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BIM interface for full vs. semi-automated building energy simulation
Energy and Buildings 68 (2014) 671–678
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
BIM interface for full vs. semi-automated building energy simulation Ki-Uhn Ahn a , Young-Jin Kim a , Cheol-Soo Park a,∗ , Inhan Kim b , Keonho Lee c a Department of Architectural Engineering, College of Engineering, Sungkyunkwan University, Cheoncheon-Dong, Jangan-Gu, Suwon, Gyeonggi, Suwon 440-746, South Korea b Department of Architecture, College of Engineering, Kyung-Hee University, 1732 Deogyeong-daero, Giheung-Gu, Gyeonggi, Yongin 446-701, South Korea c Building Research Department, Korea Institute of Construction Technology, Daehwa-Dong 283, Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi, Goyang 4110712, South Korea
a r t i c l e
i n f o
Keywords: Building information modeling Industry foundation classes EnergyPlus Interface Sensitivity analysis
a b s t r a c t BIM (building information model) enables information sharing and reuse for interoperability between prevalent software tools in the AEC (Architecture, Engineering, and Construction) industry. Although a BIM based energy simulation tool can reduce costs and time required for building energy simulation work, no practical interface between CAD tools and dynamic energy analysis tools has been developed so far. With this in mind, this study suggests two approaches (Full automated interface (FAI) and semi automated interface (SAI)) enabling information transition from CAD tools (e.g., IFC) to EnergyPlus input file, IDF. FAI, if ideally developed, can convert IFC to IDF based on the use of pre-defined defaults without requiring human intervention. In contrast, SAI converts geometry information drawn from IFC to IDF and then require human data entry for uncertain simulation inputs. For this study, a library building was chosen and space boundary generated from ArchiCAD 13 was employed for geometry mapping. The Morris method, one of sensitivity analysis methods, was used for identifying significant inputs. In FAI and SAI, dominant inputs, out of the Morris method, were identified for Monte Carlo simulation to quantify probabilistic simulation outputs. In the paper, FAI and SAI simulation results are cross-compared, and pros and cons of FAI and SAI are discussed. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Building information modeling (BIM) has recently received much attention due to its potential use for energy simulation [1–6]. BIM-based energy simulation can reduce the significant costs and time required for geometry modeling. The industry foundation class (IFC) is one of the prevalent building information formats supporting BIM. The IFC is an object-oriented specification of the attributes of and relationships between building-related entities. However, against all expectations, the current IFC is not ready to include all information for the simulation used in a variety of domains. The information schema about detailed mechanical systems (e.g., specification, relationship, control logic, etc.) applied to dynamic simulation tools (including EnergyPlus) is still unstructured in the IFC format [3]. Moreover it is difficult to seamlessly convert IFC to a well-structured simulation information model due to simplifications and assumptions required for making the energy simulation models.
This study develops an IFC–IDF interface that converts the geometric information of IFC to IDF, an input file for EnergyPlus (Fig. 1). The current interface of the middleware type automatically maps the geometric information only to IDF for regular shaped buildings. Moreover this interface is supposed to support the automation of energy simulation. The IFC–IDF interface’s feature of automating simulation runs would improve the usability and accessibility for EnergyPlus users. The purpose of this study is to (1) accomplish automated conversion of geometric information within the interface and (2) investigate validity of such automated conversion and simulation. The Morris method, one of the sensitivity analysis methods, was adopted to identify the influence of each input. With the use of this method, insignificant inputs were set to defaults and did not appear on the interface screen. This study verifies whether the result from the simplified interface would be valuable for performance assessment in design process or not. Uncertainty analysis was employed to analyze the alternatives and verify the feasibility. 2. Development of IFC–IDF interface
∗ Corresponding author. Tel.: +82 31 290 7567; fax: +82 31 290 7570. E-mail addresses: [email protected] (K.-U. Ahn), [email protected] (Y.-J. Kim), [email protected], [email protected] (C.-S. Park), [email protected] (I. Kim), [email protected] (K. Lee). URL: http://bs.skku.ac.kr (C.-S. Park). 0378-7788/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2013.08.063
2.1. Geometric information and space boundary Different domain tools define their own data structures to represent required information. CAD software tools define objects in
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Fig. 1. Main screen of the IFC–IDF interface.
Fig. 2. Architectural and thermal view of a space and space boundary; (a) architectural view, (b) thermal view for energy simulation, (c) space boundary.
an architectural view. Fig. 2(a) shows the architectural view that represents exterior and interior shapes of walls composing a space. On the other hand, energy simulation tools (e.g., EnergyPlus) define the geometry in a thermal view (Fig. 2(b)). The thermal view subdivides a space defined by the architectural view into several zones for energy analysis. Energy simulation tools usually assume that direction of heat transfer in a building is perpendicular to the surface of each wall or roof (one-dimensional) and ignore two- or three-dimensional heat transfer phenomena. Therefore, the energy simulation tools simplify geometric information on a space into several two-dimensional planes (Fig. 2(b)). Meanwhile, BIM delivers relevant building information required for each domain tool with a single information framework. Space boundary plays a role as a link between the architectural and thermal views, as shown in Fig. 2(c). Space boundary defines boundaries for spaces and relationships between spaces and the building elements [3,6,7]. Space boundary can be broken down into the 1st level space boundary and the 2nd level space boundary [8]. The 1st level space boundary does not consider heat flow between adjacent spaces, and defines objects in the architectural view, while the second level space boundary defines those in the thermal view. In the 2nd level space boundary, a three-dimensional object in the 1st level space boundary is simplified into a two-dimensional plane in relation to adjacent spaces. Furthermore, the simplified plane is subdivided into several planes to account for heat flow between adjoining spaces. Fig. 3 illustrates the subdivision of a plane into multiple planes for the 2nd level
space boundary. Wall 1 (blue) that is defined as a three-dimensional object in the 1st level is redefined as multiple sub-planes in the 2nd level (Wall 1 =
+
+
+
+
). Please be noted that the
plane of Wall 1 that belongs to Space 3 is subdivided into , and
,
in order to account for the heat transfer between Space 1
and Space 3, and between Space 2 and Space 3. However, planes , , and of Wall 1 are omitted in the 2nd level space boundary since they do not involve any part of heat transfer between the spaces.
Fig. 3. 2nd level wall space boundary [1].
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related to the reference space, and (3) connection geometry: Geometric information about the relationship between the reference space and the elements. In particular, connection geometry uses Ifc connection surface geometry to store geometric information of the 2nd level space boundary. Ifc connection surface geometry represents the two-dimensional plane of the space boundary using (1) IfcAxis2Placement3D, which contains coordinates of the axis (Ifc Cartesian Point) for drawing the plane and direction (Ifc Direction), and (2) Ifc Composite Curve, which builds the plane polylines (Ifc Polyline) with the coordinates of the nodes (Ifc Cartesian Point). The IFC–IDF interface extracts only a subset relevant to IfcRelSpaceBoundary from the entire schema and converts it into geometric information of EnergyPlus.
3. Interface simulation 3.1. Full-automated and semi-automated interface
Fig. 4. Tree view of space boundary in IFC Explorer.
2.2. Converting geometry of IFC to IDF IFC [9] provides an open format to support interoperability. IFC compartmentalizes schemas for each domain and provides information delivery manual (IDM) and model view definition (MVD). IDM enhances efficient information sharing and MVD defines subschemas for IFC data format [3,9]. The IFC–IDF Interface employs IfcRelSpaceBoundary which represents the space boundary defined in MVD. The interface converts geometry information from ArchiCAD 13 to EnergyPlus. A space established by ‘Zone Tool’ in ArchiCAD 13 is used as ‘Zone’ in EnergyPlus. Fig. 4 shows a tree view of IfcRelSpaceBoundary in the IFC explorer and Fig. 5 shows an example of converting an IFC model created in ArchiCAD 13 to IDF using the IFC–IDF interface. IfcRelSpaceBoundary organizes the information about the space boundary using (1) relating space: Information about a reference space, (2) related building element: Information about elements
It is difficult to utilize energy simulation in the conventional design process due to tight deadlines of design, lack of information about input variables, and lack of in-depth knowledge about simulation. The cost and time required for simulation make it difficult to evaluate design alternatives and ongoing feedback. In addition, even the most user-friendly simulation tools require extensive inputs, in-depth expertise and know-how. These tools hinder accessibility of designers and intensify discrepancies between designers and engineers. Even engineers cannot be always confident in their own simulation results derived from Black Box models. Finally, simulation results obtained after spending a lot of time and money might become meaningless. With this in mind, the middleware type of IFC–IDF Interface was developed. This interface helps solve the aforementioned problem with two types of simulation, fully-automated and semi-automated, using the EnergyPlus 6.0 engine. • Full-automated Interface (FAI): For automatic simulation runs, geometry and material properties of IFC are mapped to IDF, and other simulation input variables except geometry and materials are set to default values. • Semi-automated Interface (SAI): Converting the geometry and material properties of IFC to IDF is the same as FAI. However, other simulation input variables except geometry and materials can be entered by users. FAI was intended for comparing design alternatives rather than predicting the absolute values. FAI has initial values (or defaults) for all input variables and performs simulation runs automatically. FAI can be regarded as a user-friendly interface. SAI, which requires human intervention, can yield more realistic results than FAI.
Fig. 5. Example of IFC to IDF; (a) 3D view in ArchiCAD, (b) converted to IDF (view in SketchUp).
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Fig. 6. Simulation model with interior and perimeter zones.
3.2. Simulation model The test model is a five-story building in which each floor consists of interior and perimeter zones (Fig. 6). An alternative that has lower cooling energy consumption during a summer day (August 21st) has to be chosen. Alternative 1(ALT 1) has a single chiller and uses variable air volume system for the interior zone and fan coil unit system for perimeter zones. Alternative 2 (ALT 2) adds an ice storage system as a heat source, and other mechanical systems are same as those in ALT 1. In this study, each alternative was simulated with the use of FAI as well as SAI. Through this process, results of FAI are verified by comparison to results of SAI. Please be noted that at this moment in time, this study assumes the IFC–IDF Interface perfectly supports automatic simulation runs without any technical problem. In other words, this study was undertaken on the premise that Branch, Nodes, and Setpoint of EnergyPlus are set automatically. This premise needs further investigation as a future study. In both FAI and SAI, geometric information and thermal properties are automatically mapped from IFC to IDF. For SAI simulation runs, an expert directly determined the capacity of the chiller and the ice storage system based on cooling load calculation from EnergyPlus. For selection and information of mechanical systems in SAI, the authors referred to a manufacturer’s catalog [10] where detailed specifications of the systems were provided. Such detailed data of the systems were used as the SAI input variables. On the other hand, in FAI simulation runs, ‘autosize’ or ‘default’ of EnergyPlus was used because it is what FAI was intended for. The process of detecting influence of each input variable will be described in the next section.
4. Morris analysis The Morris method has been widely adopted for sensitivity analysis to identify the influence of input variables [11–13]. The mean () is used to detect input variables that have significant influence on the output, and the standard deviation () is used to detect nonlinearity of input variables’ influence on the output. Both can be displayed in a single Cartesian coordinate (X–Y) plane to facilitate intuitive understanding of the model [13,14]. In this study, the Morris method was conducted for 51 input variables (Table 1). The variation range of each input variable is set
to ±10%. Table 1 shows the list of input variables entered in the Morris method and the results. Be noted that building geometric information and thermal properties of materials were not tested in the Morris method since those were automatically mapped from IFC to IDF. As shown in Table 1, 25 inputs in ALT 1 and 19 inputs in ALT 2 were identified as insignificant input variables (marked as X in Table 1). In uncertainty analysis which will be addressed in the next section, the insignificant inputs were excluded. Fig. 7 shows the results of the Morris method in Cartesian coordinates. The closer input variables are to the coordinate (0, 0), the less influence they have on cooling energy consumption. If both of the mean () and standard deviation () of input variables are less than 0.0, then they are considered insignificant to the output [13].
5. Uncertainty analysis 5.1. Latin hypercube sampling (LHS) There are two distinct simulation approaches in evaluating building energy performance: deterministic vs. stochastic. The deterministic approach, which has been popularly taken for simulation studies, is that each input variable is set as a single value, and a definitive single output is derived. However, building’s energy consumption is of strong stochastic nature. The stochastic approach considers probability distribution of input variables and produces output as a probability density function. This approach is more objective, transparent, reliable, reproducible and rational than the deterministic approach [11,12,15–17]. In this study, the Latin hypercube sampling (LHS), one of the Monte Carlo techniques, was used for uncertainty propagation. The LHS provides good coverage of the parameter space with relatively few samples compared to the standard brute force random sampling [18]. The LHS method has proved suitable for complex nonlinear models and has been demonstrated in many building simulation studies [19]. According to the Morris results (Table 1), there are 26 (31 − 5 = 26) significant input variables in ALT 1. In ALT 2, there are 32 (51 − 19 = 32) significant variables. A total of 50 simulation cases were propagated for ALT 1 and ALT 2 in FAI and SAI respectively using SimLab 2.2 [20]. The number of generated simulation cases is more than the minimum number, or 4k/3 [21] (for ALT1, 4 × 26/3 = 34.7; for ALT 2, 4 × 32/3 = 42.7 where k is the number
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Table 1 List of input variables for the Morris method and results (O: of significant influence, X: of insignificant influence, D/U: Default used in FAI, User’s entry in SAI). EnergyPlus class
ID
EnergyPlus field
ALT 1
ALT 2
Building
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
Loads convergence tolerance value Temperature convergence tolerance value Surface temperature upper limit People per zone floor area Activity level Fraction radiant Watts per zone floor area Fraction radiant Watts per zone floor area Fraction radiant Air changes per hour Cooling design supply air temperature Heating design supply air temperature Cooling design supply air humidity ratio Heating design supply air humidity ratio Outdoor air flow per person Minimum system air flow ratio Preheat design temperature Preheat design humidity ratio Precool design temperature Precool design humidity ratio Cooling design supply air temperature Heating design supply air temperature Cooling design supply air humidity ratio Heating design supply air humidity ratio Cooling convergence tolerance Heating convergence tolerance Constant minimum air flow fraction Convergence tolerance Fan efficiency Pressure rise Motor efficiency Fan efficiency Pressure rise Fan power minimum flow fraction Motor efficiency Rated inlet water temperature Rated inlet air temperature Rated outlet water temperature Rated outlet air temperature Rated ratio for air and water convection Rated pump head Motor efficiency Nominal thermal efficiency Sizing factor Reference COP Sizing factor Air loop setpoint Condenser loop setpoint Cool water loop setpoint Hot water loop setpoint
O O X O O O O O O O O O O O X O X X X X X O X O X O X O X O O X O O X X X X X X X O X X X O O X O X X
O O X O O O O O O O O O O O X O O X X X X O X O X O X O X O O X O O X X O O X X X O X X O O O O O O X
Heat balance algorithm People
Lights Electric equipment Zone infiltration Sizing: zone
Sizing: system
Zone HVAC: four pipe fan coil Single duct: VAV: reheat Fan: constant volume
Fan: variable volume
Coil:heating:water
Pump: variable speed Boiler: hot water Chiller:electric:EIR Setpointmanager: scheduled
of input variables). Finally, a total of 200 simulations were run (50 cases for each of the following: ALT1 in FAI, ALT2 in FAI, ALT1 in SAI, ALT2 in SAI) for the analysis to be described in the next section. 5.2. Results of uncertainty analysis The results of two approaches are compared in Table 2. Since the variation range of simulation results drawn from the stochastic Table 2 Simulation results from two approaches (Unit: GJ). Approach
Deterministic approach Stochastic approach
FAI
Min Mean Max Std.
SAI
ALT 1
ALT 2
ALT 1
ALT 2
757 600 758 1011 81
899 718 906 1126 90
966 899 967 1026 29
909 831 922 983 32
Remark
D/U
D/U D/U
D/U D/U D/U D/U D/U D/U
approach is significant (please refer to the minimum and maximum values), it seems inappropriate to deliver simulation prediction as a single definite number (‘deterministic approach’). With this in mind, such deterministic approach would not be regarded as ‘objective, transparent, reliable and rational’ prediction. It is noteworthy that the uncertainty range of FAI is greater than that of SAI (Table 2, Fig. 8). This is because several input variables in SAI are entered by the users. However, if SAI is not guided with a standardized simulation process, SAI will also result in significant uncertainty. In other words, subjective judgment and assumptions are indispensable during the simulation process and simulation uncertainty can be accordingly triggered by different expertise, knowledge and technicalities of each simulationist. In addition, please be noted that FAI and SAI provide different evaluation between ALT 1 vs. ALT 2. In FAI, ALT 1 is advantageous, while it is in reverse in SAI (Table 2, Fig. 8). Fig. 9 shows the cumulative probability curves of FAI and SAI. If decision making criterion is to choose an alternative with higher probability of energy consumption under 950 (GJ), ALT 1 is superior
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Fig. 8. Probability density functions of FAI and SAI.
Fig. 9. Cumulative probability of FAI and SAI. Fig. 7. Results of Morris method; (a) ALT 1, (b) ALT 2.
to ALT 2 by 20% in FAI (in order to read 20%, refer to Y-axis). However, in SAI, ALT 2 is more advantageous by 50% than ALT 1. In addition, the decision making between ALT 1 and ALT 2 is more obvious in SAI compared to FAI (50% vs. 20%). In can be inferred that FAI is easy to use because of being ‘fully automated’ but may lead to false conclusions. In light of rational decision making perspective, SAI becomes more suitable than FAI. However, this does not guarantee that SAI always delivers better depiction of system evaluation. As mentioned earlier, if the user is not knowledgeable about simulation judgement and assumptions in SAI, the SAI results will be same as FAI leading simulation prediction to strong uncertainty. In FAI, the capacity of mechanical equipment was autosized based on EnergyPlus load calculation. In SAI, the capacity was determined by the user who referred to a manufacturer’s catalog to
obtain required information (Table 3). Careful attention should be paid when FAI is used. The control logic of sequential load distribution for the ice storage system is as follows: If cooling demand becomes greater than the capacity of the chiller, the chiller draws cold from the ice storage system. Thus, the capacity of the chiller in ALT 2 doesn’t have to be as large as that in ALT 1 since it allows load shifting, which means that it moves cooling demand in daytime to nighttime. However, in FAI, the chiller capacity of ALT 2 was set to that of ALT 1 because the effect of load shifting couldn’t be customized in FAI (Table 3). This made the significant difference in simulation prediction between FAI and SAI. The Box Plots of FAI and SAI were also used to infer to what makes the difference in predicting energy consumption between FAI and SAI (Fig. 10). As shown in Fig. 10, cooling energy and heat rejection from the cooling tower are the main causes of the
Table 3 Comparison between autosized and manual inputs (FAI vs. SAI). Input variables
Determined by
Chiller capacity [kW]
Autosizing in EnergyPlus User’s entry Autosizing in EnergyPlus User’s entry Autosizing in EnergyPlus User’s entry
Chilled water flow rate [m3 /s] Condenser fluid flow rate [m3 /s]
SAI
FAI ALT 1
ALT 2
ALT 1
ALT 2
1615 N/A 0.09659 N/A 0.09273 N/A
1615 N/A 0.09659 N/A 0.09273 N/A
N/A 1980 N/A 0.128 N/A 0.157
N/A 1400 N/A 0.126 N/A 0.150
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properties drawn from IFC, which could save significant time and efforts in structuring a geometric model in EnergyPlus. FAI was originally intended for easy comparison between alternatives without requiring expert’s intervention. For input variables other than geometry and thermal properties, FAI relies on ‘autosize’ or ‘default’ in EnergyPlus. This could cause significant variation in simulation prediction and harm objectivity and reliability of simulation output. SAI requires the user’s entry for input variables without using ‘autosize’ or ‘default’ in EnergyPlus. SAI is more reliable, transparent and quality-assuring than FAI since SAI allows the user to enter more accurate input variables and to customize control logic of innovative energy-saving systems if needed. The further study is being sought as follows: • Conversion of irregular geometry: IFC does not support circular, inclined or curved plane of walls or roofs. The conversion of such data is necessary for energy simulation and standard guidelines are being studied. • Verification of FAI and SAI: Consistency in comparison between different design alternatives will be tested for various mechanical and HVAC systems.
Acknowledgment This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-0008717).
References
Fig. 10. Box plots of FAI and SAI; (a) ALT 1, (b) ALT 2.
difference between FAI and SAI. In FAI, the capacity of the chiller was autosized and control logic of sequential load distribution for the ice storage system was set to default. SAI has less variation in results than FAI, because in SAI, the chiller capacity, chilled water flow rate, condenser fluid flow rate and control logic for the ice storage were customized by the user (Table 3). As a result, the variation by cooling and heat rejection in SAI is significantly less than those in FAI (Fig. 10). Accordingly, it can be inferred that FAI may have greater variation in predicting building energy consumption than SAI. It should be kept in mind that relevant information is not always provided in architectural drawings and specifications. As a result, unknown inputs had to be determined by subjective judgment of engineers or set to ‘autosize’ in EnergyPlus. This could increase uncertainty of simulation prediction which building stakeholders need to be aware of.
6. Conclusions The IFC–IDF Interface was developed in this study. At this moment in time, it is difficult to make a complete simulation model for EnergyPlus using interoperability of IFC. For this reason, FAI and SAI approaches were taken as a bypass solution. Both of FAI and SAI make the best use of geometric information and thermal
[1] R. See, P. Haves, P. Sreekanthan, J. O’Donnell, M. Basarkar, K. Settlemyre, Development of a user interface for the EnergyPlus whole building energy simulation program, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [2] J. O’Donnell, R. See, C.M. Rose, V. Bazjanac, P. Haves, SimModel. A domain data model for whole building energy simulation, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [3] R.J. Hitchcock, J. Wong, Transforming IFC architectural view BIMs for energy simulation, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [4] V. Bazjanac, T. Mail, IFC HVAC interface to EnergyPlus—a case of expanded interoperability for energy simulation, in: Proceedings of the SimBuild 2004 Conference, Colorado, USA, 2004. [5] V. Bazjanac, Implementation of semi-automated energy performance simulation: building geometry, in: A. Dikbas, E. Ergen, H. Giritli (Eds.), Proceedings of the 26th Conference, Managing IT in Construction, Istanbul, Turkey, 2009, pp. 595–602, CIB W78. [6] V. Bazjanac, IFC BIM-based methodology for semi-automated building energy performance simulation, in: Proceedings of the 25th International Conference on Information Technology in Construction, Santiago, Chile, 2008, CIB W78. [7] M. Weise, T. Liebich, R. See, V. Bazjanac, T. Laine, B. Welle, Implementation Guide Space Boundaries for Energy Analysis, in: US General Services Administration (GSA) and Open Geospatial Consortium (OGC), 2012 http://www.blisproject.org/IAI-MVD/documents/Space Boundaries for Energy Analysis v1.pdf (accessed on November 2012). [8] US. General Services Administration (GSA), GSA Building Information Modeling Guide Series, in: 05-GSA BIM Guide for Energy Performance, 2009. [9] Building SMART, 2012, http://www.buildingsmart.com (accessed on November 2012). [10] Bumyang Air Conditioning, 2012, http://www.bac.co.kr/index.html (accessed on November 2012, in Korean). [11] S. De Wit, Uncertainty in predictions of thermal comfort in buildings, in: Doctoral Dissertation, Delft University of Technology, Delft, The Netherlands, 2001. [12] S. De Wit, G. Augenbroe, Analysis of uncertainty in building design evaluations and its implications, Energy and Buildings 34 (9) (2002) 951–958. [13] P. Heiselberg, H. Brohus, A. Hesselholt, H. Rasmussen, E. Seinre, S. Thomas, Application of sensitivity analysis in design of sustainable buildings, Renewable Energy 34 (9) (2009) 2030–2036. [14] A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, John Wiley & Sons Publishers, Hoboken, NJ, 2004. [15] I.A. Macdonald, Quantifying the Effects of Uncertainty in Building Simulation, in: Ph.D. Thesis, University of Strathclyde, 2002.
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[16] C.J. Hopfe, Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design Optimization, in: Ph.D. Thesis, Technische Universiteit Eindhoven, The Netherlands, 2009. [17] C. Struck, J. Hensen, On supporting design decisions in conceptual design addressing specification uncertainties using performance simulation, in: Proceedings of the 10th International IBPSA Conference, Beijing, China, 2007, pp. 1434–1439. [18] G.D. Wyss, K.H. Jorgensen, A User’s Guide to LHS: Sandia’s Latin Hypercube Sampling Software, Sandia National Laboratories, Albuquerque, New Mexico, US, 1998.
[19] IBPSA, The international building performance simulation association, in: Proceedings of the 7th to 12th International IBPSA Conference, 2001. [20] SimLab, 2012, http://simlab.jrc.ec.europa.eu (accessed on November 2012). [21] R.L. Iman, J.C. Helton, A Comparison of Uncertainty and Sensitivity Techniques for Computer Models, in: International Report NUREG ICR-3904, SAND 84-1461, Sandia National Laboratories, Albuquerque, New Mexico, USA, 1985.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
BIM interface for full vs. semi-automated building energy simulation Ki-Uhn Ahn a , Young-Jin Kim a , Cheol-Soo Park a,∗ , Inhan Kim b , Keonho Lee c a Department of Architectural Engineering, College of Engineering, Sungkyunkwan University, Cheoncheon-Dong, Jangan-Gu, Suwon, Gyeonggi, Suwon 440-746, South Korea b Department of Architecture, College of Engineering, Kyung-Hee University, 1732 Deogyeong-daero, Giheung-Gu, Gyeonggi, Yongin 446-701, South Korea c Building Research Department, Korea Institute of Construction Technology, Daehwa-Dong 283, Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi, Goyang 4110712, South Korea
a r t i c l e
i n f o
Keywords: Building information modeling Industry foundation classes EnergyPlus Interface Sensitivity analysis
a b s t r a c t BIM (building information model) enables information sharing and reuse for interoperability between prevalent software tools in the AEC (Architecture, Engineering, and Construction) industry. Although a BIM based energy simulation tool can reduce costs and time required for building energy simulation work, no practical interface between CAD tools and dynamic energy analysis tools has been developed so far. With this in mind, this study suggests two approaches (Full automated interface (FAI) and semi automated interface (SAI)) enabling information transition from CAD tools (e.g., IFC) to EnergyPlus input file, IDF. FAI, if ideally developed, can convert IFC to IDF based on the use of pre-defined defaults without requiring human intervention. In contrast, SAI converts geometry information drawn from IFC to IDF and then require human data entry for uncertain simulation inputs. For this study, a library building was chosen and space boundary generated from ArchiCAD 13 was employed for geometry mapping. The Morris method, one of sensitivity analysis methods, was used for identifying significant inputs. In FAI and SAI, dominant inputs, out of the Morris method, were identified for Monte Carlo simulation to quantify probabilistic simulation outputs. In the paper, FAI and SAI simulation results are cross-compared, and pros and cons of FAI and SAI are discussed. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Building information modeling (BIM) has recently received much attention due to its potential use for energy simulation [1–6]. BIM-based energy simulation can reduce the significant costs and time required for geometry modeling. The industry foundation class (IFC) is one of the prevalent building information formats supporting BIM. The IFC is an object-oriented specification of the attributes of and relationships between building-related entities. However, against all expectations, the current IFC is not ready to include all information for the simulation used in a variety of domains. The information schema about detailed mechanical systems (e.g., specification, relationship, control logic, etc.) applied to dynamic simulation tools (including EnergyPlus) is still unstructured in the IFC format [3]. Moreover it is difficult to seamlessly convert IFC to a well-structured simulation information model due to simplifications and assumptions required for making the energy simulation models.
This study develops an IFC–IDF interface that converts the geometric information of IFC to IDF, an input file for EnergyPlus (Fig. 1). The current interface of the middleware type automatically maps the geometric information only to IDF for regular shaped buildings. Moreover this interface is supposed to support the automation of energy simulation. The IFC–IDF interface’s feature of automating simulation runs would improve the usability and accessibility for EnergyPlus users. The purpose of this study is to (1) accomplish automated conversion of geometric information within the interface and (2) investigate validity of such automated conversion and simulation. The Morris method, one of the sensitivity analysis methods, was adopted to identify the influence of each input. With the use of this method, insignificant inputs were set to defaults and did not appear on the interface screen. This study verifies whether the result from the simplified interface would be valuable for performance assessment in design process or not. Uncertainty analysis was employed to analyze the alternatives and verify the feasibility. 2. Development of IFC–IDF interface
∗ Corresponding author. Tel.: +82 31 290 7567; fax: +82 31 290 7570. E-mail addresses: [email protected] (K.-U. Ahn), [email protected] (Y.-J. Kim), [email protected], [email protected] (C.-S. Park), [email protected] (I. Kim), [email protected] (K. Lee). URL: http://bs.skku.ac.kr (C.-S. Park). 0378-7788/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2013.08.063
2.1. Geometric information and space boundary Different domain tools define their own data structures to represent required information. CAD software tools define objects in
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Fig. 1. Main screen of the IFC–IDF interface.
Fig. 2. Architectural and thermal view of a space and space boundary; (a) architectural view, (b) thermal view for energy simulation, (c) space boundary.
an architectural view. Fig. 2(a) shows the architectural view that represents exterior and interior shapes of walls composing a space. On the other hand, energy simulation tools (e.g., EnergyPlus) define the geometry in a thermal view (Fig. 2(b)). The thermal view subdivides a space defined by the architectural view into several zones for energy analysis. Energy simulation tools usually assume that direction of heat transfer in a building is perpendicular to the surface of each wall or roof (one-dimensional) and ignore two- or three-dimensional heat transfer phenomena. Therefore, the energy simulation tools simplify geometric information on a space into several two-dimensional planes (Fig. 2(b)). Meanwhile, BIM delivers relevant building information required for each domain tool with a single information framework. Space boundary plays a role as a link between the architectural and thermal views, as shown in Fig. 2(c). Space boundary defines boundaries for spaces and relationships between spaces and the building elements [3,6,7]. Space boundary can be broken down into the 1st level space boundary and the 2nd level space boundary [8]. The 1st level space boundary does not consider heat flow between adjacent spaces, and defines objects in the architectural view, while the second level space boundary defines those in the thermal view. In the 2nd level space boundary, a three-dimensional object in the 1st level space boundary is simplified into a two-dimensional plane in relation to adjacent spaces. Furthermore, the simplified plane is subdivided into several planes to account for heat flow between adjoining spaces. Fig. 3 illustrates the subdivision of a plane into multiple planes for the 2nd level
space boundary. Wall 1 (blue) that is defined as a three-dimensional object in the 1st level is redefined as multiple sub-planes in the 2nd level (Wall 1 =
+
+
+
+
). Please be noted that the
plane of Wall 1 that belongs to Space 3 is subdivided into , and
,
in order to account for the heat transfer between Space 1
and Space 3, and between Space 2 and Space 3. However, planes , , and of Wall 1 are omitted in the 2nd level space boundary since they do not involve any part of heat transfer between the spaces.
Fig. 3. 2nd level wall space boundary [1].
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related to the reference space, and (3) connection geometry: Geometric information about the relationship between the reference space and the elements. In particular, connection geometry uses Ifc connection surface geometry to store geometric information of the 2nd level space boundary. Ifc connection surface geometry represents the two-dimensional plane of the space boundary using (1) IfcAxis2Placement3D, which contains coordinates of the axis (Ifc Cartesian Point) for drawing the plane and direction (Ifc Direction), and (2) Ifc Composite Curve, which builds the plane polylines (Ifc Polyline) with the coordinates of the nodes (Ifc Cartesian Point). The IFC–IDF interface extracts only a subset relevant to IfcRelSpaceBoundary from the entire schema and converts it into geometric information of EnergyPlus.
3. Interface simulation 3.1. Full-automated and semi-automated interface
Fig. 4. Tree view of space boundary in IFC Explorer.
2.2. Converting geometry of IFC to IDF IFC [9] provides an open format to support interoperability. IFC compartmentalizes schemas for each domain and provides information delivery manual (IDM) and model view definition (MVD). IDM enhances efficient information sharing and MVD defines subschemas for IFC data format [3,9]. The IFC–IDF Interface employs IfcRelSpaceBoundary which represents the space boundary defined in MVD. The interface converts geometry information from ArchiCAD 13 to EnergyPlus. A space established by ‘Zone Tool’ in ArchiCAD 13 is used as ‘Zone’ in EnergyPlus. Fig. 4 shows a tree view of IfcRelSpaceBoundary in the IFC explorer and Fig. 5 shows an example of converting an IFC model created in ArchiCAD 13 to IDF using the IFC–IDF interface. IfcRelSpaceBoundary organizes the information about the space boundary using (1) relating space: Information about a reference space, (2) related building element: Information about elements
It is difficult to utilize energy simulation in the conventional design process due to tight deadlines of design, lack of information about input variables, and lack of in-depth knowledge about simulation. The cost and time required for simulation make it difficult to evaluate design alternatives and ongoing feedback. In addition, even the most user-friendly simulation tools require extensive inputs, in-depth expertise and know-how. These tools hinder accessibility of designers and intensify discrepancies between designers and engineers. Even engineers cannot be always confident in their own simulation results derived from Black Box models. Finally, simulation results obtained after spending a lot of time and money might become meaningless. With this in mind, the middleware type of IFC–IDF Interface was developed. This interface helps solve the aforementioned problem with two types of simulation, fully-automated and semi-automated, using the EnergyPlus 6.0 engine. • Full-automated Interface (FAI): For automatic simulation runs, geometry and material properties of IFC are mapped to IDF, and other simulation input variables except geometry and materials are set to default values. • Semi-automated Interface (SAI): Converting the geometry and material properties of IFC to IDF is the same as FAI. However, other simulation input variables except geometry and materials can be entered by users. FAI was intended for comparing design alternatives rather than predicting the absolute values. FAI has initial values (or defaults) for all input variables and performs simulation runs automatically. FAI can be regarded as a user-friendly interface. SAI, which requires human intervention, can yield more realistic results than FAI.
Fig. 5. Example of IFC to IDF; (a) 3D view in ArchiCAD, (b) converted to IDF (view in SketchUp).
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Fig. 6. Simulation model with interior and perimeter zones.
3.2. Simulation model The test model is a five-story building in which each floor consists of interior and perimeter zones (Fig. 6). An alternative that has lower cooling energy consumption during a summer day (August 21st) has to be chosen. Alternative 1(ALT 1) has a single chiller and uses variable air volume system for the interior zone and fan coil unit system for perimeter zones. Alternative 2 (ALT 2) adds an ice storage system as a heat source, and other mechanical systems are same as those in ALT 1. In this study, each alternative was simulated with the use of FAI as well as SAI. Through this process, results of FAI are verified by comparison to results of SAI. Please be noted that at this moment in time, this study assumes the IFC–IDF Interface perfectly supports automatic simulation runs without any technical problem. In other words, this study was undertaken on the premise that Branch, Nodes, and Setpoint of EnergyPlus are set automatically. This premise needs further investigation as a future study. In both FAI and SAI, geometric information and thermal properties are automatically mapped from IFC to IDF. For SAI simulation runs, an expert directly determined the capacity of the chiller and the ice storage system based on cooling load calculation from EnergyPlus. For selection and information of mechanical systems in SAI, the authors referred to a manufacturer’s catalog [10] where detailed specifications of the systems were provided. Such detailed data of the systems were used as the SAI input variables. On the other hand, in FAI simulation runs, ‘autosize’ or ‘default’ of EnergyPlus was used because it is what FAI was intended for. The process of detecting influence of each input variable will be described in the next section.
4. Morris analysis The Morris method has been widely adopted for sensitivity analysis to identify the influence of input variables [11–13]. The mean () is used to detect input variables that have significant influence on the output, and the standard deviation () is used to detect nonlinearity of input variables’ influence on the output. Both can be displayed in a single Cartesian coordinate (X–Y) plane to facilitate intuitive understanding of the model [13,14]. In this study, the Morris method was conducted for 51 input variables (Table 1). The variation range of each input variable is set
to ±10%. Table 1 shows the list of input variables entered in the Morris method and the results. Be noted that building geometric information and thermal properties of materials were not tested in the Morris method since those were automatically mapped from IFC to IDF. As shown in Table 1, 25 inputs in ALT 1 and 19 inputs in ALT 2 were identified as insignificant input variables (marked as X in Table 1). In uncertainty analysis which will be addressed in the next section, the insignificant inputs were excluded. Fig. 7 shows the results of the Morris method in Cartesian coordinates. The closer input variables are to the coordinate (0, 0), the less influence they have on cooling energy consumption. If both of the mean () and standard deviation () of input variables are less than 0.0, then they are considered insignificant to the output [13].
5. Uncertainty analysis 5.1. Latin hypercube sampling (LHS) There are two distinct simulation approaches in evaluating building energy performance: deterministic vs. stochastic. The deterministic approach, which has been popularly taken for simulation studies, is that each input variable is set as a single value, and a definitive single output is derived. However, building’s energy consumption is of strong stochastic nature. The stochastic approach considers probability distribution of input variables and produces output as a probability density function. This approach is more objective, transparent, reliable, reproducible and rational than the deterministic approach [11,12,15–17]. In this study, the Latin hypercube sampling (LHS), one of the Monte Carlo techniques, was used for uncertainty propagation. The LHS provides good coverage of the parameter space with relatively few samples compared to the standard brute force random sampling [18]. The LHS method has proved suitable for complex nonlinear models and has been demonstrated in many building simulation studies [19]. According to the Morris results (Table 1), there are 26 (31 − 5 = 26) significant input variables in ALT 1. In ALT 2, there are 32 (51 − 19 = 32) significant variables. A total of 50 simulation cases were propagated for ALT 1 and ALT 2 in FAI and SAI respectively using SimLab 2.2 [20]. The number of generated simulation cases is more than the minimum number, or 4k/3 [21] (for ALT1, 4 × 26/3 = 34.7; for ALT 2, 4 × 32/3 = 42.7 where k is the number
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Table 1 List of input variables for the Morris method and results (O: of significant influence, X: of insignificant influence, D/U: Default used in FAI, User’s entry in SAI). EnergyPlus class
ID
EnergyPlus field
ALT 1
ALT 2
Building
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
Loads convergence tolerance value Temperature convergence tolerance value Surface temperature upper limit People per zone floor area Activity level Fraction radiant Watts per zone floor area Fraction radiant Watts per zone floor area Fraction radiant Air changes per hour Cooling design supply air temperature Heating design supply air temperature Cooling design supply air humidity ratio Heating design supply air humidity ratio Outdoor air flow per person Minimum system air flow ratio Preheat design temperature Preheat design humidity ratio Precool design temperature Precool design humidity ratio Cooling design supply air temperature Heating design supply air temperature Cooling design supply air humidity ratio Heating design supply air humidity ratio Cooling convergence tolerance Heating convergence tolerance Constant minimum air flow fraction Convergence tolerance Fan efficiency Pressure rise Motor efficiency Fan efficiency Pressure rise Fan power minimum flow fraction Motor efficiency Rated inlet water temperature Rated inlet air temperature Rated outlet water temperature Rated outlet air temperature Rated ratio for air and water convection Rated pump head Motor efficiency Nominal thermal efficiency Sizing factor Reference COP Sizing factor Air loop setpoint Condenser loop setpoint Cool water loop setpoint Hot water loop setpoint
O O X O O O O O O O O O O O X O X X X X X O X O X O X O X O O X O O X X X X X X X O X X X O O X O X X
O O X O O O O O O O O O O O X O O X X X X O X O X O X O X O O X O O X X O O X X X O X X O O O O O O X
Heat balance algorithm People
Lights Electric equipment Zone infiltration Sizing: zone
Sizing: system
Zone HVAC: four pipe fan coil Single duct: VAV: reheat Fan: constant volume
Fan: variable volume
Coil:heating:water
Pump: variable speed Boiler: hot water Chiller:electric:EIR Setpointmanager: scheduled
of input variables). Finally, a total of 200 simulations were run (50 cases for each of the following: ALT1 in FAI, ALT2 in FAI, ALT1 in SAI, ALT2 in SAI) for the analysis to be described in the next section. 5.2. Results of uncertainty analysis The results of two approaches are compared in Table 2. Since the variation range of simulation results drawn from the stochastic Table 2 Simulation results from two approaches (Unit: GJ). Approach
Deterministic approach Stochastic approach
FAI
Min Mean Max Std.
SAI
ALT 1
ALT 2
ALT 1
ALT 2
757 600 758 1011 81
899 718 906 1126 90
966 899 967 1026 29
909 831 922 983 32
Remark
D/U
D/U D/U
D/U D/U D/U D/U D/U D/U
approach is significant (please refer to the minimum and maximum values), it seems inappropriate to deliver simulation prediction as a single definite number (‘deterministic approach’). With this in mind, such deterministic approach would not be regarded as ‘objective, transparent, reliable and rational’ prediction. It is noteworthy that the uncertainty range of FAI is greater than that of SAI (Table 2, Fig. 8). This is because several input variables in SAI are entered by the users. However, if SAI is not guided with a standardized simulation process, SAI will also result in significant uncertainty. In other words, subjective judgment and assumptions are indispensable during the simulation process and simulation uncertainty can be accordingly triggered by different expertise, knowledge and technicalities of each simulationist. In addition, please be noted that FAI and SAI provide different evaluation between ALT 1 vs. ALT 2. In FAI, ALT 1 is advantageous, while it is in reverse in SAI (Table 2, Fig. 8). Fig. 9 shows the cumulative probability curves of FAI and SAI. If decision making criterion is to choose an alternative with higher probability of energy consumption under 950 (GJ), ALT 1 is superior
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Fig. 8. Probability density functions of FAI and SAI.
Fig. 9. Cumulative probability of FAI and SAI. Fig. 7. Results of Morris method; (a) ALT 1, (b) ALT 2.
to ALT 2 by 20% in FAI (in order to read 20%, refer to Y-axis). However, in SAI, ALT 2 is more advantageous by 50% than ALT 1. In addition, the decision making between ALT 1 and ALT 2 is more obvious in SAI compared to FAI (50% vs. 20%). In can be inferred that FAI is easy to use because of being ‘fully automated’ but may lead to false conclusions. In light of rational decision making perspective, SAI becomes more suitable than FAI. However, this does not guarantee that SAI always delivers better depiction of system evaluation. As mentioned earlier, if the user is not knowledgeable about simulation judgement and assumptions in SAI, the SAI results will be same as FAI leading simulation prediction to strong uncertainty. In FAI, the capacity of mechanical equipment was autosized based on EnergyPlus load calculation. In SAI, the capacity was determined by the user who referred to a manufacturer’s catalog to
obtain required information (Table 3). Careful attention should be paid when FAI is used. The control logic of sequential load distribution for the ice storage system is as follows: If cooling demand becomes greater than the capacity of the chiller, the chiller draws cold from the ice storage system. Thus, the capacity of the chiller in ALT 2 doesn’t have to be as large as that in ALT 1 since it allows load shifting, which means that it moves cooling demand in daytime to nighttime. However, in FAI, the chiller capacity of ALT 2 was set to that of ALT 1 because the effect of load shifting couldn’t be customized in FAI (Table 3). This made the significant difference in simulation prediction between FAI and SAI. The Box Plots of FAI and SAI were also used to infer to what makes the difference in predicting energy consumption between FAI and SAI (Fig. 10). As shown in Fig. 10, cooling energy and heat rejection from the cooling tower are the main causes of the
Table 3 Comparison between autosized and manual inputs (FAI vs. SAI). Input variables
Determined by
Chiller capacity [kW]
Autosizing in EnergyPlus User’s entry Autosizing in EnergyPlus User’s entry Autosizing in EnergyPlus User’s entry
Chilled water flow rate [m3 /s] Condenser fluid flow rate [m3 /s]
SAI
FAI ALT 1
ALT 2
ALT 1
ALT 2
1615 N/A 0.09659 N/A 0.09273 N/A
1615 N/A 0.09659 N/A 0.09273 N/A
N/A 1980 N/A 0.128 N/A 0.157
N/A 1400 N/A 0.126 N/A 0.150
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properties drawn from IFC, which could save significant time and efforts in structuring a geometric model in EnergyPlus. FAI was originally intended for easy comparison between alternatives without requiring expert’s intervention. For input variables other than geometry and thermal properties, FAI relies on ‘autosize’ or ‘default’ in EnergyPlus. This could cause significant variation in simulation prediction and harm objectivity and reliability of simulation output. SAI requires the user’s entry for input variables without using ‘autosize’ or ‘default’ in EnergyPlus. SAI is more reliable, transparent and quality-assuring than FAI since SAI allows the user to enter more accurate input variables and to customize control logic of innovative energy-saving systems if needed. The further study is being sought as follows: • Conversion of irregular geometry: IFC does not support circular, inclined or curved plane of walls or roofs. The conversion of such data is necessary for energy simulation and standard guidelines are being studied. • Verification of FAI and SAI: Consistency in comparison between different design alternatives will be tested for various mechanical and HVAC systems.
Acknowledgment This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-0008717).
References
Fig. 10. Box plots of FAI and SAI; (a) ALT 1, (b) ALT 2.
difference between FAI and SAI. In FAI, the capacity of the chiller was autosized and control logic of sequential load distribution for the ice storage system was set to default. SAI has less variation in results than FAI, because in SAI, the chiller capacity, chilled water flow rate, condenser fluid flow rate and control logic for the ice storage were customized by the user (Table 3). As a result, the variation by cooling and heat rejection in SAI is significantly less than those in FAI (Fig. 10). Accordingly, it can be inferred that FAI may have greater variation in predicting building energy consumption than SAI. It should be kept in mind that relevant information is not always provided in architectural drawings and specifications. As a result, unknown inputs had to be determined by subjective judgment of engineers or set to ‘autosize’ in EnergyPlus. This could increase uncertainty of simulation prediction which building stakeholders need to be aware of.
6. Conclusions The IFC–IDF Interface was developed in this study. At this moment in time, it is difficult to make a complete simulation model for EnergyPlus using interoperability of IFC. For this reason, FAI and SAI approaches were taken as a bypass solution. Both of FAI and SAI make the best use of geometric information and thermal
[1] R. See, P. Haves, P. Sreekanthan, J. O’Donnell, M. Basarkar, K. Settlemyre, Development of a user interface for the EnergyPlus whole building energy simulation program, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [2] J. O’Donnell, R. See, C.M. Rose, V. Bazjanac, P. Haves, SimModel. A domain data model for whole building energy simulation, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [3] R.J. Hitchcock, J. Wong, Transforming IFC architectural view BIMs for energy simulation, in: Proceedings of the 12th International IBPSA Conference, Sydney, Australia, 2011. [4] V. Bazjanac, T. Mail, IFC HVAC interface to EnergyPlus—a case of expanded interoperability for energy simulation, in: Proceedings of the SimBuild 2004 Conference, Colorado, USA, 2004. [5] V. Bazjanac, Implementation of semi-automated energy performance simulation: building geometry, in: A. Dikbas, E. Ergen, H. Giritli (Eds.), Proceedings of the 26th Conference, Managing IT in Construction, Istanbul, Turkey, 2009, pp. 595–602, CIB W78. [6] V. Bazjanac, IFC BIM-based methodology for semi-automated building energy performance simulation, in: Proceedings of the 25th International Conference on Information Technology in Construction, Santiago, Chile, 2008, CIB W78. [7] M. Weise, T. Liebich, R. See, V. Bazjanac, T. Laine, B. Welle, Implementation Guide Space Boundaries for Energy Analysis, in: US General Services Administration (GSA) and Open Geospatial Consortium (OGC), 2012 http://www.blisproject.org/IAI-MVD/documents/Space Boundaries for Energy Analysis v1.pdf (accessed on November 2012). [8] US. General Services Administration (GSA), GSA Building Information Modeling Guide Series, in: 05-GSA BIM Guide for Energy Performance, 2009. [9] Building SMART, 2012, http://www.buildingsmart.com (accessed on November 2012). [10] Bumyang Air Conditioning, 2012, http://www.bac.co.kr/index.html (accessed on November 2012, in Korean). [11] S. De Wit, Uncertainty in predictions of thermal comfort in buildings, in: Doctoral Dissertation, Delft University of Technology, Delft, The Netherlands, 2001. [12] S. De Wit, G. Augenbroe, Analysis of uncertainty in building design evaluations and its implications, Energy and Buildings 34 (9) (2002) 951–958. [13] P. Heiselberg, H. Brohus, A. Hesselholt, H. Rasmussen, E. Seinre, S. Thomas, Application of sensitivity analysis in design of sustainable buildings, Renewable Energy 34 (9) (2009) 2030–2036. [14] A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, John Wiley & Sons Publishers, Hoboken, NJ, 2004. [15] I.A. Macdonald, Quantifying the Effects of Uncertainty in Building Simulation, in: Ph.D. Thesis, University of Strathclyde, 2002.
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[16] C.J. Hopfe, Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design Optimization, in: Ph.D. Thesis, Technische Universiteit Eindhoven, The Netherlands, 2009. [17] C. Struck, J. Hensen, On supporting design decisions in conceptual design addressing specification uncertainties using performance simulation, in: Proceedings of the 10th International IBPSA Conference, Beijing, China, 2007, pp. 1434–1439. [18] G.D. Wyss, K.H. Jorgensen, A User’s Guide to LHS: Sandia’s Latin Hypercube Sampling Software, Sandia National Laboratories, Albuquerque, New Mexico, US, 1998.
[19] IBPSA, The international building performance simulation association, in: Proceedings of the 7th to 12th International IBPSA Conference, 2001. [20] SimLab, 2012, http://simlab.jrc.ec.europa.eu (accessed on November 2012). [21] R.L. Iman, J.C. Helton, A Comparison of Uncertainty and Sensitivity Techniques for Computer Models, in: International Report NUREG ICR-3904, SAND 84-1461, Sandia National Laboratories, Albuquerque, New Mexico, USA, 1985.