Brick : Metadata schema for portable smart building applications

Balaji, Bharathan; Bhattacharya, Arka; Fierro, Gabriel; Gao, Jingkun; Gluck, Joshua; Hong, Dezhi; Johansen, Aslak; Koh, Jason; Ploennigs, Joern; Agarwal, Yuvraj; Bergés, Mario; Culler, David; Gupta, Rajesh K.; Kjærgaard, Mikkel Baun; Srivastava, Mani; Whitehouse, Kamin

doi:10.1016/j.apenergy.2018.02.091

Cited by 173 publications

(123 citation statements)

References 24 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…In this case, the production of the metadata model would fall into the hands of a qualified person within the contractor company and the metadata model would be under development in parallel with the building, reducing these initial effort hours to a minimum. In the case of the Brick schema, however, it is possible to derive a partially complete Brick model based on IFC building information models, which are far more commonly available [17]. The maintenance of the model is a matter of continuous attention.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings

Markoska

Johansen

Kjærgaard

et al. 2019

ASI

Self Cite

View full text Add to dashboard Cite

Performance testing of components and subsystems of buildings is a promising practice for increasing energy efficiency and closing gaps between intended and actual performance of buildings. A typical shortcoming of performance testing is the difficulty of linking a failing test to a faulty or underperforming component. Furthermore, a failing test can also be linked to a wrongly configured performance test. In this paper, we present Building Metadata Performance Testing (BuMPeT), a method that addresses this shortcoming by using building metadata models to extend performance testing with fault detection and diagnostics (FDD) capabilities. We present four different procedures that apply BuMPeT to different data sources and components. We have applied the proposed method to a case study building, located in Denmark, to test its capacity and benefits. Additionally, we use two real case scenarios to showcase examples of failing performance tests in the building, as well as discovery of causes of underperformance. Finally, to examine the limits to the benefits of the applied procedure, a detailed elaboration of a hypothetical scenario is presented. Our findings demonstrate that the method has potential and it can serve to increase the energy efficiency of a wide range of buildings.

show abstract

Section: Discussion and Future Workmentioning

confidence: 99%

Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings

Markoska

Johansen

Kjærgaard

et al. 2019

ASI

Self Cite

View full text Add to dashboard Cite

show abstract

“…The BrickModel is based on the Brick Schema developed by Balaji et al (2018) [21]. Figure 4 displays a sample of Brick's class hierarchy.…”

Section: Brickmodelmentioning

confidence: 99%

“…However, IFC4 describes only few tags in the description of metadata for data points [18] and, in a survey conducted by the German Architects' Association [19], only 12% of the respondents used BIM in projects at all. The approach for naming and which we described in [20], together with the Brick Schema [21] which will be included in the future ASHRAE Standard 223P [22], fulfils the requirements for simulation and ML.…”

Section: Introductionmentioning

confidence: 99%

“…Equipment: "Physical devices designed for specific tasks controlled by points belonging to it. E.g., light, fan, AHU" [21].  Point: "Points are physical or virtual entities that generate time series data.…”

mentioning

confidence: 99%

“…Physical points include actual sensors and setpoints in a building, whereas virtual points encompass synthetic data streams that are the result of some process which may operate on other time series data, e.g. average floor temperature sensor" [21]. As shown on the left side of Figure 5, we defined ports for heat flows, flows, electric currents, weather, equipment, comfort, transfer to rooms, control and fuels.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Generating Generic Data Sets for Machine Learning Applications in Building Services Using Standardized Time Series Data

Stinner¹,

Yang²,

Schreiber³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

Machine Learning Algorithms (ML) offer a high potential with low manual effort to discover appropriate energy efficiency measures for buildings. Although many building automation systems (BAS) record a high amount of data, technical systems such as boilers provide only a few data points per building. However, machine-learning algorithms require training based on a sufficient number of instances of a technical system in order to enable cross-building use. In contrast to electrical systems, few data sets of actual operation of thermal systems are publicly available. Since 2012, the monitoring system in our test object has continuously provided threshold-based data with a maximum resolution of 1 minute. We monitor the plants, energy consumption and comfort parameters with 9239 data points in total. In this paper, we show how our published data set from this building is structured. In order to facilitate the use of ML, each data point receives a uniform label according to a previously developed approach. Since the documentation of ML data sets varies in the building sector, we show an approach to standardize data sets with special datasheets for thermal systems to provide sufficient information for application of ML. We use the Brick Schema, a unified ontology standard for the description of topology in buildings, which is part of the future ASHRAE Standard 223P. We couple this with an approach we developed for the structured labeling of data points in buildings. We show how to semi-automatically generate physical models based on an open-source Modelica library from this ontology-based model. We show that the models, enriched with real time series data and data sheets, are in good agreement with the measured data. Finally, we show with an ML example that our approach based on Brick Schema and Modelica is able to deliver ML compliant data sets.

show abstract