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Automated multi-zone building energy model generation for schematic design and urban massing studies


Abstract and Figures

In this paper we present an algorithm for automated multi-zone building energy model production for urban and schematic design. By reviewing current guidelines for thermal zone discretization of early design building energy models [BEM] we present an argument that current zoning guidelines effectively recommend the use of a straight-skeleton like subdivision. Based on this finding, our procedure accepts an arbitrary building massing and subdivides each floor into core and perimeter thermal zones. As a proof of concept the algorithm was implemented as a plug-in for the parametric design environment Grasshopper for Rhinoceros. A number of examples of various complexities are shown to demonstrate its robustness and suitability for automated multi-zone BEM generation.
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Automated multi-zone building energy model generation for
schematic design and urban massing studies
Timur Dogan1, Christoph Reinhart1, and Panagiotis Michalatos2
1 Massachusetts Institute of Technology, Cambridge, USA
2 Harvard Graduate School of Design, Cambridge, USA
In this paper we present an algorithm for automated multi-zone build-
ing energy model production for urban and schematic design. By re-
viewing current guidelines for thermal zone discretization of early de-
sign building energy models [BEM] we present an argument that cur-
rent zoning guidelines effectively recommend the use of a straight-
skeleton like subdivision. Based on this finding, our procedure accepts
an arbitrary building massing and subdivides each floor into core and
perimeter thermal zones. As a proof of concept the algorithm was im-
plemented as a plug-in for the parametric design environment
Grasshopper for Rhinoceros. A number of examples of various com-
plexities are shown to demonstrate its robustness and suitability for
automated multi-zone BEM generation.
Keywords: energy modeling, urban and schematic design, automatic
1. Introduction
The earth’s urban population is expected to double by 2050, requiring the construction
and densification of hundreds of cities and neighborhoods [UN, 2012]. With 30-40% of the
world’s energy consumption coming from buildings and more than 60% of the increase in
building related CO2 emissions coming from developing countries [UNDP, 2010], creating
livable and energy efficient buildings and urban environments can thus be seen as the defining
planning challenge of our century. The use of tools for building energy use optimization is,
however, underutilized when it comes to understanding and predicting the energetic implica-
tions of schematic and urban design decisions. Computer-based building performance simula-
tion (BPS) tools have been developed since the early seventies to test drive different design
concepts during schematic design in order to ensure that individual buildings provide “high
comfort spaces” while having a “minimum environmental impact” [Hensen & Lamberts,
2011]. Yet, despite the improved usability of these tools, they tend to be mostly applied during
design-development or for demonstrating code compliance. As a direct result of this delayed
use of tools within the design process, a recent survey of energy modelers and architects by
Samuelson et al. [2012] revealed that even in AEC (Architecture, Engineering, and Construc-
tion) firms which employ in-house energy models the results of simulations had in over 30%
of cases only “rarely” or “occasionally” an impact on design decisions. This finding is prob-
lematic since architects would benefit most from simulation-based feedback during the explo-
ration of rapidly changing design variants, as it is common practice in the early design stages.
Similarly, urban designers, a group that traditionally does not employ energy modeling tools,
would also greatly benefit from using energy simulations to inform site layout, massing stud-
ies and program distribution [Besserud & Hussey, 2011].
One of the reasons for the aforementioned delayed use of BPS in design is that the
generation of BEMs out of existing architectural or massing models currently involves multi-
ple manual steps and is therefore time and resource intensive [De Wilde, 1999][Mahdavi et
al., 2003][Ianni et al., 2013]. Applying current modeling practices to urban level projects that
involve multiple buildings therefore remains the exception and (if at all) is done using a com-
bination of spreadsheets with simulations of more generic building archetypes. Such a model-
ing approach is unsuitable for resolving details of urban microclimate and detailed neighbor-
hood planning. An alternative approach to the spreadsheet method would be to use already
available 3D models (that are produced anyhow e.g. for renderings or plan production) and
automatically convert them into energy models. Over the years, different groups have worked
on various aspects of this problem [van Treeck & Rank, 2007] [O’Donnell et al. 2013] [Pratt
et al. 2012] [Jones et al. 2013].
A key distinction between previous methods is the level of detail of the architectural
input model that is to be converted into a BEM. In 2007 van Treeck demonstrated how a full
3D Building Information Model (BIM) that includes interior walls and usage definitions can
be auto-translated into an energy model. The method presented performs a "dimensional re-
duction of 3D building models using graph theory" [van Treeck & Rank, 2007]. The algo-
rithm uses Boolean operations to decompose the input geometry and then performs surface
overlap tests to establish a connectivity network for a multi-zone energy model. Similarly,
O’Donnell et al. [2013] worked on a semi-automated BIM to BEM conversion process im-
plementing automated space boundary identification. Pratt et al. [2012] presented a geometric
modeling protocol and framework that included geometry correction and surface heuristics
that would allow the conversion of more traditional 3D CAD models with interior space sub-
divisions into BEMs. Later that same group refined this approach, presenting additional
methods for “cleaning up” real world architectural models by removing small holes, reducing
the number of interior surfaces and using a view-factor based method to identify zones and
their adjacencies [Jones et al. 2013].
All of these studies assumed that a detailed BIM or CAD model of a building with
defined interior spaces is available for conversion into BEM. Typically such models are avail-
able later in the design process. The studies further tended to convert each room in the archi-
tectural model into a new thermal zone. In contrast to these previous efforts, this paper is
mainly concerned with the conversion of more abstract massing models into BEM. Within the
context of this paper a massing model is a geometric representation that approximates the
shape of a building and that does per se not contain any information about the interior subdi-
vision of a building such as floors and walls (Figure 1). This "empty hull" is then paired with
Figure 1: Massing model and its typical modeling styles in urban and schematic design.!
a) Building envelope b) Buildings as stacked floor volumes
a vague idea on how the interior can be used programmatically, how the building could be
materialized and how the facade might look like, e.g. a mixed use building, office-residential,
heavy construction with punched window facade. A thermal simulation, however, requires
more specific inputs, windows have to be modeled explicitly and the building volumes need
to be discretized more finely into thermal zones. Thus, two essential steps are required to
convert massing models into BEMs automatically. During step one, the building volume, rep-
resented by poly-surfaces, has to be converted into multi-zone thermal model geometry, in-
volving a discretization into multiple sub-volumes to represent floors and zones and geomet-
rically articulated facades. In the second step, parameters have to be assigned to the individual
zones including information such as materiality, space usage and space conditioning.
This manuscript is mainly concerned with the first step, i.e. the automated conversion
of an architectural massing model as defined above into a meaningful multi-zone BEM geom-
etry that can be used by a whole building energy modeling program such as EnergyPlus,
TRNSYS and others [Crawley et al., 2000] [Klein, 1979] [ESRU, 2005]. The next paragraph
reviews current guidelines for generating multi-zone thermal energy models of buildings and
lays the foundation for the following methodology of our auto-zoning procedure.
2. Early design multi-zone energy models
For decades modelers have implemented multi-zone thermal models to simulate ener-
gy use of a building. It is therefore somewhat surprising that there exists relatively little scien-
tifically backed advice as to how an “early design variant” should be broken up into discrete,
thermal zones. According to the literature [Hirsch, 2010] [BEMBOOK, 2014] a thermal zone
should roughly correlate with the spatial subdivision of a building into rooms and spaces. To
simplify the simulation and also the HVAC system layout, multiple rooms may be joined to-
gether in one zone if they share similar load profiles. However, the floor plan that would pro-
vide a basis for such a room subdivision is usually unknown during the schematic design
stage. ASHRAE 90.1 Appendix G hence provides a brief guideline for this case stating that
the floor plan should be divided into a “core” and “perimeter" region. The perimeter is de-
fined as the space along the facade with a depth of five meters. Further, the perimeter should
be subdivided by orientation and spaces with glazed exterior surfaces with more than one ori-
entation should be subdivided proportionally. The leftover region in the center of the floor
plate forms the “core” [ANSI/ASHRAE/IESNA, 2007]. 90.1 Appendix G provides no binding
guidance of how the boundary between different zones should be modeled.
The original motivation for breaking a building into thermal zones is that simulation
programs treat all zones as a node within a thermal network that represent a perfectly mixed
air volume. So, the theory goes that if one for example models a larger building as a single
thermal zone than heat surplus that may occur during the winter near a south facing space
may be absorbed by a space to the north that is underserved by solar gains. As a consequence
the predicted energy demand decreases due to a balancing between loads, gains and absorp-
tion/dampening capacities of a space [Smith et al. 2011]. Figure 2 compares various multi-
zone thermal models of the same floor to a single zone model assuming low and high internal
loads. The figure shows that annual loads for heating and cooling may differ by up to 13%
and 14% for low and high internal loads, respectively. As expected, the Appendix G method
yields higher and thus more conservative annual loads than the single zone simulation.
As mentioned earlier, the 90.1 Appendix G guideline does not specify how to take in-
ter-zone heat and mass transfer into account. Figure 3 reveals the magnitude of this effect by
comparing annual loads for the building from Figure 2 divided according to Appendix G but
with interior zone boundaries being either adiabatic and step by step adding conduction, solar
radiation and different levels of air mixing. As one would expect, adding these different inter-
zone heat flows successively bring the Appendix G solution closer to the single zone solution.
While ASHRAE 90.1 does not give recommendations on and leaves it up to the modeler to
provide accurate assumptions, we conclude this mini experiment by grouping the heat and
mass transfer into three useful architectural scenarios (floor-plan-typologies) that can be im-
plemented as presets in the following energy modeling workflow. The “individual office sce-
nario” only considers conduction heat transfer between the zones. The “open plan” scenario
can consider radiation exchange and inter-zone air-mixing. The “small building” scenario al-
lows to simulate lumped zones.
The previous paragraphs confirm the relevance of current practice to divide a building
into multiple thermal zones and identify 90.1 Appendix G as the only existing guideline ad-
vising on discretizing a building volume into thermal zones. This paper proposes a method to
carry out this step automatically. Several existing design tools also have auto-zoning methods
implemented. Autodesk Vasari [Autodesk, 2013] is a commercially available schematic de-
sign tool for architects, can automatically split up a building massing into perimeter and core
zones. Details of the zoning algorithm and its implementation have not been publicly re-
leased. Similarly, Reinhart et al. [2013] introduced software for multi-zone energy model pro-
duction. The software is, however, limited to simple and strictly orthogonal shapes and adja-
cencies to neighboring buildings are not recognized. In the following section a general algo-
rithm is described that overcomes these limitations.
Figure 3: Comparison of a perimeter and core subdivision with different inter-zonal
heat and mass transfer scenarios versus a single zone simulation and their correlation
with architectural floor plan typologies.
Figure 2: Comparison of subdivision schemata for two extreme internal load scenarios.
3. Methodology
The presented research builds on three tools for simulation and geometric modeling:
EnergyPlus, the McNeel Rhino Platform and Archsim Energy Modeling for Grasshopper. En-
ergyPlus is a whole building energy simulation program that can model building related ener-
gy flows in a sub hourly time resolution [EERE, 2013]. It is validated thoroughly and it is dis-
tributed free of charge. This makes it one of the most accessible professional energy simula-
tion tools available and thus is the simulation environment of choice. McNeel Rhinoceros is a
CAD modeling software that can create, edit, analyze, and translate curves, surfaces, and
solids. McNeel offers extensive support for plug-in development with the RhinoCommon
SDK. This research relies heavily on this basic geometric modeling ecosystem [McNeel,
2013]. Rhino was also chosen due to its popularity among architects and urban planners.
Archsim Energy Modeling for Grasshopper is a user and programming interface for energy
model production. It features a thermal model class library containing abstract definitions for
zones, faces, materials, etc. and can translate those into a simulation engine specific syntax.
Archsim supports EnergyPlus and the TRNSYS. Given a set of geometry and parameters it
constructs multi-zone thermal models and writes out the simulation input file [Dogan, 2013].
For rapid energy model production two essential capabilities are added to the above toolset:
Automated geometry production: The massing model input geometry has to be divided
into sub-volumes representing the floors, perimeter and core zones of a building.
Efficient parameter assignment: The produced geometry has to be paired with additional
information such materiality, loads, schedules and HVAC settings to generate a complete
thermal model.
In the following section the automated geometry production workflow is explained in
detail. Methods for efficient parameter assignment are introduced afterwards. We then present
three test cases that show the robustness of the algorithm and its feasibility for an ArchSim/
EnergyPlus modeling workflow.
Automated Geometry Production
Input: Based on the authors experience from practice and teaching, urban and
schematic design processes mainly produce two types of input geometry. In the earliest
stages, the buildings are modeled as a single volume. This form of representation helps the
designer to understand basic morphologic features such as the massing and the proportions of
a design. It usually originates from a 2D drawing that is extruded. Another very common rep-
resentation involves “stacking” floor-volumes. This adds a notion of “scale” since it immedi-
ately allows one to read floor heights from the model. It is also popular since it is inline with
the architectural design process, thinking of distributing program and densities. The following
algorithm can work with both styles (Figure 1). If “Case A” is encountered one volume is in-
terpreted as one building envelope. For “Case B”, the algorithm requires the user to group the
volumes if they belong to the same building. For "Case A" the algorithm begins with a floor
to floor subdivision of the envelope based on a floor height that is specified by the user. The
floor to floor distance can be given as array to take into account varying floor heights. For
"Case B" the floor subdivision procedure is skipped. Starting point for all following steps is a
“Building” that consists of a list of sub volumes for each floor.
Subdivision: The next step is to subdivide the floor volumes into thermal zones. For a
limited number of cases the perimeter and core zone subdivision, as described by the previ-
ously mentioned ASHRAE 90.1 Appendix G guideline, is a trivial and fast operation. The
core region can be found by offsetting the outer edge of the floor plan. Then a simple search
for the closest point from the outer polygon vertices to the inner ones can already yield the
desired subdivision. This is shown in Figure 4a. When the search function returns pointers to
the inner vertices, the lists of points that make up a zone outline can be constructed, even if
edges of the offset polygon collapse. For more complicated cases the above mentioned ap-
proach fails. Figure 4b shows a convex floor plan with a core region that retreated completely
from the left part of the polygon due to a series of collapsing edges during the offset. It subdi-
vides parts that have good “core visibility” as intended but leaves the entire left half undivid-
ed. Now, an ear-clipping method [Meisters, 1975], [ElGindy et al., 1993] could divide the rest
into convex regions. However, with a certain thickness of the remaining polygon-tip it might
be desirable to split the tip into single sided zones instead of a lumped region with access to
multiple cardinal directions.
This would require a splitting axis somewhere along the medial axis [Preparata, 1977]
of the polygon. A medial axis approximation is shown in Figure 4c. The medial axis however,
does not consist of straight line segments and instead can involve parabolic curves. This is not
desirable since the final output of the algorithm are 3D thermal zones that should ideally con-
sist of as few as possible planar surfaces. Very similar to the medial axis but involving just
straight lines is the "straight skeleton" that was first described by Aichholzer et al. [1996] and
extensively discussed [Aichholzer & Aurenhammer, 1996], [Barequet et al., 2003]. The
straight skeleton of a polygon as shown in Figure 4d divides the polygon into cells for each
outer edge and is thus fulfilling the requirement of splitting perimeter zones by orientation. In
order to obtain a core and a valid thermal zone subdivision a couple of simple steps have to be
added. In a second step the algorithm produces the core region by performing an offset of the
outer and hole polygons. Then the core overlap is removed from each skeleton cell by a 2D
boolean difference operation. Due to limitations of the radiation distribution algorithm of En-
ergyPlus and TRNSYS the thermal zones have to be strictly convex spaces. Thus, the result-
ing perimeter and core zones have to be further subdivided if they are concave. Various poly-
gon partitioning techniques exists for this task and have been described in detail [O’Rourke,
1998]. Since the resulting perimeter regions are guaranteed to be hole-free, a simple split at
each concave vertex perpendicular to the longest exterior edge of the polygon delivers the de-
sired result. The core regions however, can consist of polygons with holes. One simple exam-
ple is a donut-shape floor plate with a circular core region. Here, a triangulation and a subse-
quent diagonal-removal procedure is used to obtain strictly convex zones [Hertel &
Mehlhorn, 1983]. The result of the previous steps is a set of 2D regions for the perimeter and
the core of each floor plate. Pseudo code that briefly summarizes the previously described
steps is given below.
The implementation of a robust straight skeleton algorithm that can handle complex
polygons is not a trivial task. The presented algorithm uses a ported and slightly improved
Figure 4:!
a) Perimeter split b) Failure c) Medial axis d) Straight skeleton (with a concave cell)
version of an implementation that has been provided by Petr Felkel and has been extensively
described by Felkel & Obdrzalek [1998]. For both the offset and the Boolean operations the
algorithm rely on the polygon clipping library “Clipper” by Johnson [2012].
Form 2D regions to 3D zones: The 2D core and perimeter cells have to be further
processed to obtain the final 3D volumes that represent a thermal zone. The simplest approach
is to extrude the cells by the floor height. This however, is only possible if the facades of the
initial envelope are vertical. In order to avoid this limitation the algorithm uses the previously
generated or provided floor volumes. Analogous to cutting styrofoam blocks with a hot wire
the algorithm computes vertical clipping planes along the 2D perimeter and core regions
while skipping outer edges and then cuts out the zones from each floor volume. Pseudo code
is given on the next page.
Adjacency: In urban scenes it is quite common that buildings touch each other and
not every face of the envelope is a facade with outward facing windows. In such a case the
algorithm needs to identify such faces in order to assign the correct boundary conditions and
materials later. Additionally, the zoning should also react to inter-building partition walls to
avoid building a perimeter zone there. In order to detect adjacencies the algorithm uses Arch-
sim EM's adjacency graph building functionality. Archsim relies on congruent faces with op-
posing face normal in order to find a connection. It provides a recursive face splitting algo-
rithm to handle geometry that has touching surfaces with non-congruent surfaces. With the
adjacencies identified, the zoning procedure can now build adjacency aware core and perime-
ter regions. Adjacencies are handled by joining the neighboring floor plans into a single plate
before core and perimeter region offsets are computed. Considering the possibility that floor
plans are not always at matching Z-coordinates, the closest floor plan in the neighboring
building is used. The offset regions are then trimmed back to the buildings original size, re-
sulting in a core region that now touches the adjacent wall element. However, his procedure is
not always straight forward: In an extreme case where the core offset in the neighboring
building completely collapses and "retreats" back into only one building, undesirable core
geometry can be produced.
Input. A set F of poly-lines in a plane representing the outlines of a floor plan.!
Output. A list of doubly connected edges describing the cellular core and perimeter subdivision
1. Compute the straight skeleton S(F) and store the resulting skeleton cells in a list
2. Compute an inward offset O(F) of the input poly-lines F.!
3. O(F) describes the core region(s)
5. DO obtain c
6. IF p
7. split c
8. ELSE!
9. append c
10. Split RCORE
11. append LCC
Automatic window/shading modeling: After the zone geometry is modeled, we have
to further articulate the facades. The user can specify orientation dependent window to wall
ratios that are then used to model the window surfaces on all outward facing zone surfaces.
This can be done by duplicating and then scaling the windows parent surface. Alternatively,
standard Grasshopper modeling capabilities can be used to implement more elaborate facade
design patterns such as punched hole facades with multiple small openings or simple horizon-
tal window stripes. Manual intervention is also possible.
Parameter assignment
In addition the algorithm producing the zone geometry the user needs to be presented
with an efficient way to assign essential simulation parameters, such as materiality of the
building elements, usage patterns and control strategies in form of schedules as well as the
internal loads, to each zone. Regarding usage patterns and loads for various space types, early
design simulations often rely on standards as they are defined in the “SIA Merkblatt
2024” [SIA, 2006]. Thus, parameter templates for the various zone types that exist in the
model can easily be defined. These zone types however, are distributed throughout the entire
model and thus a diverse type-mix might exist within one building. Consequently, a parameter
definition via a building-template seems inadequate. Since selecting each and every zone in-
dividually to assign the appropriate template is also not an option, a more general method that
can assign zone templates throughout the various buildings is required. In our workflow,
analogous to the architectural zoning in a master plan, the user defines large 2D or 3D regions
to assign parameters to the individual thermal zones. To define a 2D region a closed curve,
surface or image map can be used. Alternatively, 3D regions that are modeled by large closed
volumes allow better control over the vertical distribution of the zone templates. Each region
carries settings for perimeter and core and a simple point containment test is performed on the
geometric centroid of each zone to assign the parameters. This allows the user to create com-
plicated mixed-use scenarios with a minimal amount of effort.
Testing the algorithm
Input. A set of planar zoning cells TDZ(F) and the corresponding floor volume V.!
Output. A list of closed volumes representing the thermal zone geometry for core and perimeter
ZG(TDZ(F), V).!
2. IF cp
3. Create an extrusion of c
4. Append E(c
5. ELSE!
6. Duplicate V as V
8. IF e is an interior edge!
9. Trim V
10. Append V
In order to test the algorithms planar zoning functionality we tested various floor plan
outlines with varying complexity (Figure 5). Starting with a simple rectangle we continue by
adding convex and non-convex holes. We then use our algorithm for one building of the
massing model from Figure 1 and run a multi-zone energy simulation in the Boston climate to
test the feasibility of our automated zoning algorithm within an ArchSim and EnergyPlus
workflow (Figure 6). Simulation results for one building are visualized by false coloring the
zone geometry for heating, cooling and lighting energy demand (Figure 7). While test case in
Figure 6 only requires a simple extrusion after we have obtained the 2D zoning due to its ver-
tical facades, we show the discretization of a complex 3D massing model with sloped facades
that requires the “wire cutting” approach in Figure 8.
4. Results
In Figure 5 the zoning result of our algorithm is shown for floor plan geometry of
varying complexity. The offsetting procedure has defined the core and perimeter regions, both
visualized in a dark and light gray. The core region completely disappeared from regions that
are thinner than two times the offset distance as marked by red circles in the figure. The
perimeter cells that further subdivide the perimeter region into single sided zones were de-
fined by the straight skeleton procedure and are visualized by black outlines around each cell.
The green circles mark additional cell splits into strictly convex zones as described earlier.
The darker core regions are also subdivided into strictly convex zones. The blue circle high-
lights a region where some diagonals could not be removed and thus resulting in very narrow/
flat triangles.
Figure 5: Various zoning test cases of varying complexity. !
Light grey represent the perimeter and dark grey the core.
In Figure 6, the ArchSim EM, EnergyPlus workflow integration is demonstrated. 11b
shows the 3d geometry of perimeter (medium gray) and core (dark grey) zones as well as the
window geometry (light gray). 11c shows only the inner surfaces of the model. The image
validates that all interior heat transfer surfaces have been recognized as such by ArchSim EM
and illustrates the complexity of the connectivity graph of the thermal model. The overall
model complexity is outlined in Table 1. The shown building consists of 118 zones that in to-
tal consist of 877 faces such as roof- , partition-, facade- and window surfaces. Figure 7
shows exemplarily how the EnergyPlus simulation results can be visualized. The zone geome-
try is false-colored according to the cumulative annual energy use intensity for heating a),
Figure 6: Detailed model visualization: !
a) Input volume !
b) Zone and window geometry!
c) Surface connectivity
Table 1: Model complexity (for partial model as shown in Figure 6)
Number of zones
Number of opaque surfaces
Number of glazed surface
Table 2: Runtimes [s] (for partial model as shown in Figure 6)
New algorithm
ArchSim EM
Energy Plus
Generate IDF
cooling b) and lighting c). In Table 2 the computational cost of each step is listed for the ex-
ample model of Figure 6.
In Figure 8 the 3D decomposition of a high-rise massing with sloped facade surfaces
is shown. The input geometry is split into floor volumes before the inner subdivision is com-
puted. The figure visualizes this by rendering the generated partition walls. Finally the
perimeter zones and windows are shown. Note the triangular surfaces that appear along the
edges of the massing where vertical zone partition wall meet the sloped facade surfaces.
5. Discussion
The current manuscript serves the purpose of describing a general and flexible multi-
zone energy model production algorithm that can be used by others. It is our hope that the in-
troduced procedure can simplify and accelerate BPS in early design significantly and that it
can integrate smoothly into workflows already used by design and planning teams. In Figure
5 our procedure proved its robustness with complicated geometry. It strictly follows the cur-
rent convention for zoning early design variants as defined in the previously mentioned
ASHRAE 90.1 guideline. The straight skeleton of a polygon per se follows the rule to parti-
tion the affiliation of a space to a facade proportionally. This fact is particularly useful for the
zone subdivision of more complicated floor plans since coming up with a “correct” subdivi-
sion manually can easily become a time consuming and non-trivial task. As shown in Figure
5c moving the courtyard of a simple rectangular shape off-center can already produce com-
plex space affiliations that are not directly obvious.
The convex partitioning of the core can occasionally lead to very long and narrow
zones due to the character of HertelMehlhorn based procedure which first triangulates and
then removes unnecessary diagonals to obtain convexity. To avoid this, the implementation of
a more elaborate splitting mechanism is planned for the future.
Another future goal is to compare simulation results generated with the multi-zone
discretization algorithm against an altogether different urban modeling approach which is
based on abstracting an arbitrary neighborhood massing that consists of hundreds of buildings
into a meaningful group of “typical” two zone thermal models [Dogan & Reinhart 2013]. The
approach has been implemented into a fully automated procedure that makes informed simpli-
fications based on micro-climatic boundary conditions and internal loads. The methodology
Figure 8: Complex massing: Input, floor subdivision, inner subdivision and facade.
floor volumes
partition walls
zones, windows
offers easy multi-building energy model management and drastically reduced simulation
complexity and runtimes and thus facilitates rapid early design evaluation and optimization.
6. Conclusion
A general algorithm for creating multi-zone building energy models for urban and
schematic design massing models is introduced. The algorithm can handle complex building
shapes and may facilitate and accelerate building energy modeling in early design.
7. Acknowledgements
We would like to thank Transsolar Energietechnik GmbH Munich for productive discussions
and partial funding of the research project.
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... Their typologies were derived from geometry parameters based on the number of classrooms, number of stories, window-to-wall ratio and room depth. A key distinction between geometry and massing is the level of input detail, which means that building massing is the overall configuration of the building, whereas geometry is usually originated from proportions of a 2D drawing [21,29]. So, building massing have not only a marginal effect on floor plan geometries of varying complexity, but also on energy consumption patterns for each interior space [29]. ...
... A key distinction between geometry and massing is the level of input detail, which means that building massing is the overall configuration of the building, whereas geometry is usually originated from proportions of a 2D drawing [21,29]. So, building massing have not only a marginal effect on floor plan geometries of varying complexity, but also on energy consumption patterns for each interior space [29]. Thus, this study complements other studies in terms of addressing interior efficiency as well by focusing on massing typologies rather than on solely geometry. ...
... The study used Sefaira program simulations to model the five school massing typologies. Sefaira's Real-Time Analysis Plugins, which are dynamic simulation tools for energy assessment based on architecture, lighting, and mechanical systems, use EnergyPlus as their primary simulation engine [29]. EnergyPlus is validated thoroughly and it is distributed free of charge, which makes it one of the most accessible professional energy simulation tools available, such as Design Builder, eQUEST, IDA ICE, etc. [39]. ...
To produce energy-efficient buildings, optimization process for all design stages is necessary. Optimization starts with the massing of the building. This study investigates the impact of the five school massing typologies on energy efficiency: (i) spine/street; (ii) city/town; (iii) atrium; (iv) strawberry/cluster; and (v) courtyard. The chosen massing typologies respond to the question of what an optimum spatial organization of massing is to (i) maximize the use of renewable resources; (ii) utilize thermal inertia of buildings; and (iii) consider the relationship between inside and outside, both existing and future. For each massing type, Sefaira program was used, and simulations were run for annual energy use, annual energy cost and annual carbon dioxide emissions. The energy use indices (EUI) of the alternatives are around 86 kWh/m2/yr. In the spine massing, the EUI value is much higher than the other four buildings. The highest annual net emissions are obtained in atrium type of building, which has more floors compared to other massing type. The courtyard type has the most efficient annual electricity cost per area. These findings showed that the goal of the building massing should be not only limited to achieve the low EUI. Thus, this study suggests that an energy-efficient massing should address the questions beyond well-known ASHRAE standards, and define a new holistic model that considers the ratio of surface area to volume more for reducing energy loads than a typical high-performance schools.
... Regarding the system boundary of assessment, most of these articles defined their energy indicators unclearly: three articles described the system efficiency [33,65,71], and we assume that they tested the final energy; the others did not show system information, thus, we assume that they tested energy demands. Regarding the calculation period, most studies calculated the energy use for the whole year [33,64,65,68,70,71], and some studies only calculated it for peak days [42,63] or season representative days [67]. ...
... As most information has already been shown in Table 2, the articles that were analysed in this section and Section 5 are introduced briefly. Some articles are not used for the analysis in Section 4 and also in Section 5: the studies of [33,71] did not show the results of energy performance, and the study of [70] did not present sufficient information for the on BEP calculation. As the articles in Table 2 mixed the design variables of space layouts with other parameters, the effects of space layouts cannot be identified directly from the results of these articles. ...
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As one of the most important design tasks of building design, space layout design affects the building energy performance (BEP). In order to investigate the effect, a literature review of relevant papers was performed. Ten relevant articles were found and reviewed in detail. First, a methodology for studying the effects of space layouts on BEP were proposed regarding design variables, energy indicators and BEP calculation methods, and the methodologies used in the 10 articles were reviewed. Then, the effects of space layouts on energy use and occupant comfort were analysed separately. The results show that the energy use for heating, cooling, lighting and ventilation is highly affected by space layouts, as well as thermal and visual comfort. The effects of space layouts on energy use are higher than on occupant comfort. By changing space layouts, the resulting reductions in the annual final energy for heating and cooling demands were up to 14% and 57%, respectively, in an office building in Sweden. The resulting reductions in the lighting demand of peak summer and winter were up to 67% and 43%, respectively, for the case of an office building in the UK, and the resulting reduction in the air volume supplied by natural ventilation was 65%. The influence of other design parameters, i.e., occupancy and window to wall ratio, on the effects of space layouts on BEP was also identified.
... Its modularity supports incremental development. There are several third-party modules and interfaces for EnergyPlus, including Simergy, OpenStudio (Guglielmetti, Macumber and Long, 2011), Design Builder, Archsim (Dogan, Reinhart and Michalatos, 2014), Honeybee (Mackey et al., 2015), Autodesk Insight (Autodesk, 2019). From those interface programs to EnergyPlus, four directly interact either with Computer Aided Design (CAD) or Building Information Modeling (BIM) building design tools. ...
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The resources involved in the construction and operation of buildings represent nearly 40% of the global emissions of greenhouse gases (GHG), making the building sector one of the primary contributors to global warming. This reality has led to the creation of many prescriptive regulatory and voluntary programs that aim to mitigate the environmental impact of the building sector while ensuring high standards for Indoor Environmental Quality (IEQ), particularly those regarding the thermal and visual comfort of building occupants. Thus, the design of high-performance buildings, i.e., resource- and energy-efficient buildings that yield high levels of IEQ, is a pressing need. This scenario pushes architects to simulate their projects’ environmental performance to better support design tasks in a process referred to as performance-based design. This dissertation studies the integration of daylighting and Building Energy Simulation (BES) tools into performance-based design supported by computational design (CD) methods, particularly parametric design and Building Performance Optimization (BPO). The assumption is that the early integration of parametric, BES, and daylighting simulation tools can be highly effective in the design, analysis, and optimization of high-performance buildings. However, the research argues that the current daylighting and Building Energy Simulation (BES) tools pose critical challenges to that desirable integration, thus hindering the deployment of efficient exploratory design methods such as Parametric Design and Analysis (PDA) and BPO. These challenges arise from limitations regarding (i) tool interoperability, (ii) computationally expensive simulation processes, and (iii) problem and performance goal definition in BPO. The primary objective of the dissertation is to improve the use of daylighting and BES tools in PDA and BPO. To that end, the research proposes and validates five modeling strategies that directly tackle the limitations mentioned above. The strategies are the following: (i) Strategy A: Automatically generate valid building geometry for BES; (ii) Strategy B: Automatically simplify building geometry for BES; (iii) Strategy C: Abstract Complex Fenestration Systems (CFS) for BES; (iv) Strategy D: Assess glare potential of indoor spaces using a time and spatial sampling technique; and (v) Strategy E: Painting with Light - a novel method for spatially specifying daylight goals in BPO. The research work shows that the strategies address the research problem and current limitations by (i) improving the interoperability between design and BES and daylighting simulation tools (Strategies A, B, and C); (ii) producing quick and adequate feedback on the daylight, thermal, and energy behavior of buildings (Strategies B, C, and D); and (iii) facilitating the spatial definition of performance goals in daylighting BPO workflows (Strategy E). These three important merits of the proposed strategies effectively contribute to improving the efficiency of using daylight and BES tools in the design, analysis, and optimization of high-performance buildings. Finally, the dissertation discusses the merits and limitations of each strategy, provides useful guidelines and recommendations for their use in building design, and suggests future directions for further research.
... However, converting 3D models of a large number of buildings to thermal models is not a straightforward process, and automated zoning algorithms are required. With the focus on the auto zoning process, Dogan et al. [100,103] recommend an automated shoebox model in creating multi-zone models from the 3D building models. The shoe-box algorithms automatically discretize the buildings and abstract them into one or several perimeter and core models that are placed in representative locations within the building. ...
During recent years, urban building energy modeling has become known as a novel approach for identification, support and improvement of sustainable urban development initiatives and energy efficiency measures in cities. Urban building energy models draw the required information from the energy analysis of buildings in the urban context and suggest options for effective implementation of interventions. The growing interest in urban building energy models among researchers, urban designers and authorities has led to the development of a diversity of models and tools, evolving from physical to more advanced hybrid models. By critically analyzing the published research, this paper incorporates an updated overview of the field of urban building energy modeling and investigates possibilities, challenges and shortcomings, as well as an outlook for future improvements. The survey of previous studies identifies technical bottlenecks and legal barriers in access to data, systematic and inherent uncertainties as well as insufficient resources as the main obstacles. Furthermore, this study suggests that the main route to further improvements in urban building energy modeling is its integration with other urban models, such as climate and outdoor comfort models, energy system models and, in particular, mobility models.
... [18], where WWRs follow suggestions of (sROM) [17] (Table 1). [19] and Archsim [20]. Both plugins work under Grasshopper environment, and interface Radiance [21] and EnergyPlus [22] engines for daylighting and energy simulations respectively. ...
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The direction towards sustainable development challenges the designers to explore energy-efficient designs to provide adequate levels of natural daylighting. Indeed, using computational simulations can offer valuable performance predictions; still, exploring all possible combinations of design variables is a tedious and time-consuming task. This type of problems also doesn’t yield straightforward mathematical forms, and using black-box optimizations is more useful. This paper presented reviews, methodological tools and measures to help designers evaluating the potential contribution of simulation-based metaheuristics to optimize the design of façades in such obstructed contexts. The tested metaheuristics algorithms showed promising potential to attain optimum solutions, though the results confirmed that Genetic Algorithm was not sensitive to population size for optimizing façade design problems, as it obtained optimum designs only after conducting several runs of simulations. In contrast, Particle Swarm Optimization exhibited a higher sensitivity towards swarm sizes, where it converged much faster with larger swarm sizes; and a better performance could be achieved when the swarm size was 10 times the number of design variables alongside 0.2 particle’s velocity. This implies that metaheuristics optimization can be used along with simulations as accelerated tools in the early stages of design to rapidly predict building performances in obstructed environments.
... Dogan et al. [41] presented an algorithm to produce automated, multi-zone building energy models for urban and schematic designs. Their algorithm used a robust straight skeleton algorithm [42] with an arbitrary building massing, subdivided into core and perimeter thermal zones. ...
Building energy simulation programs can be useful tools in evaluating building energy performance during a building's lifecycle, both at the design and operation stages. In addition, simulating building energy usage has become a key strategy in designing high performance buildings that can better meet the needs of society without consuming excess resources. Therefore, it is important to provide accurate predictions of building energy performance in building design and construction projects. Although many previous studies have addressed the accuracy of building energy simulations, very few studies of this subject have mentioned the importance of Heating, Ventilation, and Air-Conditioning (HVAC) thermal zoning strategies to sustainable building design. This research provides a systematic literature review of building thermal zoning for building energy simulation. This work also reviews previous definitions of HVAC thermal zoning and its application in building energy simulation programs, including those appearing in earlier studies of the development of new thermal zoning methods for simulation modeling. The results indicate that future research is needed to develop a well-documented and accurate thermal zoning method capable of assisting designers with their building energy simulation needs.
... However, the number of thermal zones will vary depending on many factors including the building use, size, and shape, where a 'zone' is a segment of a building with similar thermal requirements serviced by the same mechanical equipment and controls. On the number of thermal zones, a thermal simulation requires more specific inputs, windows have to be modelled explicitly and the building volumes need to be discretised more finely into thermal zones [35]. ...
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This paper explore the benefits of a bottom-up spatially enabled engineering building-based energy framework in identifying neighbourhoods, and community's building aggregated areas with spatial patters. We argue that an area-based approach allows more houses to be targeted in places where local area characteristics show inefficient elements, and may therefore potentially capture a greater number of households per unit of cost, compared to the existing self-referral methods. We propose a spatial method to show the extent of building envelopes, heating systems and temperature controls. Heating controls, which are not recorded in the United Kingdom Homes Energy Efficiency Database (HEED), but we believe would be considered good practice to maintain balanced temperatures around the house, and also potentially reduce the complexity in modelling the thermal zones. Additionally, heating controls are seen as compulsory in new building regulations, an eligible measure in Green Deal and Energy Company Obligations, and in the United Kingdom Department of Energy and Climate Change (DECC) heat strategy. This paper has taught us that the emerging picture surrounding local energy modelling and that, for example, singularities such as group heating and district heating (decentralised energy supply) have a great impact on final energy consumption calculations.
The identification of Standardized Regression Coefficients (SRCs) can be used to aid designers in identifying the most influential design variables affecting building performance. Nevertheless, multicollinearity can nullify the reliability of SRCs. This study aims to develop a method for estimating reliable SRCs of design variables in daylighting and energy performance of buildings in the face of multicollinearity. A parametric model is developed, then, daylight and energy simulations are commenced, next, Principal Component Analysis (PCA) is performed and the extracted Principal Components (PCs) are interpreted to meaningful factors, finally, the SRCs of the extracted PCs in daylighting and energy performance were calculated. The method was applied to a standard office, where the quantitative performance of (3564) different cases were predicted in terms of twelve urban context configurations against nine variables. Four PCs out of nine variables were extracted, namely, PC1: window geometry, PC2: glazing properties, PC3: opposing building paint, and PC4: building paint. The generated SRCs charts of the extracted PCs described the behaviors of the PCs in daylighting and energy performance. The Horizontal Obstruction Angle (HOA) affected the PCs’ behavior for all performance metrics, Annual Sunlight Exposure (ASE) was not affected by PC3 and PC4, PC1 had an inverse relationship with Lighting Energy (LE), whilst, the contribution of PC3 on LE was increased dramatically when a steep HOA existed. Total Energy Consumption (TEC) showed the same trend as Cooling Energy (CE). The resulted behaviors were consistent with the literature and confirmed the efficiency of the proposed method.
In the Mediterranean and North African regions, traditional vaulted roof forms have been widely used due to their significant influence on enhancing thermal indoor conditions. This research parametrically investigates the thermal performance of vaulted roofs, seeking a better understanding of the reciprocal relationship between the solar irradiance received by these roofs and the resulting energy consumption in the hot-arid city of Aswan (23.58 o N), Egypt. The methodological procedure is realized through two phases. The annual simulations of solar irradiance and energy consumption are carried out in the first phase, where the quantitative performance of 2310 different cases are predicted in terms of six vaulted roof forms against eleven key influencing variables. The unsupervised technique of Principal Component Analysis is used in the second phase to reduce the higher dimensionality of the resulting dataset and extract important information from newly established orthogonal principal components. The outcomes of this work aim to provide architects and practitioners with an optimized dataset to use in the design and application of vaulted roof forms and support decision makers addressing the development strategies by providing essential data for setting regulations of newly built environments in harsh hot-arid contexts.
Over recent decades, Egypt has experienced unprecedented growth of urban residential regions causing deterioration to indoor environmental quality. This research is a part of an ongoing study of building performance with different physical configurations and façades. It aims to quantify the daylighting and energy consumption of residential buildings in the hot-desert hybrid settlements of Alexandria. The methodological approach involves performing computational simulations to construct a dataset covering several influencing factors, which are exploited to develop multivariate linear regression models. This yields five equations used as proxies for predicting building performance in the early stages of the design. The developed models are validated and the predicted data, accompanied by insignificant errors, are found to be in a good agreement with simulation results, indicating that these models can explain the variation in the building performance. These measures, supported by an additional analysis of residuals, confirm the strength of selected variables and suitability of developed models to fit the dataset. This can aid architects and decision makers to assess a preliminary building performance by considering factors of heavily obstructed environment and façade configurations without the need to perform exhaustive analysis.
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In the early stages of building design, architects rapidly produce, explore, analyze, and eliminate design options. Ideally, energy analysis should accompany and inform each design option, but in reality, the creation of building models for thermal simulation is too slow to keep up with the pace of design exploration. This paper describes a framework for rapid energy analysis of architectural early design models. The framework consists of a flexible modeling protocol to be followed by the user, a standard communication protocol that may be implemented in virtually any architectural modeling software as a script or plug-in, and a translation protocol for automated production of energy models to be run in a stand-alone program simultaneously with the modeling environment. A prototype implementation of these protocols has successfully performed EnergyPlus analysis of early design stage architectural models created in SketchUp, Grasshopper for Rhinoceros, and 3ds Max. This allows timely feedback on building energy consumption to be displayed side-by-side with an actively changing architectural model.
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This paper describes the development of a new tool that allows designers to simulate and evaluate the daylight potential of urban master plan proposals. The tool is a plug-in for the Rhinoceros3D CAD modeler and follows a two-step workflow. During the initial step, hourly solar radiation levels on all facades within an urban scene are simulated based on Radiance/Daysim. During the second step, exterior radiation levels are converted into hourly interior illuminance distributions using a generalized impulse response. Climate based daylighting metrics, such as daylight autonomy, are also computed. The results yielded by the new method are carefully compared to regular and substantially more time-consuming Daysim simulations. This comparison shows that the overall daylit area in the investigated master plan matches Daysim predictions within 10%. Given its implementation into the Rhinoceros3D environment, as well as the almost instant simulation feedback, the tool may serve as a generative method for designers.
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Many attempts have been made to automatically convert architectural 3D models into thermal models for building performance simulation. This paper describes a method that is capable of abstracting an arbitrary building massing into a meaningful group of thermal shoebox models. The algorithm is meant to bridge the existing gap between architectural and thermal representations of the same building and to facilitate the use of energy models during schematic design by providing instant performance feedback from the massing stage onwards. The method uses varying facade insolation levels as the key form-related parameter. Discrete facade segments are then grouped by similarity of their local “solar microclimate”. Each group is represented by a reference shoebox model, which consists of a two-zone thermal model for perimeter and core regions. Computed shoebox results are then extrapolated and mapped back to the architectural model. Thus, the relationship between the simulation output and the provided architectural geometry is strengthened and easier to communicate. Combined with a parametric modeling environment, the method may be used to identify optimized local massing solutions. It can also be applied at the urban level to break down a whole neighborhood into a representative subset of simple thermal models, allowing the estimation of urban energy use intensity in a feasible and timely manner.
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Building energy simulation is valuable during the early stages of design, when decisions can have the greatest impact on energy performance. However, preparing digital design models for building energy simulation typically requires tedious manual alteration. This paper describes a series of five automated steps to translate geometric data from an unzoned CAD model into a multi-zone building energy model. First. CAD input is interpreted as geometric surfaces with materials. Second, surface pairs defining walls of various thicknesses are identified. Third, normal directions of unpaired surfaces are determined. Fourth, space boundaries are defined. Fifth, optionally, settings from previous simulations are applied, and spaces are aggregated into a smaller number of thermal zones. Building energy models created quickly using this method can offer guidance throughout the design process.
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Effective building performance simulation can reduce the environmental impact of the built environment, improve indoor quality and productivity, and facilitate future innovation and technological progress in construction. It draws on many disciplines, including physics, mathematics, material science, biophysics, human behavioural, environmental and computational sciences. The discipline itself is continuously evolving and maturing, and improvements in model robustness and fidelity are constantly being made. This has sparked a new agenda focusing on the effectiveness of simulation in building life cycle processes. Building Performance Simulation for Design and Operation begins with an introduction to the concepts of performance indicators and targets, followed by a discussion on the role of building simulation in performance based building design and operation. This sets the ground for in-depth discussion of performance prediction for energy demand, indoor environmental quality (including thermal, visual, indoor air quality and moisture phenomena), HVAC and renewable system performance, urban level modelling, building operational optimization and automation. Produced in cooperation with the International Building Performance Simulation Association (IBPSA), this book provides a unique and comprehensive overview of building performance simulation for the complete building life-cycle from conception to demolition. It is primarily intended for advanced students in building services engineering, and in architectural, environmental or mechanical engineering; and will be useful for building and systems designers and operators.
A key barrier to the acceptance of simulation within building design has been identified as the fact that it is not fully integrated into the design process. The project described in this paper attempts to address this barrier by embedding modelling as a standard component of design practice procedures within an architectural practice. Important elements of the research that are described in the paper are: • identifying the role building simulation can play at the different stages of design; • developing a model description that evolves through the design process as the building design becomes more highly specified; • simplifying the user interface at the early stage of the design where rapid feedback is required and where most impact can be made on the building’s energy and environmental performance; • customising results presentation to be appropriate for the particular stage of design; and • implementing these simulation concepts, observing their acceptability, and addressing quality assurance and training issues. Key words: Building design practice, outline design stage.