. Design/methodology/approach The paper presents a process of capturing cloud-based POE data, both wireless sensor network-based physical measurement data and mobile app-based occupant perception data. Real-time capture and visualization of such building performance data was demonstrated through a pilot data collection. Subsequently, the innovative visualization of the cloud connected data is supported by a prototype game engine-based BIM model. Findings Cloud-based POE data, both quantitative physical measurements and qualitative occupants’ reported perceptions, can be effectively used in FM practice with the use of innovative data capture and visualization techniques in a beneficial manner for facility operation and management decisions. This paper also demonstrates the ability of BIM to serve as a “single source of truth” to support post-construction building performance data. Originality/value While addressing a number of research gaps, the paper provides a holistic approach to BIM-based performance monitoring for smart FM to achieve the ultimate vision of BIM enabled FM. The innovative system is expected to provide a powerful and practical tool for data collection, analysis and visualization for intelligent facility management decision making. This paper contributes to fill an important research and practice gap in the area of next generation smart building management practices.

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Data mining analysis of building simulation performance data

Building Services Engineering Research and Technology ◽

10.1191/0143624404bt098oa ◽

2004 ◽

Vol 25 (3) ◽

pp. 253-267 ◽

Cited By ~ 8

Author(s):

C Morbitzer ◽

P Strachan ◽

C Simpson

Keyword(s):

Data Mining ◽

Performance Data ◽

Building Simulation ◽

Simulation Performance ◽

Data Mining Analysis

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High performance data mining using the nearest neighbor join

2002 IEEE International Conference on Data Mining, 2002. Proceedings. ◽

10.1109/icdm.2002.1183884 ◽

2003 ◽

Cited By ~ 8

Author(s):

C. Bohm ◽

F. Krebs

Keyword(s):

Data Mining ◽

High Performance ◽

Nearest Neighbor ◽

Neighbor Join ◽

Performance Data

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High performance data mining (tutorial PM-3)

Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00 ◽

10.1145/349093.349109 ◽

2000 ◽

Cited By ~ 3

Author(s):

Vipin Kumar ◽

Mohammed Zaki

Keyword(s):

Data Mining ◽

High Performance ◽

Performance Data

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Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters

2009 IEEE International Conference on Data Mining Workshops ◽

10.1109/icdmw.2009.34 ◽

2009 ◽

Cited By ~ 27

Author(s):

Dennis Wegener ◽

Michael Mock ◽

Deyaa Adranale ◽

Stefan Wrobel

Keyword(s):

Data Mining ◽

High Performance ◽

Large Data ◽

Performance Data

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Analysis of basic building performance data for identification of performance issues

Facilities ◽

10.1108/f-01-2016-0003 ◽

2017 ◽

Vol 35 (13/14) ◽

pp. 801-817 ◽

Cited By ~ 5

Author(s):

Tristan Gerrish ◽

Kirti Ruikar ◽

Malcolm Cook ◽

Mark Johnson ◽

Mark Phillip

Keyword(s):

Performance Metrics ◽

Thermal Response ◽

Building Design ◽

Energy Performance ◽

Performance Data ◽

Building Performance ◽

Historical Building ◽

Content Type ◽

Performance Issues ◽

Under Performing

Purpose The aim of this paper is to demonstrate the use of historical building performance data to identify potential issues with the build quality and operation of a building, as a means of narrowing the scope of in-depth further review. Design/methodology/approach The response of a room to the difference between internal and external temperatures is used to demonstrate patterns in thermal response across monitored rooms in a single building, to clearly show where rooms are under-performing in terms of their ability to retain heat during unconditioned hours. This procedure is applied to three buildings of different types, identifying the scope and limitation of this method and indicating areas of building performance deficiency. Findings The response of a single space to changing internal and external temperatures can be used to determine whether it responds differently to other monitored buildings. Spaces where thermal bridging and changes in use from design were encountered exhibit noticeably different responses. Research limitations/implications Application of this methodology is limited to buildings where temperature monitoring is undertaken both internally for a variety of spaces, and externally, and where knowledge of the uses of monitored spaces is available. Naturally ventilated buildings would be more suitable for analysis using this method. Originality/value This paper contributes to the understanding of building energy performance from a data-driven perspective, to the knowledge on the disparity between building design intent and reality, and to the use of basic commonly recorded performance metrics for analysis of potentially detrimental building performance issues.

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