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Abstract

Narrowing the performance deficit between design intent and the real-time environmental and energy performance of buildings is a complex and involved task, impacting on all building stakeholders. Buildings are designed, built and operated with increasingly complex technologies. Throughout their life-cycle, they produce vast quantities of data. However, many commercial buildings do not perform as originally intended. This paper presents a semantic web based approach to the performance gap problem, describing how heterogeneous building data sources can be transformed into semantically enriched information. A performance assessment ontology and performance framework (software tool) are introduced, which use this heterogeneous data as a service for a structured performance analysis. The demonstrator illustrates how heterogeneous data can be published semantically and then interpreted using a life-cycle performance framework approach.

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... This segregation is translated into data modelled to meet the requirements of independent systems (e.g. BAS, assetmanagement system, occupancy, design and construction data) rather than as part of the overarching built environment entity (Corry et al., 2015;Woodhead et al., 2018). In addition, systems are distributed and building components information is often outdated, incomplete and inaccurate when exchanged between the asset life cycles (e.g. from design and construction to operations and management) . ...
... linked data and ontologies) have become popular for built environment data integration in the last decade (Donkers et al., 2022;Kim et al., 2018;Tang et al., 2019). Semantic web technologies drive data integration while enhancing understandability by achieving broad classification and description of built environment entities (Corry et al., 2015;Pauwels et al., 2017). Ontologies are domain specific (e.g. ...
... Data integration is conducted through sensor or asset IDs described in RDF. Corry et al. (2015) and Hu et al. (2016) developed a hybrid architecture linking the relational DBs of time-series data through the sensor ID to sensor reference information using SSN and then used Sparql to discover the relationships between sensors and semantically described building contextual data in IFC. McGlinn et al. (2017) used a similar approach to store actuator and BIM data in RDF and used Sparql to reason the interdependencies between them and then integrated the results with time-series data from sensors (including reference sensor data) from a relational DB. ...
Article
Improving efficiency of operations is a major challenge in Facility Management given the limitations of outsourcing individual building functions to third-party companies. The status of each building function is isolated in siloes which are controlled by these third-party companies. Companies provide access to aggregated information in the form of reports through web portals, emails, or bureaucratic processes. Digital Twins represent an emerging approach to return awareness and control to facility managers by automating all levels of information access (from granular data to defined KPIs and reports) and actuation. This paper proposes a low-latency data integration method that supports actuation and decision making in Facility Management, including construction, operations and maintenance data, and Internet of Things. The method uses federated data models and semantic web ontologies, and it is implemented within a data lake architecture with connections to siloed data to keep the delegation of responsibilities of data owners. A case study in the Alan Reece building (Cambridge, United Kingdom) demonstrates the approach by enabling Fault-Detection-and-Diagnosis of the Heating Ventilation and Air Conditioning system for facility management.
... The main challenge in the development of a digital twin for a built environment is the natural segregation of data storage (Hu et al. 2016). Data is modelled to meet independent systems requirements (e.g., building management system, asset management system, occupancy, design and construction data, etc.) rather than as part of the overarching built environment entity (Corry et al. 2015). Furthermore, systems and buildings components information is often outdated, incomplete, and inaccurate when exchanged along the assets' life cycles (e.g., from design and construction to operations and management) (O'Donnell et al. 2013). ...
... Semantic web approaches drive data integration while achieving broad classification and description of built environment entities (Pauwels et al. 2017, Corry et al. 2015. Ontologies like ifcOWL or Brick Schema have standardise the way in which construction and systems data is modelled, but neither incorporate all the intricacies and complexities of buildings. ...
... This process is also known as data engineering. Ontologies are widely used to drive the integration of diverse sources (Corry et al. 2015, Hu et al. 2016. Even that the ACP data strategy is used to govern the flow of realtime data, there are two ontologies that help understanding sensor data in the built environment: the ISO 12006 (ISO 2015) (Industry Foundation Classes -IFC data model) and Brick Schema. ...
... Further to the initial static and dynamic data type categorization, on the basis of existing ontologies [47,48,29] with more comprehensively defined data types in the literature, the current study categorized data types required for a SCCx ontology as: (1) external conditions, (2) indoor conditions, (3) building systems and components, (4) energy use, (5) maintenance, (6) occupant-related data, (7) physical building information, (8) performance-based data, and (9) simulation-based data, as presented in Table 2. Table 2. Summary of data types used in state-of-the-art ontologies. ...
... External conditions [49], [14], [21], [50], [51], [52], [53], [48], [54], [35], [55], [56], [57], [58], [59] Indoor conditions [60], [61], [62], [49], [14], [47], [24], [21], [46], [50], [63], [51], [15], [64], [53], [48], [37], [54], [35], [55], [29], [56], [57], [15], [65], [66], [67], [58], [68], [69], [59] Building systems/components [60], [61], [62], [49], [14], [24], [21], [46], [50], [63], [15], [64], [53], [48], [54], [35], [55], [29], [56], [57], [65], [67], [58], [68], [69] Energy use [61], [49], [14], [47], [24], [21], [50], [51], [52], [64], [53], [48], [54], [35], [55], [29], [57], [65], [58], [68], [59] Maintenance [52], [64], [53], [67], [68], [69] Occupant-related data [61], [14], [47], [24], [21], [50], [51], [52], [64], [48], [54], [56], [67], [58], [69], [59] Physical building information [60], [61], [14], [47], [21], [46], [50], [63], [51], [15], [64], [53], [48], [37], [54], [35], [55], [29], [56], [57], [15], [65], [66], [67], [58], [68], [69] Performance-based data [47], [21], [46], [50], [63], [52], [57], [58], [68], [69], [59] Simulation-based data [61], [62], [47], [46], [50], [54], [67], [58] In addition to building data, building performance evaluation requires an understanding of climatic conditions, which are either measured or obtained from weather forecasts. Knowledge of indoor environmental conditions measured by BMS-integrated sensors are also important since associated data points are used as input to a BMS for adjusting actuators. ...
... External conditions [49], [14], [21], [50], [51], [52], [53], [48], [54], [35], [55], [56], [57], [58], [59] Indoor conditions [60], [61], [62], [49], [14], [47], [24], [21], [46], [50], [63], [51], [15], [64], [53], [48], [37], [54], [35], [55], [29], [56], [57], [15], [65], [66], [67], [58], [68], [69], [59] Building systems/components [60], [61], [62], [49], [14], [24], [21], [46], [50], [63], [15], [64], [53], [48], [54], [35], [55], [29], [56], [57], [65], [67], [58], [68], [69] Energy use [61], [49], [14], [47], [24], [21], [50], [51], [52], [64], [53], [48], [54], [35], [55], [29], [57], [65], [58], [68], [59] Maintenance [52], [64], [53], [67], [68], [69] Occupant-related data [61], [14], [47], [24], [21], [50], [51], [52], [64], [48], [54], [56], [67], [58], [69], [59] Physical building information [60], [61], [14], [47], [21], [46], [50], [63], [51], [15], [64], [53], [48], [37], [54], [35], [55], [29], [56], [57], [15], [65], [66], [67], [58], [68], [69] Performance-based data [47], [21], [46], [50], [63], [52], [57], [58], [68], [69], [59] Simulation-based data [61], [62], [47], [46], [50], [54], [67], [58] In addition to building data, building performance evaluation requires an understanding of climatic conditions, which are either measured or obtained from weather forecasts. Knowledge of indoor environmental conditions measured by BMS-integrated sensors are also important since associated data points are used as input to a BMS for adjusting actuators. ...
Preprint
Full-text available
Smart and continuous commissioning (SCCx) of buildings can result in a significant reduction in the gap between design and operational performance. Ontologies play an important role in SCCx as they facilitate data readability and reasoning by machines. A better understanding of ontologies is required in order to develop and incorporate them in SCCx. This paper critically reviews the state-of-the-art research on building data ontologies since 2014 within the SCCx domain through sorting them based on building data types, general approaches, and applications. The data types of two main domains of building information modeling and building management system have been considered in the majority of existing ontologies. Three main applications are evident from a critical analysis of existing ontologies: (1) key performance indicator calculation, (2) building performance improvement, and (3) fault detection and diagnosis. The key gaps found in the literature review are a holistic ontology for SCCx and insight on how such approaches should be evaluated. Based on these findings, this study provides recommendations for future necessary research including: identification of SCCx-related data types, assessment of ontology performance, and creation of open-source approaches.
... This allows linking machine-readable data about a knowledge area with its counterpart in another, via semantic web Universal Resource Identifiers (URI); a critical step in data exchanges in software systems. These technologies have the potential to address the persistent limitations of interoperability between BPS tools and design software, as well as manage effectively the heterogeneity and large volumes of data in these domains (Corry et al., 2015). Semantics aid the digitalization of building data and the conversion of BIM from a passive repository towards a building-oriented data service, by providing reusable, extensible, and machine-readable data webs. ...
... Examples, of ontologies associated with static data types are BOT (Rasmussen, 2021), which describes topological relationships between building components and is aligned with building operations ontologies, and ifcOWL (Terkaj & Pauwels, 2017), which represents the IFC schema for building construction data in the web ontology language (OWL) (OWL, 2012). A limited number of ontologies focus on performance analytics, such as SimModelRDF (Pauwels, Corry, O'Donnell, 2014), which is an OWL translation of the SimModel XML schema and Performance Framework (PF), a high-level description of the relations between performance metrics and performance objectives (Corry et al. 2015). Other instances of ontologies are related to building heritage and preservation for documentation purposes (Pauwels, Zhang and Lee, 2017), as well as life cycle management and environmental assessment at the product level for buildings (Tchouanguem et al., 2019). ...
... OPA addresses the limitations of linking performance objectives with analyses outputs, as well as linking these outputs with the relevant object geometry. While SimModelRDF links properties to analyses outputs (input/output) and PF (Corry et al., 2015) links objectives to metrics, the inter linking of properties with analysis outputs and performance objectives is still not possible. Moreover, OPA captures the concept of energy audit data. ...
Conference Paper
Full-text available
While the Architecture Engineering Construction and Owner-operated (AECO) industry has been successful in digitizing data concerning buildings through Building Information Modeling (BIM) applications, transforming these data into usable digital services (digitalizing) has not been fully addressed. The Semantic Web allows for the creation of abstraction layers that enable building data as a service. This paper proposes Semantic Web ontologies for representing buildings, the relationships between their elements and analytical data, along with attendant annotation systems. This method enables bi-directional exchanges between heterogeneous platforms, introducing flexibility in representing, sharing and re-using data. The work demonstrates a framework for the digitalization of building data and a service-oriented model, improving stakeholder collaboration.
... This type of problem often aims to develop a new modeling platform if the existing modeling environment cannot meet a new performance aspect and/or further integration needs, or if the existing modeling environment should be revised or reconstructed. Exemplary studies concerning design configuration include exterior lighting design tool [37], design expert system [38][39][40][41], open knowledge base for HPB [42], optimization framework for HPB [43], BIM interoperability specification [44], BIM-DOE design framework [29], BIM based optimization framework for HPB [45], sustainable BIM [41,46,47], semantic web framework [48], ontology framework for sustainable structure [49], performance assessment ontology [50], simulation based framework [24,51], façade design framework [52][53][54], single house design framework [55], BMS framework for design and operation [56], qualitative assessment tool for building envelope [57], multicriteria decision making framework [16], case based reasoning framework for HPB [31], and cross-domain building data share platform [58]. ...
... Performance assessment ontology, simulation Domain Model ontology, semantic Sensor Network ontology [50], SEMERGY building model [48], Information extraction ontology [27], gbXML for as-built building scanning [78], SysML for sustainable building design [79,80], Hierarchical graph-based building model [81], Relational database schema for transformation of BIM [82], Productoriented knowledge base and optimization process model [83] KR-II-2. ...
... Structural design rule [49], ifcOWL [50,84], Semantic BIM [55], SEMERGY building model [48], Linked data ontology [42], Energy efficiency knowledge base [ A semantic model is a set of descriptions and specifications that represent information about the subjects of the problem universe, structure/hierarchy/relations of the subjects, and their properties and functions. Ontology is a schema or method to construct a semantic model by specifying how categories and concepts are defined, what properties and relations are drawn between the concepts, how data and entities substantiate the concept, and any other significance to explain the problem universe. ...
Article
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New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.
... Throughout this work we distinguish a certain method of BMServ, such as the APAR rule set [8] and the instance of such a method, which actually is deployed in some real building. Although it is proven that these methods help to significantly improve the energy efficiency of a building, still, the sector fails to achieve the anticipated goals [9]. ...
... Finally, the operational history of a building including time series readings of sensor and actuators as recorded in the Building Management System (BMS) need to be available. Typically this knowledge needs to be collected from disparate sources and extracted from heterogeneous formats such as textual descriptions, spreadsheets, databases, human experts and drawings [9,11]. ...
... Existing contributions describing ABMS are analysed as presented in Section 2. Most contributions on ABMS use ontology-based knowledge representation. A key motivation in this regard is the ability to successfully cope with heterogeneous data formats and separated knowledge silos prevalent in the building domain [9,11,23]. Some limitations in the existing contributions can be identified. ...
Article
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Open Access! Despite its high potential, the building's sector lags behind in reducing its energy demand. Tremendous savings can be achieved by deploying building management services during operation, however, the manual deployment of these services needs to be undertaken by experts and it is a tedious, time and cost consuming task. It requires detailed expert knowledge to match the diverse requirements of services with the present constellation of envelope, equipment and automation system in a target building. To enable the widespread deployment of these services, this knowledge-intensive task needs to be automated. Knowledge-based methods solve this task, however, their widespread adoption is hampered and solutions proposed in the past do not stick to basic principles of state of the art knowledge engineering methods. To fill this gap we present a novel methodological approach for the design of knowledge-based systems for the automated deployment of building management services. The approach covers the essential steps and best practices: (1) representation of terminological knowledge of a building and its systems based on well-established knowledge engineering methods; (2) representation and capturing of assertional knowledge on a real building portfolio based on open standards; and (3) use of the acquired knowledge for the automated deployment of building management services to increase the energy efficiency of buildings during operation. We validate the methodological approach by deploying it in a real-world large-scale European pilot on a diverse portfolio of buildings and a novel set of building management services. In addition, a novel ontology, which reuses and extends existing ontologies is presented.
... As a result, cities currently consume 'two-thirds of primary energy resources and are responsible for more than 70% of Green House Gas emissions worldwide' [2]. Buildings in these cities account for 40% of global energy consumption [3]. In order to address this issue, there is great potential in urban energy modeling which can result in increased energy use efficiency on an urban and building level [4]. ...
... More readily available information could indeed facilitate the identification of problems and solutions with respect to urban energy consumption [6]. Moreover, while many studies focus on integrating data on the building scale [7,3,5,8], most goals for reducing energy use and Green House Gasses are set on a national level, and most action is taken at the city scale [9]. Abbasabadi and Mehdi Ashayeri [10] describe the fundamentally different approaches in assessing urban energy use. ...
Conference Paper
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There is great potential in urban energy modeling for mitigating the effects of increasing energy consumption in cities. However, there is limited integration of traditional building information and urban data in general. Therefore, this project suggests a novel data integration structure, the Neighborhood Energy Ontology (NEO). This ontology aims to connect urban data from different domains and scales to provide more intelligible insight to the end user. In order to assist with this goal, a dashboard was created which allows the end-user to interact with the data and come to new insights. It is suggested that the created ontology, in combination with the dashboard, is a suitable proof-of-concept to show how semantic solutions can aid in improving the potential of urban energy modeling to mitigate the adverse effects of increasing urbanization.
... Ontology is a formal metadata model of semantic web technology that can be used to represent knowledge as a set of concepts within specific domains and relationships among the concepts [8], and could be a solution to data integration for improving the availability and efficiency of information [9]. Recent studies [10][11][12][13][14] have shown that ontology-based approaches are practical and efficient in building energy management with advantages including structured data schema of knowledge representation, convenient bridging for data interoperability and cross-domain linking, and efficient logic inference for implicit knowledge discovery. Consequently, ontology-based approaches could provide solution to data integration and automatic modeling for BEM. ...
... Corry et. al. [11] developed an ontology framework for performance assessment (including building function, thermal loads, and system performance) by integrating data from heterogeneous sources (including building silo, legislation silo, human resources silo, and inventory silo). The ontology framework was implemented in an actual building and took Predicted Mean Vote (PMV) as the example. ...
... Semantic web technologies promise to accomplish higher interoperability levels in the architecture, engineering, and construction (AEC) domain [67]. Multiple researchers applied semantic web technologies to create rich digital representations of buildings [12], some of whom for research related to occupants' comfort [17,22,25,30,64,69]. To better understand the existing promises and challenges of semantic web technologies to solve the interoperability issues, the following subsections give an overview of state-of-the-art semantic web developments related to occupants, their feedback, and their building performance assessments. ...
... Hu et al. [42] and Donkers et al. [22] stored performance metrics in an RDF format to evaluate building performance. Their work is based on ontologies previously developed by Hu et al. [40,41] and Corry et al. [17]. A similar ontology was created by Li et al. [58] and describes stakeholders and their key performance indicators in RDF. ...
Article
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Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.
... Using KPIs to assess a building's performance is common and that is why some ontologies have been taken that into consideration. The first to be discussed was introduced by Corry et al. [137]. In it, ifcOWL, SimModel and SSN ontologies are reused to create an architecture that focuses on reducing the performance gap between the real and simulated data. ...
... An ontology-based architecture that focused on performance tracking at building and district levels was developed by Li et al. [139], and tested in a case study of a microgrid comprising 19 solar houses. This architecture consisted of the ifcOWL ontology, the SimModel ontology, for creating an XML-based building simulation model (to be used in EnergyPlus and OpenStudio), and the SSN ontology, which was used for semantically integrating sensor data [137][138][139]. In addition, an ontology-based architecture for building energy savings was proposed by Han et al. [140], which included the RDF schema, D2RQ ontology translator, OWLIM-RDF database and EnergyPlus as a simulation tool [141,142]. ...
Article
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The Architecture, Engineering and Construction (AEC) industry has been utilizing Decision Support Systems (DSSs) for a long time to support energy efficiency improvements in the different phases of a building’s life cycle. In this context, there has been a need for a proper means of exchanging and managing of different kinds of data (e.g., geospatial data, sensor data, 2D/3D models data, material data, schedules, regulatory, financial data) by different kinds of stakeholders and end users, i.e., planners, architects, engineers, property owners and managers. DSSs are used to support various processes inherent in the various building life cycle phases including planning, design, construction, operation and maintenance, retrofitting and demolishing. Such tools are in some cases based on established technologies such Building Information Models, Big Data analysis and other more advanced approaches, including Internet of Things applications and semantic web technologies. In this framework, semantic web technologies form the basis of a new technological paradigm, known as the knowledge graphs (KG), which is a powerful technique concerning the structured semantic representation of the elements of a building and their relationships, offering significant benefits for data exploitation in creating new knowledge. In this paper, a review of the main ontologies and applications that support the development of DSSs and decision making in the different phases of a building’s life cycle is conducted. Our aim is to present a thorough analysis of the state of the art and advancements in the field, to explore key constituents and methodologies, to highlight critical aspects and characteristics, to elaborate on critical thinking and considerations, and to evaluate potential impact of KG applications towards the decision-making processes associated with the energy transition in the built environment.
... As designed National building codes 6 Process energy 7 As designed As designed Renewable energy sources As designed As designed Most GBRTs only require performance simulations, such as the energy performance, at the design stage. Different studies highlighted that certified green buildings often suffer from a mismatch between the predicted and the actual energy performance, referred to as the "performance gap" (Carbon Trust, 2011;Dwyer, 2012;Corry et al., 2015). The performance gap can be caused by many reasons, such as inaccurate inputs of simulation assumptions, geometry modelling, internal loads, materials specifications, and more (Tarantino, 2020). ...
Thesis
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Sustainable construction is often used to describe buildings designed according to criteria embedded in green building rating tools (GBRTs) systems. GBRTs often tackle multi-criteria, such as energy, water, indoor environment, and materials performance of buildings, requiring experienced professionals to conduct various assessment processes and simulations. Traditional CAD tools often produce different versions of building models for sustainability assessment to perform various performance simulations using building performance simulation tools (BPS) for cost estimates, structural analysis, building energy and daylighting performance. The manual assessment is often considered expensive and challenging,requiring extensive time and effort and, in some cases, could lead to errors and redundant work. The sustainability assessment of buildings is data-driven and highly relies on available building information and tools capable of processing and augmenting design data from the initial design phase. The representation of project information in digital environments, such as Building Information Modelling (BIM), has shifted the industry towards a more efficient practice. BIM was adopted to facilitate green building assessment by integrating with BPS tools and providing data-rich models for various assessment processes. However, the currently developed approaches tackle a specific sustainability issue in a specific standard, such as LEED’s energy assessment. The existing literature concludes that currently, there is no comprehensive assessment tool that can streamline green building evaluation. This thesis aims to develop a BIM-based sustainability assessment tool that facilitates the assessment of green buildings. In this regard, we question the ability of BIM technology to provide the necessary means to automate the assessment of GBRTs. Hence, the integrated sustainability assessment tool (iSAT) was developed by first conducting a comparative analysis between selected GBRTs to highlight the maturity and comprehensiveness of the JGBG compared to others, which helped formulate a better understanding of the factors assessed, processes and complexity involved in the assessment process. Secondly, an algorithm was developed using an information technology approach to allow the integration of BIM data, BPS tools, and GBRTs to eliminate the complexity of processes involved and, thus, streamline the assessment process for the targeted criteria. The proposed tool was tested on two case studies designed and certified based on the JGBG. To conclude, BIM has shown a fundamental technical advancement over traditional CAD tools, allowing easier integration with BPS tools. However, not all GBRT criteria can be automated due to the nature of these criteria requiring professional body or expertise involvement. Nevertheless, the proposed tool can efficiently integrate BIM, BPS, and GBRTs, demonstrating that selected criteria are automatically assessed. Further efforts are still needed to overcome challenges while developing the tool, such as improving the quality of BIM-exported data models, developing a middleware tool to fix these files, and allowing for actual data inputs to improve the accuracy of BPS further.
... Recent developments in adjacent IoT-centric domains have highlighted the use of ontologies as a promising concept that can help align and integrate diverse data sources [2,11,14,15]. Ontologies provide a web-based and standardized schema to share, represent, and integrate diverse data sources for improved interoperability [16,17]. ...
Preprint
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Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved on average mean absolute errors of 0.40°C for temperature, 32 ppm for CO2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework's ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework's practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment.
... According to the study in Ref. [9], efficiently configured BACS can even save up to 30% on heating in an office building. Unfortunately, the current design and commissioning of control logic in BACS are still strongly dependent on textual descriptions, spreadsheets as well as two-dimensional drawings for information exchange and control programmers for manual programming [5,[10][11][12][13]. Such low-digital documentation of control logic is unstandardized as well as ambiguous, which needs to be further interpreted and left room for various implementations. ...
Article
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Faulty programming of control functions in Building Automation and Control Systems (BACS) might result in inefficient building operations. To reduce programming errors, an automated implementation process of control functions might be a promising solution. Recently, Building Information Modeling (BIM) contributes to digitizing building construction projects but is rarely used in the planning and implementation of control functions in BACS. The control description in BIM also remains unclear. Regarding these problems, a control documentation method for BIM and an automated control implementation approach can simplify control implementation in BACS and hence improve the building operation. In the previous work, we developed the MODI method for a structured planning process of mode-based control algorithms for building energy systems. This method showed the potential to support digitized control planning and implementation in BACS. Based on this, in this paper, we introduce a documentation method to report mode-based control algorithms in the industrial foundation class (IFC), enabling data sharing among BIM, and a software-assisted approach to automatically generate PLC codes for implementing these algorithms. The case study demonstrates the documentation of a desired mode-based control strategy for an energy supply network in IFC and the implementation of this strategy in a PLC program. In the simulation phase, we test the implemented control strategy to verify the functionalities of the PLC program. The results prove that mode-based control strategies can be fully automatically implemented in a PLC program based on IFC data.
... The study stated that it is usually more efficient to leave data such as performance measurements in their native format while new mechanism that automatically prepares data streams for processing by rule-based performance definitions should be proposed. More studies can be referred to Lorry et al. [13] and Hu et al.'s [14] work. In these studies, ontology models were constructed to transform data from heterogeneous sources into semantically enriched information. ...
Chapter
Buildings account for a large proportion of energy consumption, and improving building energy efficiency during the operation phase has attracted increasing research attention to achieve the carbon neutrality goal. Building energy simulation is a powerful tool to predict and manage building energy performance during the operation phase. Decision making can be informed by timely and reliable simulation results. However, building energy simulation requires various data and information including building geometries, thermal properties of constructions, Heating, Ventilation, and Air-conditioning (HVAC) systems, etc. Collecting these data and information from different sources (e.g., Building Information Modeling (BIM), Building Management Systems (BMS)) can be a tedious and time-consuming job, which limits a timely prediction and decision-making. This study proposes an ontology-based framework which can integrate data for building energy simulation from different data sources. Firstly, we collect and integrate four types of data (i.e., weather, building, internal heat gain, and HVAC system) from different sources. Ontology models are designed by integrating existing ontology models Brick Schema and Building Topology Ontology (BOT). An inference rule for thermal zoning is proposed. Secondly, a group of rooms in a campus building are selected as a case study to demonstrate the implementation of the models and the inference rule. The proposed models reduce the efforts needed to collect and integrate data for building energy simulation during the operation phase. Using the proposed model, data needed for building energy simulation can be obtained promptly and accurately, which strongly supports building energy management towards energy efficiency.
... Ontologies have also been widely applied to smart homes and building energy management. This enabled the computation of energy performance metrics and correlated variables, with a semantic aggregation of data (e.g., temperature and energy consumption readings) [93], system surveillance [94], and a meaningful building EnPI for each stakeholder [95]. Beyond building monitoring, modelling and control aspects have also been investigated in the literature [96,97]. ...
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Optimizing and predicting the energy consumption of industrial manufacturing can increase its cost efficiency. The interaction of different aspects and components is necessary. An overarching framework is currently still missing, and establishing such is the central research approach in this paper. This paper provides an overview of the current demands on the manufacturing industry from the perspective of digitalization and sustainability. On the basis of the developed fundamentals and parameters, a superordinate framework is proposed that allows the modelling and simulation of energy-specific properties on several product and process levels. A detailed description of the individual methods concludes this work and demonstrates their application potential in an industrial context. As a result, this integrated conceptual framework offers the possibility of optimizing the production system, in relation to different energy flexibility criteria.
... Ontology-based data management is a linked data approach mostly used to publish ontologies for construction SM, to ensure terms and concepts developed are machinereadable [34,35]. In the development process, the ontology uses linked data in which several statements are considered reasonable and are joined by applying the logical "AND" operator. ...
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The construction industry is one of the most dangerous industries in terms of safety performance, with practitioners and experts actively developing various solutions to reduce accident frequency and severity. However, accident information is collected in a wide range of formats by various elements in the construction industry, leading to interoperability issues and poor productivity due to the difficulties of sharing and reusing information. To improve the management of various types of safety management (SM) records in the construction industry, practitioners and researchers have adopted ontological methods. This paper summarizes the SM trends in construction management, along with gaps and opportunities for future work. A data processing framework is developed with a phase research for objective and subjective topic analysis from a collection of articles from 2012–2022 on topics relevant to the use of ontology in SM. The analysis focuses on the ontological life cycle (development, integration, and application), revealing an increasing trend of ontology-based SM (ObSM) research in the SM maintenance phase. Increasing case size and system automation is needed for future ontology-based SM optimization. The findings of the study will help to gain a thorough knowledge of ObSM, which will increase interest in effectiveness and the use of engineering and analytical techniques in SM.
... The digitalbuildings ontology [55] was built upon Haystack and Brick to semantically represent structured information about buildings and building-installed equipment at single building and portfolio level. The PerformanceAssessment ontology provides semantic approach to integrate heterogeneous building data for structured performance moni-toring and analysis [33] . BEMS [56] and NewOSEIM [57] are both application-oriented ontologies to support building energy management with consideration of occupants' preferences. ...
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Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: (1) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and (2) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.
... Daniele et al. [41] used a similar approach when developing a common ontology language for smart applications. Multiple researchers evaluate their ontology mainly based on competency questions (e.g., [32,35,42]). To ensure a fair evaluation, the Building Performance Ontology was evaluated against a predefined set of criteria, namely: ...
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Indoor environmental quality (IEQ) affects occupants’ satisfaction, health, productivity, comfort, and well-being. IoT developments enable better monitoring of IEQ parameters; however, integrating the various types of heterogeneous data from both the IoT and BIM domains is cumbersome and capital intensive, and therefore, limits the potential of smart buildings. Semantic web technologies can reduce heterogeneity issues, which is necessary to facilitate complex IEQ models. An ontology integrating data related to a building’s topology and its static and dynamic properties is still lacking. The outline of this research is twofold. First, a systematic literature review was conducted to find state-of-the-art semantic web technologies related to building topology, static properties, and dynamic properties from the IoT and BIM domains. By graphically reviewing various ontologies, their valuable patterns, commonalities, and best practices were revealed. Secondly, those results were used to develop a new ontology that integrates topological building information with static and dynamic properties. This Building Performance Ontology (BOP) provides a generic upper-level description of properties and two lower-level ontologies representing observations and actuation. The ontology results in intuitive queries and is both horizontally and vertically extensible. Multiple levels of detail are introduced to ensure practical applicability and efficient patterns based on the data modeler’s needs. BOP opens up a new range of research opportunities in the IEQ domain.
... In 2012, the AlexNet network was proposed to focus research in the field of computer vision on convolutional neural networks and deep learning. With the emergence of various advanced frameworks in 2014, improved AlexNet and ZFNet were introduced [31][32][33]. In the same year, RepVGG, which consists of only 3 × 3 convolutions with ReLU activation functions further enhances the feature extraction performance of the VGG network through a simple branchless structure [34][35][36]. ...
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With the continuous improvement in China’s economy, the construction industry has developed and rampantly progressed. Besides the wastage of resources and energy, the development has caused serious pollution to the environment. This makes the construction industry a high energy-consuming and highly polluting industry. There is a pressing need to reduce the wastage of resources and to adequately manage consumption of energy throughout the life cycle of buildings. This paper explores an effective method of building life cycle energy management by appropriately utilizing information system and the emerging deep learning technology. To achieve energy saving in buildings, a feasible model is proposed for predicting, analyzing, and building energy consumption based on neural networks. By analyzing the massive data stored in the building information system, the operation of each subsystem in the building is guided and regulated to achieve energy deployment and build energy optimization. Focusing the key meters, the average generalization ability of the proposed model (R-Squared = 1.9, MSE = 1.02) is better than the other contemporarily used models, LightGBM, LSTM, and SVR. Moreover, the method can effectively predict the energy consumption of the whole life cycle of the building and has higher prediction accuracy. The method proposed has great significance in research related with improving building energy performance and designing decision support tool.
... Related Work. Corry et al. (2015) proposed an ontology that receives data from building objects, sensors and simulation models and assesses that data in a structured way. That is, to use the ontology as a repository, or data integration tool. ...
... Zheng et al. concentrated on the information related to construction systems, connected to activities, agents, equipment, locations, building objects, and other participating entities, to develop an ontology focused on the digital construction workflow [18]. Corry et al. presented a performance assessment ontology and correspondent framework devoted to the energetic and environmental management of buildings, where the focus was the integration of relevant datasets, data integration and analysis [19]. Concerning renovation projects and related product installation activities, the Reno-Inst ontology by Amorocho and Hartmann [20] focused on the installation of windows, ETICS panels and radiators, which are considered for renovation. ...
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Intervention projects for historical buildings depend on the quality of multidisciplinary data sets; their collection, structure, and semantics. Building information model (BIM) based workflows for historical buildings accumulate some of the data sets in a shared information model that contains the building’s geometry assemblies with associated attributes (such as material). A BIM model of any building can be a source of data for different engineering assessments, for example, solar and wind exposure and seismic vulnerability, but for historic buildings it is particularly important for interventions like conservation, rehabilitation, and improvements such as refurbishment and retrofitting. When the BIM model is abstracted to a semantic model, enabling the use of semantic technologies such as reasoning and querying, semantic links can be established to other historical contexts. The semantic technologies help historic building experts to aggregate data into meaningful form. Ontologies provide them with an accurate knowledge representation of the concepts, relationships, and rules related to the historic building. In the paper, we are proposing an improved workflow for the transformation of a heritage BIM model to a semantic model. In the BIM part the workflow demonstrates how the fully parametric modelling of historical building components is relevant, for example, in terms of reusability and adaptation to a different context. In the semantic model part, ontology reuse, reasoning, and querying mechanisms are applied to validate the usability of the proposed workflow. The presented work will improve knowledge-sharing and reuse among stakeholders involved in historic building projects.
... With numerous companies involved in the supply and distribution of energy, data arise from a range of sources, for sharing across the sector. A common method in the energy sector is to use device ontologies, particularly Semantic Sensor Network ontology (Compton et al., 2012), to bring information together in a common format (Corry et al., 2015;Dey et al., 2015). An example for the purpose of smart energy management in buildings is provided by the Optimus ontology (Marinakis and Doukas, 2018). ...
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The creation and use of ontologies has become increasingly relevant for complex systems in recent years. This is because of the growing number of use cases that rely on real world integration of disparate systems; the need for semantic congruence across boundaries; and, the expectations of users for conceptual clarity within evolving domains or systems of interest. These needs are evident in most spheres of research involving complex systems but they are especially apparent in infrastructure and cities where traditionally siloed and sectoral approaches have dominated undermining the potential for integration to solve societal challenges such as net zero; resilience to climate change; equity and affordability. This paper reports on findings of a literature review on infrastructure and cities ontologies and puts forward some hypotheses inferred from the literature findings. The hypotheses are discussed with reference to literature and provide avenues for further research on (1) belief systems that underpin non top level ontologies and the potential for interference from them; (2) the need for a small number of top level ontologies and translation mechanisms between them; (3) clarity on the role of standards and information systems upon the adaptability and quality of datasets using ontologies. We also identify a gap in the extent ontologies can support more complex automated coupling and data transformation when dealing with different scales.
... A challenge in integrating IoT data to an FM-BIM is their heterogeneity. In general, FM-BIMs contain primarily semantic, geometric, and topographical (static) data, while IoT sensor data streams are timeseries (dynamic) in nature [5,8]. The most common integration technique for static and dynamic data is referred to as linked data [9,10,11]. ...
Preprint
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Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design team, rather than responsive to actual building performance. These are further limited by prescribed logic, possess only rudimentary visualizations, and lack broader system integration capabilities. Advances in machine learning, edge analytics, data management systems, and Facility Management-enabled Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted building management. This paper presents an integration technique for mapping the data from a building Internet of Things (IoT) sensor network to an FM-BIM. The sensor data naming convention and timeseries analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit timeseries data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study of an office living lab consisting of a local sensor network mimicking a BAS, which streams to a cloud server via a virtual private network connection. The resultant data structure and key visualizations are presented to demonstrate the value of this approach, which permits the end-user to select the desired timeframe for visualization and readily step through the spatio-temporal building performance data.
... This allows static and dynamic building data sources to be cross referenced through standardized semantic data representation. Different semantic data serialization formats exist within the building domain [5,6]. Some examples of semantic web serialization formats include Turtle (.ttl), N-Triples (.nt), JSON-LD (.jsnld) among others, including ontology specific custom formats. ...
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Enabling buildings with Smart Building applications will help to achieve the ongoing efficient commissioning of buildings, ultimately attaining peak performance in energy use and improved occupant health and comfort, at minimum cost. For these technologies to be scalable data ontology must be adopted to semantically represent data generated by building mechanical systems, acting as conduit for connection to Smart Building applications. The viability of Brick and Project Haystack ontologies, as found by industry and academia, prompted a quantitative comparison of completeness and expressiveness using a case study with an industry ontology as the baseline. Additionally, a qualitative comparison was completed using key ontology qualities outlined in literature. A recommendation of Brick is made based on results. Brick achieved higher assessment values in completeness and expressiveness achieving 59% and 100% respectively, as compared to Haystacks 43% and 96%. Additionally, Brick exhibited five of six desirable qualities, where Haystack exhibited only three. The recommendation of the appropriate ontology forms the basis for longer-term Smart Building application development, which will support innovative approaches to sustainability in building operations across scale, as well as next-generation building controls and automation strategies.
... In the field of AEC/O, several contributions stand out in this research line: the semantic enrichment of IFC files [14], the exchange of BIM information through Semantic Web tools such as Linked Data [15], and the development of ontologies for the BIM domain such as IFC OntoStep [16,17] and ifcOWL [18]. Along these lines, we find a number of contributions that use ontologies to facilitate the interoperability and the mapping of information from BIM models into different business processes, such as: energy management [12,19], emergency management [20], indoor navigation [21], urban management [22], event management [23], and performance evaluation [24], among others. However, for many areas there is still no standard for information exchange between BIM and semantic tools, with the result that several AEC/O sectors are not exploiting the full potential of Semantic Web [25]. ...
Article
Building Information Modeling (BIM) has revolutionized the construction industry as a platform for core integrated design, modeling, asset planning, and collaboration. Although BIM simplifies the retrieval and use of information in construction projects, BIM tools use native formats that pose challenges for data reuse and exchange. This article proposes a method for mapping BIM data into sets of concepts of a specific domains, and the use of Semantic Web tools for the exchange of data and information. Domain ontologies are a widely used tool for the formal definition of a field of knowledge, which facilitates the exchange of information in heterogeneous systems through technologies such as the Semantic Web. The proposed mapping can be used to enrich information and improve data integration in systems based on semantic tools that manage services or maintain facilities and building infrastructures. The article also presents a case study based on the management of airport facilities to illustrate the practical application of the method.
... As noted by (Ding et al., 2016), an ontology can offer three main benefits in knowledge modelling and management: 1) improve model flexibility and extendibility; 2) provide a robust semantic representation; and 3) enhance knowledge retrieval by improving the retrieval requests from the concept level. Consequently, several researchers have adopted ontology and Linked Data in various applications for the AECO sector, such as cross-domain information integration for Building Information Modelling (BIM) open standards (Torma, 2015) , cost estimation (Abanda,Kamsu-Foguem and Tah, 2017), manufacturing , asset management , energy management (Corry et al., 2015, Tomašević et al., 2015, Look ahead planning (Soman,Molina-Solana and Whyte, 2020) and crowd simulation (Boje, 2019). ...
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A pronounced gap often exists between expected and actual safety performance in the construction industry. The multifaceted causes of this performance gap are resulting from the misalignment between design assumptions and actual construction processes that take place on-site. In general, critical factors are rooted in the lack of interoperability around the building and work-environment information due to its heterogeneous nature. To overcome the interoperability challenge in safety management, this paper represents the development of an ontological model consisting of terms and relationships between these terms, creating a conceptual information model for construction safety management and linking that ontology to IfcOWL. The developed ontology, named Safety and Health Exchange (SHE), comprises eight concepts and their relationships required to identify and manage safety risks in the design and planning stages. The main concepts of the developed ontology are identified based on reviewing accident cases from 165 Reporting of Injuries, Diseases and Dangerous Occurrences Regulations (RIDDOR) and 31 Press Releases from the database of the Health and Safety Executive (HSE) in the United Kingdom. Consequently, a semantic mapping between the developed ontology and IfcOWL (the most popular ontology and schema for interoperability in the AEC sector) is proposed. Then several SPARQL queries were developed and implemented to evaluate the semantic consistency of the developed ontology and the cross-mapping. The proposed ontology and cross-mapping gained recognition for its innovation in utilising OpenBIM and won the BuildingSMART professional research award 2020. This work could facilitate developing a knowledge-based system in the BIM environment to assist designers in addressing health and safety issues during the design and planning phases in the construction sector.
... For instance, with the alignments between five domain ontologies in [7], the BOT, BRICK, ifcOWL are included. Regarding the application of ontologies, Corry et al. [8] developed a lifecycle performance framework to manage the buildings in the aspect of energy using ifcOWL, Simulation Domain Model ontology [9] and Semantic Sensor Network ontology [10]. ...
Conference Paper
The use of Building Information Modeling (BIM) for the planning and construction of infrastructures has increased significantly in recent years. However, there is currently a lack of flexible concepts for the management and use of BIM models for asset management, considering existing systems and processes. A major challenge is how the BIM models can be continuously enriched with information on the most current condition. In this paper, a concept for applying BIM models for bridge inspection and the subsequent integration of the results into existing asset management systems is presented. The information for inspection and condition assessment is provided as an information container according to ISO 21597. Semantic Web technologies are used to describe the information for the structural components, the structural conditions, and the detected damages of a bridge using existing ontologies. An illustrative example demonstrates how BIM models are enriched in the context of inspection and how the data can be transferred into existing asset management systems.
... Particularly, the Semantic Sensor Data (SSN) ontology [32] is the recommendation of the World Wide Web Consortium for describing sensors and their observations. The SSN ontology has been previously used for representing energy meter data and performance indicators [33], [34], and can be related to DABGEO [35], an upper-level ontology for energy concepts [36]. Furthermore, sensor data models can be linked to other building data models, thus facilitating data integration and information retrieval. ...
Article
The enormous quantity of data handled by Building management systems are key to develop more efficient energy operational systems. However, the inability of current systems to take benefit from the generated data may waste good opportunities of improving building performance. Big Data appears as a suitable framework to sustain the management system and conduct future prospective analysis. In this work we present a Big Data based architecture for the efficient management of buildings. The different Big Data components are involved not only in the data acquisition phase, but also in the implementation of algorithms capable of analysing massive data collected from very heterogeneous sources. They also enable fast computations that can help the generation of optimal operational plan generations to improve the building functioning. The proposed architecture has been effectively introduced in four different-purpose buildings, demonstrating that Big Data can help during the energy cycle of the building.
... There has been extensive research in the last decade which has focused on applying semantic descriptions of BA to AFDD [12], specifically for energy optimization [5,9]. [10] surveys approaches over a decade and classifies the methods as based on quantitative models, qualitative models, or process history. ...
... However, synergistic potentials between the technical developments and organizational dimensions exist. Though the capability of the systems discussed above for building performance and energy management are well-established, existing work highlight the gap between the lack of a feedback loop from in-use performance to influence future design (De Wilde, 2014;Corry et al., 2015;Gerrish et al., 2017;Shiel et al., 2018). Oti et al. (2016) proposed a BIM-enabled framework that can be used to provide feedback from building energy consumption to improve the future design and FM. ...
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Purpose This paper aims to present a review of research developments relating to the application of building information modeling (BIM) to facilities management (FM). It sheds light on major technical and organizational issues with the view of identifying how existing BIM for FM knowledge applies to large capital projects. Design/methodology/approach The study adopted a literature search approach to retrieve relevant articles which were subjected to keyword analysis to enable categorization of extant BIM for FM research into appropriate sub-domains. A qualitative analysis of 94 BIM for FM-related literature was carried out in addition to a review of 9 capital project-related articles, leading to the establishment of research trends, gaps and future directions. Findings The review found that research in the BIM-FM integration field is predominantly technology and process-oriented, with less attention paid to people or organizational aspects. Therefore, there is a need for expanding the knowledge base in this direction. Several future research directions were identified to lay the foundations for research on BIM application to FM in large capital projects and other application areas for interested researchers. These future directions were categorized under the identified sub-domains of the field and mapped onto two generic activities, i.e. technical integration and business integration, involved in technology adoption by organizations. Originality/value The main contribution of this study is the categorization of existing research on BIM for FM, leading to the identification of research gaps concerning the initiation and implementation of BIM for FM in large capital projects. As a secondary benefit, this study has validated some sub-domains of the BIM for the FM research field identified in previous review papers using an empirical approach. This validation of defined sub-domains is useful for an emerging research field as it provides a common understanding of trends and specific application areas.
... Moreover, the AI system achieves a balanced condition between energy consumption and energy production system's performance parameters [23], adapting itself to the environment in order to achieve the predefined objectives. ...
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The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.
... Sørensen et al. reviewed the existing ontologies relevant to creating digital links between virtual models and physical components in the construction process to improve information handling and sharing in construction and building operation management (Sørensen et al., 2010). Corry et al. proposed a semantic-based approach to integrating heterogeneous building data (Corry et al., 2015a). Semantic web technologies have been used in environmental monitoring to facilitate knowledge encoding and data integration outside the construction environment (Patton et al., 2014). ...
Article
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Buildings consume a large proportion of global primary energy and building performance management requires massive data inputs. Key Performance Indicator (KPI) is a tool used for comparing different buildings while avoiding problems caused by heterogeneous data sources. However, silos of building and energy consumption data are separate, and the linkages between a KPI formula and different data sets are often non-existent. This paper develops an ontology-based approach for automatically calculating the KPI to support building energy evaluation. The proposed approach integrates building information from BIM and energy and environmental information collected by sensor networks. A KPI ontology is developed to establish a KPI formula, thereby linking static and dynamic data generated in the building operation phase. Each KPI can be defined by inputs, a formula and outputs, and the formula consists of parameters and operators. The parameters can be linked to building data or transformed into a SPARQL query. A case study is investigated based on the proposed approach, and the KPIs for energy and environment are calculated for a real building project. The result shows that this approach relates the KPI formula to the data generated in the building operation phase and can automatically give the result after defining the space and time of interest, thus supporting building performance benchmarking with massive data sets at different levels of details. This research proposes a novel approach to integrating the KPI formula and linked building data from a semantic perspective, and other researchers can use this approach as a foundation for linking data from different sources and computational methods such as formula created for building performance evaluation.
... In order to enable an automated and easy-to-use process for evaluating building energy performance, Corry et al. defined a performance assessment ontology to analyse building energy performance [36]. This ontology connects existing RDF data silos including ifcOWL, and SSN instances. ...
Article
Cross-domain information is essential for building energy performance assessment. The heterogeneous nature of this information is a major source for inefficient assessments. The semantic web provides a flexible pathway for addressing recognised interoperability issues. However, further implicit knowledge in cross-domain information could provide meaningful solutions for such assessments. This paper aims to develop a conceptual framework that links cross-domain information, infers implicit knowledge, and empowers building managers with insightful assessments. The framework integrates Web Ontology Language (OWL) ontologies, Resource Description Framework (RDF) instances, and a set of predefined rules to infer implicit knowledge, which can satisfy data requirements of performance metrics and enable meaningful performance assessments. Then building managers can identify inefficient building operations and improve energy efficiency while maintaining desired building functions. This approach reduces burdensome intervention from the managers when compared with traditional solutions. A demonstration highlights the engineering value by evaluating energy performance of a university building.
... As mentioned in the introduction, building data interoperability [17] using common data exchange formats is necessary to increase the digitalisation and automation of buildings. The use of semantic web technologies [271] and standards based on IFC could support not only design but also operation (e.g., energy and environmental monitoring) [272], employing "surrogate" modelling strategies (physical/statistical, "grey-box") [209] compatible with the above mentioned principles. Finally, as introduced in Section 2, the research presented in this paper is part of a broader investigation, focused on the concept of "Buildings-as-Energy-Service": new forms of knowledge integration are needed to develop innovative services and products that can work as "ecosystems" and exploit this concept. ...
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Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.
Article
Purpose Despite extensive research on the underlying reasons for the energy performance gap in buildings, there is a critical need for stakeholders to standardize and facilitate the use of this knowledge and support its broader application by machines. Our research addresses this gap by developing both an ontology and a tool to utilize risk information regarding the performance gap in buildings. Design/methodology/approach Research into this topic began with the creation of an energy performance gap-risk ontology for new and existing buildings using the METHONTOLOGY method. This comprised a comprehensive literature review and semi-structured interviews with ten experts concerning six buildings, in order to develop taxonomies and define risk factor interactions. It was followed by a three-stage validation using a mixed-method research methodology. Steps included comparing the ontology with a similar empirical study, gathering expert opinions via interviews and ratings assessments, and finally, interviewing an experienced professional to ascertain whether there were any concepts not covered by the ontology. The taxonomies were modeled in Protégé 5.5, and using the ontology, a spreadsheet tool was developed using Microsoft Visual Basic for Applications in Excel. Findings The ontology identified 36 primary risk factors and a total of 95 when including additional risks linked to certain factors. Factors such as professional liability insurance, stakeholder motivation, effective communication, experience, training, integrated design, simplicity of detailing, building systems or design and project commissioning can help manage the performance gap in buildings. The tool developed serves as a decision-support system, offering features like project risk checklists to assist stakeholders in addressing the performance gap. Originality/value This study is the first to develop an energy performance gap-risk ontology and a tool to help project stakeholders collect, store and share building risk information.
Conference Paper
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Among the various studies that have addressed the application of BIM dynamics in domain of health and safety management, the lack of research regarding the impact of BIM dynamics on safety culture is clearly visible. These days, it is apparent that more and more construction technologies are currently being used for safety and health management. These technologies can be used in different construction applications to mitigate workplace hazards. Among these technologies, BIM and IoT has been shown to have significant potential in high-risk Environment, Health, and Safety (EHS) industries. Several researchers have started to explore the potential synergy between BIM and IOT. Dynamic BIM presents an influential pattern for applications to improve construction safety management. The main purpose of this paper is to demonstrate the necessity of research on the use of dynamic BIM in improving construction safety culture. Hence, the literature review method was used to identify the domains in which dynamic BIM has been applied. Prominent application domains in which dynamic BIM has been applied are construction operation and monitoring, facility management (FM), construction logistic and management, health and safety (H&S) management. Results show that no research has addressed the impact of dynamic BIM on safety culture. Therefore, research related to dynamic BIM and its impact on construction safety culture is necessary.
Thesis
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Urbanization poses a significant challenge in the 21st century. Currently, more than half of the global population resides in urban areas, and this percentage is projected to reach 68% by 2050. The increase in urban population has led to a substantial rise in residential energy consumption, alongside a surge in commercial energy use to meet the growing demand for services. Consequently, overall building energy consumption has witnessed a significant increase. Therefore, effectively managing energy use in urban buildings has become imperative. To achieve this goal, various methodologies and tools for urban building energy modeling have been developed. These models offer valuable insights into the energy demands of building stock, covering benchmarking analysis, scenario assessments, peak load evaluations, energy pattern analysis, and other specialized analyses. Despite extensive research in the field of energy modeling, assessing urban energy remains complex due to three significant challenges. Firstly, urban building simulation involves various aspects such as geography, construction, materials, and HVAC (Heating, Ventilation, and Air Conditioning) systems, each of which is stored in its own unique data model. As a result, creating text-based simulation files for urban buildings from scratch is an intricate task which requires the integration and processing of cross-domain data models. Secondly, conventional simulation models rely on climate conditions provided by a limited number of weather stations, which do not accurately capture the microclimate variations caused by urban morphologies, natural conditions, and man-made structures. This limitation results in unrealistic and unreliable simulation outputs, further hindering effective decision-making for urban sustainability. Lastly, previous efforts have primarily focused on complex physical conditions within cities but have often encountered challenges such as intricate modeling and substantial computational loads. To address these gaps, this dissertation proposes a system architecture for urban building energy distributed simulation. The first aspect involves designing ontologies using semantic network technology, grounded in the features of building energy simulation inputs, clearly defining the potential logical relationships between the inputs, and facilitating the generation of qualified simulation files. Additionally, the concept of UrbanPatch, which represents the microclimate perception domain of urban buildings, is introduced. By analyzing the building morphology and green spaces within each UrbanPatch, a microclimate tuning approach is proposed to localize weather conditions for buildings. Finally, a rapid simulation approach is created, which decomposes the city model into spatially correlated building blocks for distributed simulation. The proposed algorithm, known as distributed adjacency blocks (DABs), uses 2D footprints to construct 3D building groups and considers solar azimuth angles, altitude angles, and shading planes to simplify the simulation targets. Using multiple threads and abstracted inter-building boundary conditions, the energy dynamics of an entire city can be simulated in parallel. The innovative system architecture for urban building energy distributed simulation proposed in this dissertation offers a novel solution that prompts researchers to reconsider the traditional bottom-up approach towards city-scale energy simulation. Centered around distributed building networks, this dissertation not only distributes the computational load across multiple computing components, enabling dynamic energy simulations for extensive metropolitan areas, but also accounts for the influence of microclimate on building energy consumption in the urban built environment, resulting in more precise and reliable simulation outcomes and enhancing the efficiency of city energy decision-making and management.
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Preprint
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Pre-print as published on https://como.ceb.cam.ac.uk/preprints/270/.
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This paper presents a Simulation Domain Model (SimModel) -a new interoperable XML-based data model for the building simulation domain. SimModel provides a consistent data model across all aspects of the building simulation process, thus preventing information loss. The model accounts for new simulation tool architectures, existing and future systems, components and features. In addition, it is a multi-representation model that enables integrated geometric and MEP simulation configuration data. The SimModel objects ontology moves away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with Industry Foundation Classes (IFC). The first implementation of SimModel supports translations from IDD, Open Studio IDD, gbXML and IFC. In addition, the EnergyPlus Graphic User Interface (GUI) employs SimModel as its internal data model. Ultimately, SimModel will form the basis for a new IFC Model View Definition (MVD) that will enable data exchange from HVAC Design applications to Energy Analysis applications. Extensions to SimModel could easily support other data formats and simulations (e.g. Radiance, COMFEN, etc.).
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Since our last report (Mills et al. 2004) the database has grown from 224 to 643 buildings (all located in the United States, and spanning 26 states), from 30 to 100 million square feet of floorspace, and from 17millionto17 million to 43 million in commissioning expenditures. The recorded cases of new-construction commissioning took place in buildings representing 2.2billionintotalconstructioncosts(upfrom1.5billion).Theworkofmanymorecommissioningproviders(18versus37)isrepresentedinthisstudy,asismoreevidenceofenergyandpeakpowersavingsaswellascosteffectiveness.Wenowtranslatetheseimpactsintoavoidedgreenhousegasesandprovidenewindicatorsofcosteffectiveness.Wealsodrawattentiontothespecificchallengesandopportunitiesforhightechfacilitiessuchaslabs,cleanrooms,datacenters,andhealthcarefacilities.Theresultsarecompelling.Wedevelopedanarrayofbenchmarksforcharacterizingprojectperformanceandcosteffectiveness.Themediannormalizedcosttodelivercommissioningwas2.2 billion in total construction costs (up from 1.5 billion). The work of many more commissioning providers (18 versus 37) is represented in this study, as is more evidence of energy and peak-power savings as well as cost-effectiveness. We now translate these impacts into avoided greenhouse gases and provide new indicators of cost-effectiveness. We also draw attention to the specific challenges and opportunities for high-tech facilities such as labs, cleanrooms, data centers, and healthcare facilities. The results are compelling. We developed an array of benchmarks for characterizing project performance and cost-effectiveness. The median normalized cost to deliver commissioning was 0.30/ft2 for existing buildings and 1.16/ft2fornewconstruction(or0.41.16/ft2 for new construction (or 0.4% of the overall construction cost). The commissioning projects for which data are available revealed over 10,000 energy-related problems, resulting in 16% median whole-building energy savings in existing buildings and 13% in new construction, with payback time of 1.1 years and 4.2 years, respectively. In terms of other cost-benefit indicators, median benefit-cost ratios of 4.5 and 1.1, and cash-on-cash returns of 91% and 23% were attained for existing and new buildings, respectively. High-tech buildings were particularly cost-effective, and saved higher amounts of energy due to their energy-intensiveness. Projects with a comprehensive approach to commissioning attained nearly twice the overall median level of savings and five-times the savings of the least-thorough projects. It is noteworthy that virtually all existing building projects were cost-effective by each metric (0.4 years for the upper quartile and 2.4 years for the lower quartile), as were the majority of new-construction projects (1.5 years and 10.8 years, respectively). We also found high cost-effectiveness for each specific measure for which we have data. Contrary to a common perception, cost-effectiveness is often achieved even in smaller buildings. Thanks to energy savings valued more than the cost of the commissioning process, associated reductions in greenhouse gas emissions come at 'negative' cost. In fact, the median cost of conserved carbon is negative - -110 per tonne for existing buildings and -25/tonnefornewconstructionascomparedwithmarketpricesforcarbontradingandoffsetsinthe+25/tonne for new construction - as compared with market prices for carbon trading and offsets in the +10 to +$30/tonne range. Further enhancing the value of commissioning, its non-energy benefits surpass those of most other energy-management practices. Significant first-cost savings (e.g., through right-sizing of heating and cooling equipment) routinely offset at least a portion of commissioning costs - fully in some cases. When accounting for these benefits, the net median commissioning project cost was reduced by 49% on average, while in many cases they exceeded the direct value of the energy savings. Commissioning also improves worker comfort, mitigates indoor air quality problems, increases the competence of in-house staff, plus a host of other non-energy benefits. These findings demonstrate that commissioning is arguably the single-most cost-effective strategy for reducing energy, costs, and greenhouse gas emissions in buildings today. Energy savings tend to persist well over at least a 3- to 5-year timeframe, but data over longer time horizons are not available. It is thus important to 'Trust but Verify,' and indeed the field is moving towards a monitoring-based paradigm in which instrumentation is used not only to confirm savings, but to identify opportunities that would otherwise go undetected. On balance, we view the findings here as conservative, in the sense that they underestimate the actual performance of projects when all costs and benefits are considered. They underestimate the technical potential for a scenario in which best practices are applied.
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Commissioning is arguably the single most cost-effective strategy for reducing energy, costs, and greenhouse gas emissions in buildings today. Although commissioning has earned increased recognition in recent years, it remains an enigmatic practice whose visibility severely lags its potential. The application of commissioning to new buildings ensures that they deliver or exceed the performance and energy savings promised by their design and intended operation. When applied to existing buildings, commissioning identifies deficiencies and the almost inevitable “drift” from intended performance over time, and carries out interventions to put the building back on course. More formally, commissioning is a systematic, forensic approach to quality assurance and performance risk management, rather than a technology per se. This article presents the world’s largest compilation and meta-analysis of commissioning experience and the associated literature, comprising 643 non-residential buildings, 99 million ft2 of floorspace, 43 million in commissioning expenditures, and the work of 37 commissioning providers. The median normalized cost to deliver commissioning is43 million in commissioning expenditures, and the work of 37 commissioning providers. The median normalized cost to deliver commissioning is 0.30/ft2 (2009 currencies) for existing buildings and2009 currencies) for existing buildings and 1.16/ft2 for new construction (or 0.4% of the overall construction cost). The one third of projects for which data are available reveal over 10,000 energy-related deficiencies, the correction of which resulted in 16% median whole-building energy savings in existing buildings and 13% in new construction, with payback times of 1.1 and 4.2years, respectively. Because energy savings exceed commissioning costs, the associated reductions in greenhouse gas emissions come at a “negative” cost of −110/tonneCO2fornewbuildingsand110/tonne CO2 for new buildings and −110/tonne CO2 for new buildings and −25/tonne for new construction. Cases with comprehensive commissioning attained nearly twice the overall median level of savings and five times the savings of the least-thorough projects. Significant non-energy benefits such as improved indoor air quality are also achieved. Applying the median whole-building energy-saving values to the US non-residential buildings stock corresponds to an annual energy-saving potential of $30 billion (and 340Mt of CO2) by the year 2030. KeywordsEnergy efficiency–Commissioning–Commercial buildings–Risk management–Quality assurance–Carbon reductions–Savings persistence
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