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  Building Information Modelling for analysis of energy ef  󿬁 cientindustrial buildings  –  A case study Georgios Gourlis, Iva Kovacic n Department for Industrial Building and Interdisciplinary Planning, Faculty of Civil Engineering, Vienna University of Technology, Karlsplatz 13/ e234-2,1040 Vienna, AustriaKeywords: BIMBEMIndustrial constructionThermal simulation a b s t r a c t Industrial buildings demand higher amount of energy than other building typologies, thus powerfulmodelling and simulation tools for energy-optimisation and identi 󿬁 cation of synergies-potentialsbetween the building envelope, building services and production systems are needed.Building Information Modelling (BIM), as emerging technology, bears promise to support processesintegration thus enabling life-cycle management of buildings. BIM model serves as a joint knowledgedatabase where data transfer between various models is possible; thereby enabling follow up studies,such as cost, thermal and structural analysis.Adoption of BIM to BEM (building energy modelling) approach is particularly interesting for opti-misation of industrial facilities. Multiple layers of interacting complex systems (building, services andmachine  󿬂 oor layout) require careful modelling and control of geometry in terms of collisions, variousadaptions due to the short product-life-cycles, as well as integrated energy performance analysis alonginteracting systems.This paper explores the potentials and de 󿬁 cits of the modelling, analysis and optimisation of energy-ef  󿬁 cient industrial buildings using BIM to BEM methodology, by means of case study research of twoindustrial facilities. Varying needs concerning the Level of Development and semantic differences in themodelling procedures of part-taking disciplines (architecture, structural engineering or analysis) wereidenti 󿬁 ed as problems; as well as time pressure as one of the main reasons for defects of buildingmodels. The identi 󿬁 ed de 󿬁 cits represent various types of uncertainties related to the integrated energymodelling, as BIM to BEM can be referred to. We conclude that as a  󿬁 rst step of integrated modelling, anuncertainty-analysis should be carried out, and strategies how to deal with these developed. In order tominimise BIM to BEM uncertainties, not only interoperability issues of the software has to be improved(modelling uncertainty), but moreover, the rede 󿬁 nition of the design process and enhancement of individual capabilities is necessary (process uncertainty). &  2016 Published by Elsevier Ltd. Contents 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1. Building Information Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2. BIM for industrial facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3. BIM to BEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.4. Uncertainties in energy modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rser Renewable and Sustainable Energy Reviews http://dx.doi.org/10.1016/j.rser.2016.02.0091364-0321/ &  2016 Published by Elsevier Ltd.  Abbreviations:  AEC, Architecture Engineering Construction; BIM, Building Information Modelling; BEM, Building Energy Modelling; CAD, Computer Aided Design; ERP,Enterprise Resource Planning Software; FM, Facility Management; gbXML, Green Building Extensible Markup Language Schema; GFA, Gross Floor Area; GIS, GeographicalInformation Systems; HVAC, Heating Ventilation Air-Conditioning (Engineering); IFC, Industrial Foundation Classes Data Standard; MEP, Mechanical Electrical Plumbing(Engineering); TBS, Technical Building Services n Corresponding author. Tel.:  þ 43 58801 215 26. E-mail addresses:  georgios.gourlis@tuwien.ac.at (G. Gourlis), iva.kovacic@tuwien.ac.at (I. Kovacic). Please cite this article as: Gourlis G, Kovacic I. Building Information Modelling for analysis of energy ef  󿬁 cient industrial buildings  –  Acase study. Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.02.009i Renewable and Sustainable Energy Reviews  ∎  ( ∎∎∎∎ )  ∎∎∎ – ∎∎∎  4. Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1. Introduction Buildingstocks are responsible for 40% ofenergyconsumptioninthe EU and for 36% of greenhouse gas emissions [1], the largest partof which occurs throughout the operation-phase. Recent researchand practice has been largely focusing on analysis and optimisationof energy consumption of residential buildings [2,3], less so on public or commercial buildings (such as schools or of  󿬁 ces) [4].Optimisation of energy performance of industrial buildings hasseldom been in the focus of research, due to the large energy-consumption of industrial production processes [5]. However,through recent policymaking  –  introduction of Energy Directive orEnergy Performance of Buildings Directive [6], as well as to recentenergy-availability issues, more integrated approaches regardingenergy ef  󿬁 ciency of industrial facilities have been proposed [7]. Inthis context, the balanced performance of building design, thermalenvelope and HVAC systems, and use of synergies with relevantprocesses and occupancies has been increasingly advocated as theright approach [8]. Yet such an approach requires modelling, ana-lysis and optimisation of complex systems, for which powerfulcomputational tools are needed. Building Information Modelling, as “ a digital representation of physical and functional characteristics of a facility ”  offers potentials for life-cycle modelling and managementof buildings and building systems [9]. Through creation of a jointknowledge base  –  information rich building model  –  a follow upthermal, structural or cost analysis can be carried out. BIM, seen asmulti-dimensional tool for life-cycle management, can be classi 󿬁 edinto 3D BIM  –  parametric building model, as an upgrade to a 2DCAD plan, 4D addressing time  –  scheduling and construction stagessimulation, 5D cost  –  planning and estimation, 6D sustainability  – thermal analysis and environmental assessment, eventually evenautomated building certi 󿬁 cation, and  󿬁 nally 7D as a fully mature,comprehensive model enabling facility management, maintenanceand operation [10].In this paper we will explore the potentials of energy-analysisand simulation on a case study of two industrial facilities usingBIM to BEM (Building Information Modelling to Building EnergyModelling) approach, thereby addressing the issues of the socalled 6D BIM  –  assessment of sustainability. We evaluated themodelling process and software-interfaces from BIM to follow upthermal simulation using BEM and tested the suitability of themodels as joint knowledge base for life-cycle management of architectural, HVAC and shop- 󿬂 oor models. We will outline pos-sible solutions for the minimisation of aforementioned uncer-tainties in such integrated modelling processes. 2. Literature review   2.1. Building Information Modelling  The common understanding of BIM terminology in the AECindustry in both practice and academia is multifaceted. Succar [11]delivers an overview of various BIM de 󿬁 nitions. BIM terminus issrcinally coined by the CAD-software developer Autodesk [12],Graphisoft [13] was using Virtual Building, where as Eastman [14] introduces the Building Product Models.BIM is de 󿬁 ned as:   The  “ new CAD ”  paradigm [15]  –  an advanced version of digitaldrafting tool.   The building modelling  tool  providing possibilities of interac-tion with non CAD-based tools, such as quantity surveyors ’  orproject management tools [16].   A  methodology  : “ …  to manage the essential building designand project data in digital format throughout the building's life-cycle. ”  [17] (p. 403).   The emerging new  paradigm :  “ …  an emerging technologicaland procedural shift in the Architecture, Engineering and Con-struction industry. ”  [10] (p. 357).   Or according to the UK Government programme [18]:  “ …  acollaborative  way of working  , underpinned by the digitaltechnologies which unlock more ef  󿬁 cient methods of designing,creating and maintaining our assets. ” BIM is often mentioned in relation to building product mod-elling, a predecessor terminus to BIM, dating from the 80ies [17].The product models address the object-oriented modelling of thedata-rich building components, incorporating 3D geometries,spatial information, thermal values, and material properties;parameters upon which data interoperability builds up [19].To the most utilised BIM Tools count Autodesk Revit (as onestop shop, offering possibilities for architectural, structural andMEP modelling and even proprietary tools for thermal and day-light analysis), Archicad by Graphisoft primarily used for archi-tectural modelling, Tekla by Trimble, as engineering modellingtool, Allplan by Nemetschek, Microstation by Bentley etc.BIM has often been recognised in research and practice as asuitable tool for support of collaborative planning, facilitatingcommunication and information exchange between diverse plan-ning process participants [20]. More practice-oriented publicationsoften advocate BIM bene 󿬁 ts as maximisation of ef  󿬁 ciency, qualityand reducing time effort [21]. It is largely understood as object-oriented digital representation of a building or built environment,which enables interoperability and data-exchange in digital form[22]. In this context BIM addresses primarily the process of model-building and information exchange [11].BIM, in addition to support of collaborative processes, canthrough its capability of attributing both spatial and geometrical aswell as non-geometrical attributes to building elements beimplemented in various areas of the AEC industry, such as sus-tainability analysis [23], structural analysis [24], thermal simula- tion [25], daylight simulation [26], construction management [27], cost estimation and planning [28],  󿬁 re protection [29], safety onconstruction site [30], facility management [31] etc. Therefore the development of functioning and open interfacesis one of the major tasks in the advancement and successfuladoption of BIM technology in the industry. One of the mostimportant, open non-proprietary interfaces is the IndustrialFoundation Classes (IFC), developed and supported by buildingS-MART (International Alliance for Interoperability), which also G. Gourlis, I. Kovacic / Renewable and Sustainable Energy Reviews  ∎  ( ∎∎∎∎ )  ∎∎∎ – ∎∎∎ 2 Please cite this article as: Gourlis G, Kovacic I. Building Information Modelling for analysis of energy ef  󿬁 cient industrial buildings  –  Acase study. Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.02.009i  certi 󿬁 es the BIM software for IFC-import and export ability [32].Despite the efforts towards providing maximum interoperabilityand advancement of the IFC standard, due to the highly frag-mented AEC market and lack of process integration, software-interoperability remains one of the greatest challenges for suc-cessful BIM adoption. A large number of software still offers pro-prietary, software-speci 󿬁 c interfaces, trying to provide in such waya one-stop shop solution in form of   “ One-Platform-BIM ” . However,through current strategy by public policy to mandate BIM use inpublic projects  –  such as the UK Government Construction Strat-egy  –  not only should BIM use be enhanced for the integration of the fragmented AEC industry at the design and planning stage, butmoreover for achieving an added value along the life-cycle [33].Successful BIM use throughout the life-cycle is related to theef  󿬁 cient data- and model-exchange among various stakeholdersfrom the AEC industry, which again calls for improvement of interfaces, creation of joint working platforms such as  “ Cloud BIMinformation exchange mechanisms ”  [10], as well as exact analysisof actual needs of each discipline in order to provide and transferwhat is actually needed instead of what is available.It can be concluded that a joint understanding of BIM is lackingin the AEC industry  –  it is simultaneously understood as a soft-ware, designing and planning method or a new integrated pro-cedure in the AEC industry [34]. The lack of joint understandingposes great challenges for a successful implementation and use of full potentials along the whole value-chain, particularly regardingthe problem-solving of interoperability issues.  2.2. BIM for industrial facilities Adoption of BIM is particularly bene 󿬁 cial for design, planning,optimisation and management of industrial facilities. Industrialfacilities as building typology are particularly demanding in termsof design, due to the diverging interior climate requirements of various functional units (of  󿬁 ce, production, storage), regulations of vertical and horizontal circulation and accessibility (e.g. employeesvs. customers) and  󿬁 nally interactions of various systems such asbuilding and structural components, HVAC and machine  󿬂 oorlayout and infrastructure. The design process requires soundvalidation and design review (e.g. in terms of collisions) ,  which isenhanced through BIM modelling approach combined with auto-mated model checking and analysis tools, such as Solibri Checkeror Tekla BIMsight.Different than other building typologies, where economic life-cycles range from 50 to 80 years, industrial buildings are char-acterised by relative short life-cycles ranging from 15 up to 30years, as determined by the short product-life-cycles. A pre-requisite for achieving economic and environmental sustainabilityis the prolongation of the building's life duration, which calls forthe highest possible  󿬂 exibility and expandability of the layout,posing challenges on the structural design. Further on, dependingon the production process, there are higher internal heating loadsthan in other building typologies, which can be used for heating of accompanying of  󿬁 ces and supporting facilities, for warm watersupply etc. The use of such synergy effects, as well as optimisationof the load bearing structure in terms of   󿬂 exibility, calls for carefulmodelling and analysis of the systems  –  building structure andenvelope, HVAC and energy supply  –  and even coupling theproduction-system models already in the early design phases. Acomprehensive BIM model, as a joint knowledge base of spatial,geometrical, energy and cost data offers potential for couplingwith computational energy analysis or even enterprise resourcesplanning tools, not only for the design, but moreover for themanagement of an industrial facility along its life-cycle.The most commonly utilised tool for modelling of industrialfacilities is the Autodesk REVIT software [35], which offersarchitectural, structural and HVAC modules (Revit MEP); in socalled One-Platform-BIM, reducing in this way data transfer viainterfaces. Despite the One-Platform solution for the facility side,the tool (equipment) and shop-layout suppliers use wide range of various software tools, most of which are not IFC capable, whichposes large problems for BIM utilisation in industrial construction.Use of BIM for design and life-cycle management of industrialfacilities is increasing in the practice, however due to the con- 󿬁 dentiality and data protection there are still a very few publishedstudies identifying the potentials and limits of BIM in industrialconstruction.Huang et al. [36] identify the BIM potential for life-cyclemanagement of industrial parks in Taiwan, underlining theadvantages of combining BIM based visualisation, GIS and ICTsolutions, for successful management of industrial parks. Themulti-modular system architecture offers navigation support andutilities and facilities are modelled with BIM, therefore users canretrieve drawing and attribute data in real time of e.g. pipeline andutilities systems. Wang et al. [37] explore the possibilities in thedesign of industrial facilities from the pre-design (workshopdesign) till construction using Autodesk Revit Software, andinterface (DXF) towards work 󿬂 ow-software for optimisation of production-work 󿬂 ows. The parametric model delivers statisticaland analytical data, maintenance drawings etc.Especially interesting is the use of BIM for design of semi-conductor production facilities, due to the very short planning andconstruction time horizons (10 months from pre-design till takeover)  –  where BIM can show advantages in reduction of planningtime through reduction of changes (visualisation of collisions, auto-mated extraction of cost and time relevant data) and allowing cou-pling of the facility supply with the tools. On the concrete case studyof a semi-conductor facility the information of the tool supplier,facility- and tool-layout designer was exchanged using BIM [38]. ToolInformation Model was imported in Revit MEP application (facilitysupply model) testing the Industrial Foundation Classes (IFC) inter-face; however it was found that the IFC standard does not match theSEMI Standard (semi-conductor industry standard) thus allowing thedata exchange only in one way.A special focus of this research is the use of BIM for energy-optimisation of industrial facilities based on integrated approach,including consideration of waste heat from machines, machiningprocesses, occupancy related interior gains as well as solar gains [39].  2.3. BIM to BEM  The utilisation of BIM for building performance modelling andanalysis is an increasing research topic in the academic commu-nity, due to the BIM potentials for integration of the geometrical,material, technical, structural, and HVAC data on the one hand, aswell as stricter requirements and policies for sustainable con-struction on the other. Several tools have already been introducedfor BIM-based and -supported semi-automated or even automatedenergy analysis. A prototypical Design Performance Viewer (DPV)tool was developed for Autodesk Revit architectural modellingsoftware, intended for the calculation of energy and exergy in theearly design stages by Schlueter and Thesseling [40]. The samemodelling software was tested for automated assessment of sus-tainability certi 󿬁 cates, extruding necessary information for rele-vant indicators [41]. Utilising BIM application programminginterface (API) and Modelica-based BEM, Jeong et al. [42] pre-sented an automated framework for simulating and visualisingenergy analysis results back inside the BIM software Revit, pro-viding direct feedback to designers. Also integrated in Revit, BPOptcombined visual programming-based parametric BIM with build-ing thermal and daylighting simulations, and was tested in thecase of a residential building, where automatically collected data G. Gourlis, I. Kovacic / Renewable and Sustainable Energy Reviews  ∎  ( ∎∎∎∎ )  ∎∎∎ – ∎∎∎  3 Please cite this article as: Gourlis G, Kovacic I. Building Information Modelling for analysis of energy ef  󿬁 cient industrial buildings  –  Acase study. Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.02.009i  from the BIM model were used for minimising energy consump-tion while maximising appropriate daylighting level, according toLEED requirements [43].Different to the One-Platform-BIM solutions, Lawrence Berke-ley National Laboratory developed the Space Boundary Tool (SBT)for a semi-automated process for transformation of BIM to BEMmodels, using open-BIM approach via IFC interface, thus providingfor a more generic work 󿬂 ow [44,45]. Welle et al. [46] and Ahn et al. [47] also created IFC-based tools for enabling automatedthermal simulation with EnergyPlus by creating input data  󿬁 les(IDF) containing geometry, thermal space boundaries and materialinformation from the BIM model, aiming to improve the accuracyand modelling time of the BEM models. Whereas Cemesova et al.[48] proposed a tool for combining BIM IFC-based geometry andinformation from the Passive House Planning Package (PHPP)design tool to assess energy performance and decision making forPassivHaus certi 󿬁 cations.In all referenced studies, interoperability and data-transfer aswell as ease of use from BIM to BEM systems play a crucial role inorder to reduce the re-modelling efforts and easy creation of building energy models [44]. Clarke and Hensen [49] state that the core issue for design process integration is how to transfer infor-mation between tools, without the need to access different BIMmodels. Information exchange from BIM to BEM software is mostcommonly provided via the already discussed reference standardof IFC and via the gbXML (green building extensible markup lan-guage) data format, developed for the energy simulation domainand therefore supported by many analysis tools. Detailed exam-ination of properties, comparison and limitations of the twoapproaches are described in [50,51]. On one hand gbXML is sim- pler and easier to understand and implement by BEM softwaredevelopers, therefore thermal simulation tools such as IES-VE [52],EnergyPlus [53], eQUEST [54] and similar expert tools still only support this format and not IFC import. On the other, IFC is theonlyopen ISO standardised interface in the building data exchangecontext [55], becoming the primary BIM language able to compriseseveral types of BIM information across all disciplines and life-cycle phases. Researchers intently explore the capabilities of bothgbXML  [56,57] and IFC [58,59] schemas, but also examine approaches not embracing these data formats [40,42,43]. However under the prism of open-BIM, using standard data transfer sche-mas facilitates the BIM to BEM procedure among different tools.El Asmi et al. [59] reviewed the technological stand of BIM toBEM data formats and concluded that even the most advanced andextended data framework fails to generate reliable BEM modelsfrom BIM modes, including all required information. Worth men-tioning is the limited interoperability of HVAC system compo-nents, which is not improved in the latest version of the IFC formatIFC4 [60], a  󿬁 eld particular important in the context of industrialbuildings.Experiment results on the interconnection of BIM and BEMtools showed that there are often problems in data transferabilitysuch as error-prone geometry leading to inconsistencies and lossof information (e.g. material properties) [61 – 63]. BIM modelscontain a greater degree of information than required and can betranslated for a thermal energy analysis [64]  –  displaying too highLevel of Development. For example BEM model can contain a largenumber of thermal zones when imported from BIM (every room istranslated to a thermal zone), therefore methods are tested forreducing this information to the required extent [65]. Thenumerous geometry-related modelling problems in data transferfrom BIM to BEM are mostly associated to the varying boundariesof room stamps and thermal zones, as well as to wrong inter-pretations of non-planar geometry [61], leading to duplicate ormissing objects and missing or incorrect space volumes [62].Operative cause is that in architectural models a room stampidenti 󿬁 es an area in m 2 of a speci 󿬁 c functional unit (interiorboundaries of walls), whereas most building energy models need aboundary adjusted thermal zone de 󿬁 nition, which includes cen-treline of horizontal or vertical partitions and is not interested intheir thickness [66]. This leads to inaccurate analytical repre-sentations of the building design that need to be manually trans-formed for further use for performance simulation [67]. A recentpractise oriented case study showed that large and complex “ realistic ”  BIM models may completely fail to be transferred toBEM and a trial and error process has to be employed, withoutproviding a guaranteed outcome [68].  2.4. Uncertainties in energy modelling  To summarise, automated and semi-automated processes forerror free data transfer have been developed to assist BIM – BEMsoftware communication without human intervention [44 – 46],however these require custom software plug-ins and program-ming skills or a speci 󿬁 c design methodology during the creation of the BIM model [41,62], an attribute that existing BIM models, designed by planers and architects, do not have. In the practiceBEM models based on BIM data export are intensively reworked bysimulation experts in order to be used for further analysis, thisthough bears the risk of arbitrary building de 󿬁 nitions based onpersonal understanding and expertise, being also time consuming.Such procedures may contribute to the fact that predictions forenergy consumption of BEM models often deviate from actualmeasured data, resulting in the case of complex non-residentialbuildings in under-predictions in the order of 30% [69].Various dif  󿬁 culties burdening the energy modelling and opti-misation process can be assigned to the uncertainties identi 󿬁 ed inintegrated energy modelling processes by [70]. The uncertaintiesusing the BIM modelling and follow up analysis and simulationapproach can be met at linguistic level (various planning dis-ciplines of various professional languages) [71], as epistemicuncertainties (model structure and software/hardware errors)[72], and also planning procedural uncertainties (resources andtime) [73].None of the afore mentioned tools or processes has found wideapplication in the practice, due to the formerly explored reasons  – the knowledge-transfer gap between the partaking disciplines orthe lack of strategies for dealing with uncertainties when inte-grated energy modelling is applied within the state-of-the-artdesign process. 3. Methodology  In order to evaluate the potentials of BIM for design andenergy-optimisation of industrial facilities case study methodol-ogy was used. Case studies are often used for theory building,serving as singular  “ experiments ”  [74]. Multiple case studies buildagain a series of related experiments, extending the emergingtheory [75]. However, differently than laboratory experiments,which isolate the phenomena from the context, case studies arestrongly related to the real-word context in which they occur, thusproviding the knowledge of what was planned and what actuallyhas occurred [76].Next tothe case study, thermal simulation modelling was applied.For the energy and thermal modelling of the building a so called “ white box ”  approach was used [77], which uses physics based equations to model building or building systems. The  “ black box ” approach, on the other side, is based mostly on probabilistic model,using statistical data. Generally the  “ white-box ”  simulation modelconsists of the input parameters such as weather conditions, andparametric description of building elements; the simulation engine G. Gourlis, I. Kovacic / Renewable and Sustainable Energy Reviews  ∎  ( ∎∎∎∎ )  ∎∎∎ – ∎∎∎ 4 Please cite this article as: Gourlis G, Kovacic I. Building Information Modelling for analysis of energy ef  󿬁 cient industrial buildings  –  Acase study. Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.02.009i
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