Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control

Benndorf, Gesa A.; Wystrcil, Dominik; Réhault, Nicolas

doi:10.1063/1.5053110

Cited by 45 publications

(19 citation statements)

References 181 publications

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“…In the past decade, the integration of intelligence (e.g., microcontrollers and microcomputers), sensors, and networking (i.e., Internet of Things (IoT) connectivity [14]) has become more common in all types of buildings [15]. These systems can generate significant amounts of data if they record operational parameters with time intervals on the order of seconds or minutes [16]. Accordingly, digitization has enabled energy-focused advances in building maintenance [17], commissioning practices [18], asset tracking [19], and energy audits [20].…”

Section: Digitalization Of Building Datamentioning

confidence: 99%

“…However, a lack of interoperability in the semantic layer (sublayer 4 in Figure 1) is currently a significant problem that hinders the streamlined integration of interdependent software applications [21] and the development of applications that can be reused across buildings [26]. In large commercial buildings, building automation systems (BASs) have progressively adopted standard communication protocols (e.g., BACnet [27], KNX [28]) during the past two decades [16]. This has facilitated greater integration of building systems to a certain degree, but mapping legacy building automation system (BAS) metadata to semantic information models requires extensive expertise [21] and significant time and cost [29].…”

Section: Interoperability Framework and The Role Of Semantic Interopmentioning

confidence: 99%

“…Bergmann et al [21] summarize the purpose of several building metadata schemas in the context of proposing a pathway to drive semantic interoperability for grid-interactive energy-efficient buildings. Benndorf et al [16] review semantic interoperability in building design and building automation, including new Semantic Web technologies. Bhattacharya et al [53] compare the ability of three ontologies to describe concepts necessary to run a set of applications described in the academic literature.…”

Section: Contribution Of This Reviewmentioning

confidence: 99%

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Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

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Section: Digitalization Of Building Datamentioning

confidence: 99%

Section: Interoperability Framework and The Role Of Semantic Interopmentioning

confidence: 99%

Section: Contribution Of This Reviewmentioning

confidence: 99%

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Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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“…Whilst the model-based approach can result in an effective anomaly detection tool, it does require detailed information and expert knowledge about the particular HVAC system and is therefore suitable for clearly defined and well-established technologies. Some of the barriers to widespread uptake of model-based anomaly detections in buildings [23] may be removed by recent innovations in Building Information Models (BIM).…”

Section: Anomaly Detection In Building Hvac Systemsmentioning

confidence: 99%

The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes

Wang

Parkinson

et al. 2019

Building and Environment

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Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochasticbased two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants' votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

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“…Further applications are the modelling and prediction of behavior of energy systems or energy consumption [1]. They can support the normalization of metadata of data points [2] or fault detection [3]. Since different disciplines use the term "data point" differently, we use the following definition, which we derived from the use in building automation (BA): a data point is an information carrier that continuously provides information about a state.…”

Section: Introductionmentioning

confidence: 99%

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)

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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.

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Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control

Cited by 45 publications

References 181 publications

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes

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

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