Multi-dimensional building performance data management for continuous commissioning

Ahmed, Ammar; Ploennigs, Joern; Menzel, Karsten; Cahill, Brian

doi:10.1016/j.aei.2010.06.007

Cited by 51 publications

(25 citation statements)

References 24 publications

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“…Instead, the proposed method computes a simple meaningful linguistic description, which characterizes the entire anomalous event. For an anomaly occurring at time t 1 …”

Section: Linguistic Description Of Anomaliesmentioning

confidence: 99%

“…BEMS use this information to control the heating, cooling and lighting of the building [2], [3]. This type of control has the potential of large energy savings when compared to conventional systems, without sacrificing occupant comfort [1], [9]- [11]. Furthermore, gathering and analyzing sensor data allows the identification of previously unknown building performance characteristics [1].…”

Section: Introductionmentioning

confidence: 99%

“…Current BEMS tools also lack the capability of providing actionable information by processing and integrating gathered data [1]. Some advanced tools specifically created for monitoring and analyzing BEMS data exist in the industry [15]- [19].…”

Section: Introductionmentioning

confidence: 99%

“…When properly tuned, BEMS enable significant energy savings in buildings [1]- [3]. According to the Department of Energy (DOE) over 50% of energy used in buildings is consumed by Heating Ventilation and Air Conditioning (HVAC) units and lighting systems [4].…”

Section: Introductionmentioning

confidence: 99%

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Computational intelligence based anomaly detection for Building Energy Management Systems

Linda

Wijayasekara

Manic

et al. 2012

2012 5th International Symposium on Resilient Control Systems

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Abstract-In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the stateawareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.

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“…Instead, the proposed method computes a simple meaningful linguistic description, which characterizes the entire anomalous event. For an anomaly occurring at time t 1 …”

Section: Linguistic Description Of Anomaliesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational intelligence based anomaly detection for Building Energy Management Systems

Linda

Wijayasekara

Manic

et al. 2012

2012 5th International Symposium on Resilient Control Systems

View full text Add to dashboard Cite

show abstract

“…This tool supports complex queries and advanced features for aggregating, analyzing, and comparing data. It is also used to discover trends and patterns in data (Ahmed et al, 2009). …”

Section: Prerequisites and Current Deficitsmentioning

confidence: 99%

Performance Based Maintenance Scheduling for Building Service Components

Menzel

Tobin

Brown

et al. 2009

Leveraging Knowledge for Innovation in Collaborative Networks

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Abstract. This paper discusses Performance Based Maintenance Scheduling as an enabler for Optimised Building Operation in terms of cost, energy usage, and user comfort, and the role of collaborative networks in achieving these optimal conditions. We finally explain what categories of constraints need to be considered to organize performance based maintenance scheduling in a holistic and integrated manner.

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Building Operation, Control and Management

2018

Building Performance Analysis

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Multi-dimensional building performance data management for continuous commissioning

Cited by 51 publications

References 24 publications

Computational intelligence based anomaly detection for Building Energy Management Systems

Computational intelligence based anomaly detection for Building Energy Management Systems

Performance Based Maintenance Scheduling for Building Service Components

Building Operation, Control and Management

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