Article
To read the full-text of this research, you can request a copy directly from the authors.

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.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... 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 Furthermore, systems and buildings components information is often outdated, incomplete, and inaccurate when exchanged along the assets' life cycles (e.g., from design and construction to operations and management) (O'Donnell et al.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
Full-text available
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
Full-text available
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.
... External conditions [50], [14], [21], [51], [52], [53], [54], [49], [55], [35], [56], [57], [58], [59], [60] Indoor conditions [61], [62], [63], [50], [14], [48], [24], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70], [60] Building systems/components [61], [62], [63], [50], [14], [24], [21], [47], [51], [64], [15], [65], [54], [49], [55], [35], [56], [29], [57], [58], [66], [68], [59], [69], [70] Energy use [62], [50], [14], [48], [24], [21], [51], [52], [53], [65], [54], [49], [55], [35], [56], [29], [58], [66], [59], [69], [60] Maintenance [53], [65], [54], [68], [69], [70] Occupant-related data [62], [14], [48], [24], [21], [51], [52], [53], [65], [49], [55], [57], [68], [59], [70], [60] Physical building information [61], [62], [14], [48], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70] Performance-based data [48], [21], [47], [51], [64], [53], [58], [59], [69], [70], [60] Simulation-based data [62], [63], [48], [47], [51], [55], [68], [59] 1 ...
... External conditions [50], [14], [21], [51], [52], [53], [54], [49], [55], [35], [56], [57], [58], [59], [60] Indoor conditions [61], [62], [63], [50], [14], [48], [24], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70], [60] Building systems/components [61], [62], [63], [50], [14], [24], [21], [47], [51], [64], [15], [65], [54], [49], [55], [35], [56], [29], [57], [58], [66], [68], [59], [69], [70] Energy use [62], [50], [14], [48], [24], [21], [51], [52], [53], [65], [54], [49], [55], [35], [56], [29], [58], [66], [59], [69], [60] Maintenance [53], [65], [54], [68], [69], [70] Occupant-related data [62], [14], [48], [24], [21], [51], [52], [53], [65], [49], [55], [57], [68], [59], [70], [60] Physical building information [61], [62], [14], [48], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70] Performance-based data [48], [21], [47], [51], [64], [53], [58], [59], [69], [70], [60] Simulation-based data [62], [63], [48], [47], [51], [55], [68], [59] 1 ...
... External conditions [50], [14], [21], [51], [52], [53], [54], [49], [55], [35], [56], [57], [58], [59], [60] Indoor conditions [61], [62], [63], [50], [14], [48], [24], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70], [60] Building systems/components [61], [62], [63], [50], [14], [24], [21], [47], [51], [64], [15], [65], [54], [49], [55], [35], [56], [29], [57], [58], [66], [68], [59], [69], [70] Energy use [62], [50], [14], [48], [24], [21], [51], [52], [53], [65], [54], [49], [55], [35], [56], [29], [58], [66], [59], [69], [60] Maintenance [53], [65], [54], [68], [69], [70] Occupant-related data [62], [14], [48], [24], [21], [51], [52], [53], [65], [49], [55], [57], [68], [59], [70], [60] Physical building information [61], [62], [14], [48], [21], [47], [51], [64], [52], [15], [65], [54], [49], [37], [55], [35], [56], [29], [57], [58], [15], [66], [67], [68], [59], [69], [70] Performance-based data [48], [21], [47], [51], [64], [53], [58], [59], [69], [70], [60] Simulation-based data [62], [63], [48], [47], [51], [55], [68], [59] 1 ...
Article
Smart and ongoing commissioning (SOCx) of buildings can result in a significant reduction in the gap between design and operational performance. Ontologies play an important role in SOCx as they facilitate data readability and reasoning by machines. A better understanding of ontologies is required in order to develop and incorporate them in SOCx. This paper critically reviews the state-of-the-art research on building data ontologies since 2014 within the SOCx 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 SOCx and insight on how such approaches should be evaluated. Based on these findings, this study provides recommendations for future necessary research including: identification of SOCx-related data types, assessment of ontology performance, and creation of open-source approaches.
... By recognizing the potentials of formal ontologies and semantic web technologies for integrating distributed sources of data/information, researchers have been investigating such approaches in the context of the built environment research. Examples of such efforts can be found in [16][17][18]. However, investigating such approaches for addressing BIM-IoT data integration in particular, is limited within the existing body of knowledge. ...
... The application of a common (shared) ontology as a means for integrating distributed sources of data about buildings have been investigated previously. In [16], for instance, researchers developed a new ontology for integrating the data coming from three main sources, namely BIM-based, sensor-based, simulation-based data (represented in three local ontologies). The main theme of the mentioned study can be viewed as a BIM-IoT integrated scenario as the research objective has been to evaluate the gap between the design of the buildings and their actual performance by linking real-world observations (derived from sensors) and the simulated data about the building performance. ...
... Hence, an ontology-based mediation module is established for each local ontology-based schem. Similar to the approaches taken in [16,18], semantic correspondences between local ontology schemas and the common (shared) ontology can be formally defined. Finally, with these semantic correspondences in place, the integrated access to the federated RDF data sources can be provided using the query-rewriting patterns presented in [23]. ...
Chapter
Building Information Modeling (BIM) and Internet of Things (IoT) are two emergent technologies that promise substantial improvements in the lifecycle management of facilities. Recently, research on BIM-centered IoT applications has been gaining an increasing momentum. Currently, there exist some major challenges that impede the realization of the synergistic potentials of BIM and IoT. Among them is to maintain an effective semantic interoperability between the BIM and IoT data ecosystems. Indeed, an accurate integration between IoT-enabled raw sensory data and existing BIM-based digital models of facilities requires a profound understanding of contextual clues (time, location, occupants’ profiles, etc.), which are embedded within actual observations derived from IoT sensors. In this paper, a framework is presented for integrating disparate sources of BIM and IoT data through an ontology-based mediation mechanism. This framework enables an integrated access to local sources of BIM and IoT data thorough query-rewriting processes. In order to demonstrate the applicability of the proposed framework, query rewriting examples have been provided in the context of indoor comfort analysis for facility occupants. In the case demonstrations, the geospatial data acts as the main clue for the derivation of semantic dependencies between local instances of BIM and IoT data.
... 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
Full-text available
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
Full-text available
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.
... Manufacturer's product descriptions required to conduct building performance analysis can be readily accessible, usable and searchable on the web with the unifying language of semantic web and linked data (Pauwels et al., 2017). Data stemming from both the building and manufacturing domain can be described and linked homogeneously in RDF, using the RDF triples subject-predicate-object (Corry et al., 2015). This opens an opportunity for designers to explore possible design alternatives to optimize building performances. ...
... Support building performance analysis and optimization: Shayeganfar et al. (2013) indicates the gap between real world product description and the way they are abstracted in BIM tools. This gap created discrepancy between the real performance of a building and the simulated performance of a building model (Corry et al., 2015). Shayeganfar et al. (2013) continues to explain the potential of the semantic web technologies to bridge this gap given the fulfilment of two prominent components. ...
... 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. ...
Article
Full-text available
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. ...
Article
Full-text available
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: ...
Article
Full-text available
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]. ...
Article
Full-text available
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. ...
Article
Full-text available
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). ...
Article
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
Full-text available
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. ...
Preprint
Full-text available
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.
... 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). ...
Article
Full-text available
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. ...
Article
Full-text available
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. ...
Article
Full-text available
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
Full-text available
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. ...
Article
Full-text available
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.
... As a consequence, the above restrictions for FM systems, the necessity of usage data which comes from design and construction stage, heterogeneous data, IoT related issues, and storage issues in BIM lead to the consideration of the integration of BIM, Building Energy Performance Simulations and BDA within the study [32,33]. Figure 1 shows the scope of the FM data management framework required in FM. ...
Article
Full-text available
An increase in the usage of information and communication technologies (ICT) and the Internet of Things (IoT) in Facility Management (FM) induces a huge data stack. Even though these data bring opportunities such as cost savings, time savings, increase in user comfort, space optimization, energy savings, inventory management, etc., these data sources cannot be managed and manipulated effectively to increase efficiency at the FM stage. In addition to data management issues, FM practices, or developed solutions, need to be supported with the implementation of lean management philosophy to reveal organizational and managerial wastes. In the literature, some researchers performed studies about awareness about building information modeling (BIM)-FM, and FM-related data management problems in terms of lean philosophy. However, the comprehensive solution for effective FM has not been investigated with the application of lean management philosophy yet. Therefore, this study aims to develop an FM framework for healthcare facilities by considering lean management philosophy since more stable workflow, continuous improvement, and creating more value to customers will help to deliver a more acceptable solution for the FM industry. Within this context, the integration of BIM, Building Energy Performance Simulations, and Big Data Analytics are proposed as a solution. In the study, the Design Science Research (DSR) methodology was followed to develop the FM framework. Depending on the DSR methodology, two scenarios were used to investigate the issue in a real healthcare facility and develop the FM framework. The developed framework was evaluated by four experts, and the revisions of the proposed framework were realized.
... With the widespread use of sensory media, the expansion of wireless internet networks and the development of increasingly smart robotic systems, along with the growing capacity of computers at a lower cost, the problems arising from big data (Ford, 2015) are expected to be overcome, thus transforming the way that goods are produced in Europe and in the rest of the world (Corry et al., 2015;European Parliament, 2015) From a holistic perspective, the I4.0 concept will incorporate many other concepts, which are sometimes difficult to describe individually, such as the concept of a smart object (Heidari et al., 2014) or the sensory network associated with products and means of production, integrated in CPSs, and sending, receiving and processing information, making autonomous decisions based on digitalisation and previous simulations of the product models (Stock and Seliger, 2016). ...
... (1) An immediate application of our NLP model is the development of a text classification module as an add-in to existing building service systems. The NLP module can be also used to web-based building management systems, for example, to detect building performance deficits [49]. (2) Our NLP model can be integrated into the emerging Building Information Modeling-Facility Management (BIM-FM) system which underlines the value of human request responses for advancing BIM-FM system [50], and our NLP model provides such a text-data processing module that connects building users with BIM-FM. ...
... Figure 10 shows a high-level conceptual depiction of the possible interoperability among different buildings that share the same ontology. Because of the hierarchy behind the abstract schema, changes by AI on the abstract model can simply be disseminated to various connected systems and translated using the respective operational conventions of each building system (Corry et al., 2015). The use of ontologies may eliminate the difficulties restricting smooth interoperability between BMSs. ...
Article
Full-text available
In buildings, one or a combination of systems (e.g., central HVAC system, ceiling fan, desk fan, personal heater, and foot warmer) are often responsible for providing thermal comfort to the occupants. While thermal comfort has been shown to differ from person to person and vary over time, these systems are often operated based on prefixed setpoints and schedule of operations or at the request/routine of each individual. This leads to occupants’ discomfort and energy wastes. To enable the improvements in both comfort and energy efficiency autonomously, in this paper, we describe the necessity of an integrated system of sensors (e.g., wearable sensors/infrared sensors), infrastructure for enabling system interoperability, learning and control algorithms, and actuators (e.g., HVAC system setpoints, ceiling fans) to work under a governing central intelligent system. To assist readers with little to no exposure to artificial intelligence (AI), we describe the fundamentals of an intelligent entity (rational agent) and components of its problem-solving process (i.e., search algorithms, logic inference, and machine learning) and provide examples from the literature. We then discuss the current application of intelligent personal thermal comfort systems in buildings based on a comprehensive review of the literature. We finally describe future directions for enabling application of fully automated systems to provide comfort in an efficient manner. It is apparent that improvements in all aspects of an intelligent system are be needed to better ascertain the correct combination of systems to activate and for how long to increase the overall efficiency of the system and improve comfort.
... Energy efficiency becomes an important consideration as early as the schematic design phase of a project, but the real energy consumption data is not available until the operation phase starts. Even though many software applications can perform energy simulation and analysis, there is always a deficit between design intent and real energy performance (Corry et al., 2015, De wilde, 2014, Shiel et al., 2018. Collecting real energy consumption data is critical to provide feedback for future design and simulation (De wilde, 2014, Shiel et al., 2018. ...
... Thus, the quantity of information is an issue to be addressed. Corry et al. [178] proposed an ontology to address the information from various buildings. Buildings are heterogeneous, and building data can be as well. ...
Article
This paper aimed to review the literature of the past ten years about the energy performance of buildings during their operational stage. The focus of this review was empirical works that examined the energy use in real buildings. An overview of the literature survey is presented. A meta-analysis technique allowed the identification of two approaches of study: building-level analysis and stock-level analysis. The building-level analysis considers the building as the system of study. Otherwise, the stock-level analysis considers a group of buildings as the subject of study while the buildings are elements inside the system. Notable research topics were addressed involving performance gap, energy audit, retrofit savings assessment, Zero Energy Buildings (ZEB), benchmarking, regulations and strategies to overcome climate change. This literature review summarised the level of information of the studies by listing the granularity of the energy performance data according to the purpose of the study. Furthermore, a specific section was dedicated to assemble the methods and tools adopted. Finally, we proposed conceptual models for both approaches (building and stock-level) that outlined the main aspects and dynamics identified in this literature review. Thus, we obtained insights to be investigated in further studies.
... In the smart building domain, ontologies varied from the physical building, systems, devices, and governing rules [21,22] to including device users [23][24][25][26][27][28][29] and the energy providers [30]. The inclusion of the role of actors within the knowledge domain is applicable to the design of our ontology; however, these ontologies do not appear to capture the interactions (or relationships) between actors in the system. ...
... Especially as green buildings involve the use of novel components with limited insights on maintainability [16]. It is corroborated by studies which show complex green buildings with underwhelming operational performances [17][18][19][20]. This can be attributed to the lack of maintainability thinking and green FM during building operation [8,[21][22][23][24]. ...
Article
The concept of green maintainability provides the building sector with an exciting opportunity to transform itself with highly maintainable and sustainable buildings. It spans throughout the building project delivery process and overarches different building systems. This paper aims to propose a set of critical green maintainability performance indicators for building projects; to minimize the adverse environmental impacts whilst maximizing the functional, safety, energy efficiency and financial performance of the facility. To overcome data deficiency challenges, a qualitative research approach was used in this study; consisting of a systematic literature review and expert interviews. The identified set of green maintainability indicators are validated using a pilot case study of an exemplary building project with highly desirable levels of green maintainability. The identified critical indicators are crucial in the development of a green maintainability assessment system. This study is a significant attempt to promote green maintainability among building professionals to advance the lifecycle sustainability of built environments by seeking to improve facility design processes with capable tools.
... The usage of ontology would enhance information sharing and reuse by the structured information in way that improves the ability of different computer systems to inter-operate efficiently without misunderstanding and missing data. For instance, Corry, et al. [3] proposed a performance assessment ontology for the environmental and energy management of buildings, based on which, the heterogeneous data can be published semantically and then interpreted using a life-cycle performance framework. manual and qualitative reviews that are prone to subjectivity [6]. ...
Article
There is a wide range of literature on adopting ontology to solve construction problems, but no review of existing studies has systematically analyzed and visualized the trends in ontology research. This study reviews ontology research mainly published in the Scopus database from 2007 to 2017 with the combination of scientometric analysis and critical review. Scientometric analysis (e.g. co-author, co-word, co-citation, and clusters) objectively visualized the research status quo while a critical review was used to identify the research themes and challenges of ontology research in the construction industry. The results identified a large network of co-authors in this field to understand collaboration relationships. Over half the papers (53%) were published by the following three countries: the United States, the United Kingdom, and Canada. The top co-occurring keywords were “project management” at which ontology facilitates knowledge management and information retrieval. When the time factor was taken into consideration, keywords naturally evolved from “project management”, and “knowledge management” to “building information modeling”, and “compliance control” with the successful adoption of information techniques in the construction industry. Four research themes were identified with the combination of cluster analysis and critical review: “Domain ontology”, “Industry foundation classes”, “Automated compliance checking”, and “Building information modeling”. This review provides an in-depth understanding of existing ontology research and indicates the emerging trends in this research domain.
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.
Article
Full-text available
The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities.
Article
Full-text available
This review focuses on recent research literature on the use of Semantic Web Technologies (SWT) in city planning. The review foregrounds representational, evaluative, projective, and synthetical meta-practices as constituent practices of city planning. We structure our review around these four meta-practices that we consider fundamental to those processes. We find that significant research exists in all four metapractices. Linking across domains by combining various methods of semantic knowledge generation, processing, and management is necessary to bridge gaps between these meta-practices and will enable future Semantic City Planning Systems.
Article
Generative computation has the potential to enhance the accuracy, effectiveness, and creativity of spatial layout in design and planning. The paper proposes a methodology to separate the knowledge about objects, spatial relationships, and constraints from the generative process. The separation between the knowledge in a domain and its possible practical uses is an important achievement of semantic technologies, because it grants access to a large body of knowledge, spanning various aspects and processes across buildings and cities, which is being codified into formal ontologies. The present study has reused existing knowledge from two established ontologies. An illustrative case-project demonstrates the suitability of the methodology for a complex layout planning problem, involving a large number of decision-makers, with multiple competing objectives and criteria. The system implements multidimensional visual interactive tools to assist designers, planners, and decision-makers in exploring the layouts and the criteria, to develop their confidence in what qualifies as a good and effective solution.
Article
In this paper, a semantic model-based approach is proposed for building energy systems fault detection. Its basic idea is to mimic the general intelligence of human experts in understanding massive amounts of operational data of various buildings, and further proposing customized fault detection solutions. A domain ontology is developed to allow computers to understand the prior knowledge of building energy systems fault detection. Classes and properties are developed to formalize all possible configurations in this domain. Semantic rules are proposed to detect the operation problems, control problems, equipment malfunction and sensor failure in building energy systems. These rules are written in abstract syntax. They can be reused in various building energy systems. For a target system, the building data are mapped to the ontology to generate a customized knowledge graph. The knowledge graph captures the physics underlying the system operations. The semantic rules are activated based on the knowledge graph to detect the faults. The proposed approach is demonstrated using the historical data from an industrial building located in Wuhan, China. The results shows that the approach is powerful in providing the customized fault detection solutions for different situations. It has high levels of interpretability, reliability and automation. The knowledge graph is automatically updated with new data step by step. The semantic rules are activated if the conditions are satisfied based on the knowledge graph. The fault action mechanisms are captured based on the inference chains of the rules. Experts can find the fault reasons and take actions for commissioning.
Preprint
Full-text available
Pre-print as published on https://como.ceb.cam.ac.uk/preprints/270/.
Article
Despite their vast potential for delivering rich and intuitive visualizations of live building monitoring data, digital twins have been rarely studied in the context of thermal comfort. To narrow this gap, this study investigates the synergistic benefits of Building Information Modeling (BIM), the Internet of Things (IoT) and Virtual Reality (VR) for developing an immersive VR application for real-time monitoring of thermal comfort conditions. A system architecture was proposed for live calculation of the PMV/PPD indices based on ASHRAE standard 55 and enrichment of BIM-based representations of building spaces in VR environments with live IoT-enabled monitoring data. Openly available software tools were used to make the geometric and sensory data accessible within a VR application and calculate the PMV/PPD indices. Using a semi-automated method, raw thermal images streaming from a cost-effective non-intrusive sensor were processed on an affordable edge computing device to enable near real-time calculation of Mean Radiant Temperature (MRT). A prototype of the system was implemented and used in a series of experiments where a dynamic thermal environment was created in a mechanically conditioned space. The results support the consistency between the system's output and the actual thermal sensations observed under various conditions.
Article
Hazards potentially affect the safety of people on construction sites include falls from heights (FFH), trench and scaffold collapse, electric shock and arc flash/arc blast, and failure to use proper personal protective equipment. Such hazards are significant contributors to accidents and fatalities. Computer vision has been used to automatically detect safety hazards to assist with the mitigation of accidents and fatalities. However, as safety regulations are subject to change and become more stringent prevailing computer vision approaches will become obsolete as they are unable to accommodate the adjustments that are made to practice. This paper integrates computer vision algorithms with ontology models to develop a knowledge graph that can automatically and accurately recognise hazards while adhering to safety regulations, even when they are subjected to change. Our developed knowledge graph consists of: (1) an ontological model for hazards: (2) knowledge extraction; and (3) knowledge inference for hazard identification. We focus on the detection of hazards associated with FFH as an example to illustrate our proposed approach. We also demonstrate that our approach can successfully detect FFH hazards in varying contexts from images.
Article
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 time-series analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit time-series data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study describing the scalability of the approach to integrate a building BAS system with a BIM. 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.
Conference Paper
Full-text available
The concept of green maintainability provides the building sector with an exciting opportunity to transform itself with highly maintainable and sustainable buildings. It spans across different building systems; throughout their lifecycles whilst dealing with conflicting objectives of different stakeholders. This level of complexity bids the question, "how green maintainability can be measured objectively and be aggregated in a meaningful manner". This paper illustrates the development of a conceptual framework for green maintainability to address this question, by understanding the impact of the design alternatives brought forth by design for green maintainability. This conceptual framework is developed based on a comprehensive literature review. This study is an attempt to assist building design teams to incorporate green maintainability considerations in their design such that economic, environmental and social benefits are realized during the facility operations.
Article
Full-text available
Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.
Conference Paper
Full-text available
Many building energy performance (BEP) simulation tools, such as EnergyPlus and DOE-2, use custom schema definitions (IDD and BDL respectively) as opposed to standardised schema definitions (defined in XSD, EXPRESS, and so forth). A Simulation Domain Model (SimModel) was therefore proposed earlier, representative for a new interoperable XML-based data model for the building simulation domain. Its ontology aims at moving away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with the Industry Foundation Classes (IFC). In this paper, we document our ongoing efforts to make building simulation data more interoperable with other building data. In order to be able to better integrate SimModel information with other building information, we have aimed at representing this information in the Resource Description Framework (RDF). A conversion service has been built that is able to parse the SimModel ontology in the form of XSD schemas and output a SimModel ontology in OWL. In this article, we document this effort and give an indication of what the resulting SimModel ontology in OWL can be used for.
Conference Paper
Full-text available
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.).
Conference Paper
Full-text available
Effective Decision Support Systems (DSS) for building service managers require adequate performance data from many building data silos in order to deliver a complete view of building performance. Current performance analysis techniques tend to focus on a limited number of data sources, such as BMS measured data (temperature, humidity, C02), excluding a wealth of other data sources increasingly available in the modern building, including weather data, occupant feedback, mobile sensors & feedback systems, schedule information, equipment usage information. This paper investigates the potential for using Linked Data and Semantic Web technologies to improve interoperability across AEC domains, overcoming many of the roadblocks hindering information transfer currently.
Conference Paper
Full-text available
Building Energy Simulation (BES) models play a significant role in the design and optimisation of buildings. Simulation models may be used to compare the cost-effectiveness of Energy-Conservation Measures (ECMs) in the design stage as well as assessing various performance optimisation measures during the operational stage. Common metrics used to indicate Building Energy Performance include Energy cost, Carbon Dioxide emissions and Indoor Thermal Comfort (Predicted Mean Vote - PMV / Predicted Percentage Dissatisfied - PPD). Multi-variable optimisation of Building Design and Control often focuses on minimising cost while maximising thermal comfort. This paper focuses on the use of simulated thermal comfort for performance optimisation; particularly the experimental validation of this key building performance index using a calibrated BES model of a case study naturally ventilated building.
Article
Full-text available
With the increasing demand for more energy efficient buildings, the construction industry is faced with the challenge to ensure that the energy performance predicted during the design stage is achieved once a building is in use. There is, however, significant evidence to suggest that buildings are not performing as well as expected and initiatives such as PROBE and CarbonBuzz aim to illustrate the extent of this so called ‘performance gap’. This paper discusses the underlying causes of discrepancies between energy modelling predictions and in-use performance of occupied buildings (after the twelve month liability period). Many of the causal factors relate to the use of unrealistic input parameters regarding occupancy behaviour and facilities management in building energy models. In turn, this is associated with the lack of feedback to designers once a building has been constructed and occupied.The paper aims to demonstrate how knowledge acquired from Post-Occupancy Evaluation (POE) can be used to produce more accurate energy performance models. A case study focused specifically on lighting, small power and catering equipment in a high density office building is analysed and presented. Results show that by combining monitoring data with predictive energy modelling, it was possible to increase the accuracy of the model to within 3% of actual electricity consumption values. Future work will seek to use detailed POE data to develop a set of evidence based benchmarks for energy consumption in office buildings. It is envisioned that these benchmarks will inform designers on the impact of occupancy and management on the actual energy consumption of buildings. Moreover, it should enable the use of more realistic input parameters in energy models, bringing the predicted figures closer to reality.
Conference Paper
Full-text available
This paper describes the use of building performance simulation into a wider implementation of an energy management system (EnMS) based on ISO 50001 requirements. The CASCADE project, funded by the European Union's Seventh Framework Programme (FP7) call "ICT for Energy Efficient Buildings" aims to test different modelling strategies supporting Fault Detection and Diagnosis (FDD). Some of the main challenges includes the integration of new and legacy IT systems, the adoption of a robust calibration methodology, and the systematic verification of energy savings. This paper gives an overview of the CASCADE project and then proposes an approach that defines the use of calibrated whole building performance simulation (WPBS) as a supporting tool for improved building operation. Coupling of WBPS within the CASCADE methodology will increase the functional support offered to energy managers. A case study based on Milan Malpensa Airport that shows the initial model development is also described. Finally a definition of the future steps that will be followed by this research work is described.Copyright
Article
Full-text available
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations --- the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
Article
Full-text available
The aim of commissioning new buildings is to ensure that they deliver, if not exceed, the performance and energy savings promised by their design. When applied to existing buildings, commissioning identifies the almost inevitable 'drift' from where things should be and puts the building back on course. In both contexts, commissioning is a systematic, forensic approach to quality assurance, rather than a technology per se. Although commissioning has earned increased recognition in recent years - even a toehold in Wikipedia - it remains an enigmatic practice whose visibility severely lags its potential. Over the past decade, Lawrence Berkeley National Laboratory has built the world's largest compilation and meta-analysis of commissioning experience in commercial buildings. 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 $17 million to $43 million in commissioning expenditures. The recorded cases of new-construction commissioning took place in buildings representing $2.2 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/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/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.
Article
Full-text available
Recital 16 of the Energy Performance of Buildings Directive (EPBD) requires the energy certificate to describe a building's actual energy-performance situation to the extent possible. If we wish to achieve the rapid reductions in energy use and CO 2 emissions that the EPBD anticipates, it is vital that this clause is taken seriously. It provides a fantastic opportunity to report actual energy use clearly, to grade it against a clear description of the building in use, and to relate it transparently to expectations at the design stage. This will at last begin to close the feedback loop, reduce the credibility gaps that so often occur between design expectations of energy efficiency and actual fuel consumption outcomes, and consequently lead to more rapid improvements in building energy performance. Credibility gaps arise not so much because predictive techniques are "wrong", but because the assumptions often used are not well enough informed by what really happens in practice, because few people who design buildings go on to monitor their performance. While some differences are legitimate (e.g. the building is used more, or has more things in it), surveys nearly always reveal avoidable waste - which can arise from poor briefing, design, construction and commissioning, and not just bad training, bad maintenance and bad management. A widespread problem is control systems which just do not work, or have poor management and user interfaces, resulting in equipment defaulting to ON unnecessarily. To achieve genuine step-change improvements, procuring clients, design and building teams, users and managers will all need to engage much more closely with achieved performance. Better transparency between intentions and outcomes will release drivers towards better assumptions, better predictions, better design, better implementation, and better management of both the procurement and the product. We discuss how certification might be developed to help identify and close the credibility gaps, and present an idea for an energy certificate which takes these issues into account.
Article
Full-text available
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/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
Article
Full-text available
Technological advances in real-time data collection, data transfer and ever-increasing computational power are bringing simulation-assisted control and on-line fault detection and diagnosis (FDD) closer to reality than was imagined when building energy management systems (BEMSs) were introduced in the 1970s. This paper describes the development and testing of a prototype simulation-assisted controller, in which a detailed simulation program is embedded in real-time control decision making. Results from an experiment in a full-scale environmental test facility demonstrate the feasibility of predictive control using a physically-based thermal simulation program.
Article
Full-text available
Ontologies have been successfully applied as a semantic enabler of communication between both users and applications in fragmented, heterogeneous multinational business environments. In this paper we discuss the underlying principles, their current implementation status, and most importantly, their applicability to problems in the building information modeling domain. We introduce the development of an ontology for the building and construction sector based on the industry foundation classes. We discuss several approaches of lifting modeling information that is based on the express family of languages for data modeling onto a logically rigid and semantically enhanced ontological level encoded in the W3C Ontology Web Language. We exemplify the added value of such formal notation of building models by providing several examples where generic query and reasoning algorithms can be applied to problems that otherwise have to be manually hard-wired into applications for processing building information. Furthermore, we show how the underlying resource description framework and the set of technologies evolving around it can be tailored to the need of distributed collaborative work in the building and construction industry.
Article
Full-text available
Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In this paper we argue and show how the use of Semantic Web technologies can increase to an important extent the level of automation for analysing business processes. We present a domain-independent ontological framework for Business Process Analysis (BPA) with support for automatically computing metrics. In particular, we define a set of ontologies for specifying metrics. We describe a domain-independent metrics computation engine that can interpret and compute them. Finally we illustrate and evaluate our approach with a set of general purpose metrics.
Conference Paper
Full-text available
Business Process Management (BPM) aims at supporting the whole life-cycle necessary to deploy and maintain business processes in organisations. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. However, the degree of automa- tion currently achieved cannot support the level of adaptation required by businesses. Initial steps have been performed towards including some sort of automated reasoning within Business Process Analysis (BPA) but this is typically limited to using taxonomies. We present a core ontology aimed at enhancing the state of the art in BPA. The ontology builds upon a Time Ontology and is structured around the process, resource, and object perspectives as typically adopted when analysing business processes. The ontology has been extended and validated by means of an Events Ontology and an Events Analysis Ontology aimed at captur- ing the audit trails generated by Process-Aware Information Systems and deriving additional knowledge.
Conference Paper
IT solutions can aid decision makers in making informed decisions that lower the energy consumption in buildings. However, in order to design and implement an IT solution there are a number of issues that need to be resolved, for example, adequately handling sometimes contradicting goals of the decision makers and integrating the Decision Support System with the existing building IT infrastructure in the form of building management systems. In this paper we report on our initial experiences from implementing a decision support system for the management of energy consumption in public buildings. The experiences are based on our work with the EnRiMa project that aims to develop a state-of art decision support system for lowering the energy consumption and CO2 emissions of public buildings. We divide our experiences into two areas, namely, business concerns and software architectural, and provide our initial solutions and lessons learned with respect to these areas. Furthermore, we discuss a number of challenges for future work in the area of IT support for energy efficiency.
Article
There often is a significant difference between predicted (computed) energy performance of buildings and actual measured energy use once buildings are operational. This article reviews literature on this ‘performance gap’. It discerns three main types of gap: (1) between first-principle predictions and measurements, (2) between machine learning and measurements, and (3) between predictions and display certificates in legislation. It presents a pilot study that attempts an initial probabilistic probe into the performance gap. Findings from this pilot study are used to identify a number of key issues that need to be addressed within future investigations of the performance gap in general, especially the fact that the performance gap is a function of time and external conditions. The paper concludes that the performance gap can only be bridged by a broad, coordinated approach that combines model validation and verification, improved data collection for predictions, better forecasting, and change of industry practice.
Article
Building managers have specific duties and certain outputs that are required of them. Without the necessary data, information, tools, and time, they are unable to adequately meet their organisational goals. Scenario modelling enables explicit and unambiguous coupling of building functions with other pivotal aspects of building operation in a method that specifically considers the education and technical expertise of building managers. This new method captures, transforms, and communicates the complex interdependencies of environmental and energy management in buildings through an easily navigable, holistic, and reproducible checking mechanism that compares actual performance with predicted performance and completes the “plan-do-check-act” cycle for building managers. Most important, the structured nature of this method caters to the diverse profile of building managers, making it applicable for widespread deployment. This paper demonstrates the benefit of using the new method by examining its application to a performance analysis of two existing buildings.
Article
Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.
Article
Buildings often do not perform as well in practice as expected during pre-design planning, nor as intended at the design stage, nor even as measured during commissioning and maintenance operations. While this statement is generally considered to be true, it is difficult to quantify the impacts and long-term economic implications of a building in which performance does not meet expectations. This leads to a building process that is devoid of quantitative feedback that could be used to detect and correct problems both in an individual building and in the building process itself. A key element in this situation is the lack of a standardized method for documenting and communicating information about the intended and actual performance of a building. This deficiency leads to several shortcomings in the life-cycle management of building information. Planners have no means of clearly specifying their expectations. Designers do not concisely document their design intent. Commissioning personnel have no standardized method for documenting the results of performance testing. Post-occupancy building performance cannot readily be compared to expectations in an attempt to evaluate and improve design and operation decisions. Lastly, without quantification of the magnitude of performance problems it is difficult to motivate building process participants to alter their current practice. This document describes an information management concept and a prototype tool based on this concept that has been developed to address this situation. The Building Life-cycle Information System (BLISS) has been designed to manage a wide range of building related information across the life cycle of a building project. Metracker is a prototype implementation of BLISS based on the International Alliance for Interoperability's (IAI) Industry Foundation Classes (IFC). The IFC is an evolving data model under development by a variety of architectural, engineering, and construction (AEC) industry firms and organizations (IAI, 2001). Metracker has been developed to demonstrate and explore the process of tracking performance metrics across the building life cycle.
Article
Urban areas have unique characteristics that render their residents and assets particularly vulnerable to climate change. Many large urban centers are located along coasts or in low-lying areas around the mouths of major rivers, placing economic capital and human populations at risk of climate-related hazards including sea level rise and flooding from severe precipitation. Recent literature illustrates the economic and social challenges facing cities around the world as a result of climate change including energy shortages, damaged infrastructure, increasing losses to industry, heat-related mortality and illness, and scarcity of food and water. These challenges are interrelated. Economic losses make it difficult for residents to maintain their livelihoods and can therefore exacerbate social issues including poverty and hunger. At the same time, some demographic and socioeconomic characteristics of cities can make them especially vulnerable to climate change impacts. This paper reviews current literature on these issues and identifies future research needed to more fully understand climate change in the urban context.
Article
Continuous Commissioning (CCSM) is an ongoing process to resolve operating problems, improve comfort, optimize energy use, and identify retrofits,for existing commercial and institutional buildings and central plant facilities. CC,focuses on optimizing improving overall system control and operations for the building as it is currently utilized and on meeting existing facility needs. Innovative optimal engineering solutions are developed using engineering-based model analysis integrated with scientific field measurement. Integrated approaches are used to implement these solutions to ensure practical local and global system optimization and to ensure persistence of the improved operational schedules. Implementation of the CC process has typically decreased building energy consumption by 20% in well over 100 large buildings where it has been implemented. This paper presents the CC process, the primary CC techniques and measures, and a case study.
Article
An industry foundation class based building product model of University College Cork's "to be constructed" Environmental Research Institute will be developed. This paper discusses combining such a building product model with a building management system and other tools and technologies to create a framework for monitoring, analysing and controlling a building throughout its building lifecycle based on a set of performance metrics. The framework will be known as the building energy monitoring, analysing and controlling (BEMAC) framework. Current building performance assessment practices lack standardisation/continuity throughout the building lifecycle. An environment such as the BEMAC framework described in this paper offers a means to achieving such standardisation/continuity by documenting and communicating performance metrics data such that these data can provide value across the complete lifecycle of a building project, from planning through design and construction into occupancy and operation.
Article
The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation.