Methodology to Develop Ontological Building Information Model for Energy Management System in Building Operational Phase

Wicaksono, Hendro; Dobreva, Preslava; Häfner, Polina; Rogalski, Sven

doi:10.1007/978-3-662-46549-3_11

Cited by 7 publications

(7 citation statements)

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“…Instead they represent ways of combining diverse information coming from different building systems and sub-systems. In this paper, the method of representing these rules in similar to the one described in [12]. According to (1), if the thermal rating of a cooling system is less than X RT and EEF is less than Y kW/RT, the cooling system can be regarded as energy efficient.…”

Section: Evaluation Inference Rulesmentioning

confidence: 99%

“…Ontology-based approaches can provide a higher level of abstraction, flexibility and implementation independence to attain a shared understanding of information across complex heterogeneous systems [11,12]. Ontology is related to the definition of common language and representation to structure information and knowledge and it provides a common vocabulary to express several properties, classes, and attributes of the components.…”

Section: Introductionmentioning

confidence: 99%

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An Ontology-Based Framework for Building Energy Management with IoT

et al. 2019

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In this paper, we develop an ontology-based framework for energy management in buildings. We divide the functional architecture of a building energy management system into three interconnected modules that include building management system (BMS), benchmarking (BMK), and evaluation & control (ENC) modules. The BMS module is responsible for measuring several useful environmental parameters, as well as real-time energy consumption of the building. The BMK module provides the necessary information required to understand the context and cause of building energy efficiency or inefficiency, and also the information which can further differentiate normal and abnormal energy consumption in different scenarios. The ENC module evaluates all the information coming from BMS and BMK modules, the information is contextualized, and finally the cause of energy inefficiency/abnormality and mitigating control actions are determined. Methodology to design appropriate ontology and inference rules for various modules is also discussed. With the help of actual data obtained from three different rooms in a commercial building in Singapore, a case study is developed to demonstrate the application and advantages of the proposed framework. By mitigating the appropriate cause of abnormal inefficiency, we can achieve 5.7%, 11.8% and 8.7% energy savings in Room 1, Room 2, and Room 3 respectively, while creating minimum inconvenience for the users.

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Section: Evaluation Inference Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Framework for Building Energy Management with IoT

et al. 2019

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“…In the built environment domain, the openBIM IFC data model is already experiencing strong uptake [22]. This model uses a less expressive format than OWL, and its federation into OWL is an active area of research [23]. Whilst this does not sufficiently model energy management concepts, its extension toward BEMS would improve the adoption of the resulting model.…”

Section: B Interoperating Legacy Systems With Advanced Analytics-thementioning

confidence: 99%

“…The project aimed to produce a BEMS, which could be retrofitted into public buildings with minimal investment, to exploit an enhanced sensing infrastructure and the existing BEMS, augmented with analytics and visualization components through a semantic Web approach. This involves a semantic knowledge base, which describes the physical properties of the building as an extension of the openBIM IFC data model [18], [23], through an RDF store and SPARQL endpoint. The semantic model in the knowledge base also contextualizes the historical data stored in an MySQL database by formalizing a shared meaning.…”

Section: Overview Of Proposed Solutionmentioning

confidence: 99%

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User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization

Howell

Wicaksono

Yüce

et al. 2019

IEEE Trans. Cybern.

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This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.

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Ontology-Based Semantic Retrieval Method of Energy Consumption Management

Xiao

Lin

2018

Advances in Informatics and Computing in Civil and Construction Engineering

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Methodology to Develop Ontological Building Information Model for Energy Management System in Building Operational Phase

Cited by 7 publications

References 5 publications

An Ontology-Based Framework for Building Energy Management with IoT

An Ontology-Based Framework for Building Energy Management with IoT

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization

Ontology-Based Semantic Retrieval Method of Energy Consumption Management

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