Approach to Reference Models for Building Performance Simulation

Sahil-Jai Arora

1,2, a

, Clara Ceccolini

1,3, b

and Markus Rabe

Bosch Thermotechnik GmbH, Junkersstraße 20-24, 73243 Wernau (Neckar), Germany

Department IT in Production and Logistics, TU Dortmund University, 44221 Dortmund, Germany

INATECH, Department of Sustainable Systems Engineering, Freiburg University, 79110 Freiburg, Germany

Keywords:

Reference Model, Conceptual Architecture, Procedure Model, Model-based Engineering, Design by Reuse,

Building Performance Simulation, Building Energy Systems.

Abstract:

In the ﬁelds of business process modeling, logistics, and information model development, Reference Models

(RMs) have shown to enhance standardization, support the common understanding of terminology and proce-

dures, reduce the modeling efforts and cost through the paradigm ”Design by Reuse”, and enable knowledge

transfer. The use of RMs in Building Performance Simulation (BPS) shows potential to achieve similar ben-

eﬁts. Firstly, to clarify the terminology adopted in the different ﬁelds, this paper presents a comprehensive

overview of the diversely interpreted deﬁnitions, beneﬁts, and attributes of RMs and related terms, including

classiﬁcation into common and uncommon understanding. Secondly, the paper transfers the approach of RMs

to BPS. A deﬁnition for RMs applicable to BPS is provided, the identiﬁed RM qualities are matched with

BPS’s challenges, and ﬁnally an example of an RM for simulation-based test benches is presented.

1 INTRODUCTION

Modeling and simulation of buildings and Heating,

Ventilation, and Air Conditioning (HVAC) systems

have become an established practice, both in the re-

search and industry, to manage the increasing com-

plexity of Building Energy System (BES) interactions

and tackle the global targets to their decarbonization

(Hasan et al., 2015). These applications are desig-

nated with the term Building Performance Simulation

(BPS). The use of simulation and computational mod-

els can support the BES life cycle (Hensen and Lam-

berts, 2019), from the design process until the com-

missioning and maintenance phases.

In general, model-based engineering, which

adopts models instead of directly realizing a solution

(van Beek et al., 2014), leads to frontloading efforts

during the development process. Therefore, it sup-

ports an early-stage concept veriﬁcation and, hence,

faster and more efﬁcient time-to-market, improving

the chances to detect errors early.

Nevertheless, BPS and likewise model-based en-

gineering induce several challenges. The design of

https://orcid.org/0000-0002-6877-1480

https://orcid.org/0000-0002-0238-8796

These authors contributed equally to this work.

reliable and accurate mathematical models is time-

consuming and compels experts and cost; therefore,

there is a need for model re-usability (Wetter, 2011).

Moreover, besides the intrinsic multi-disciplinary ap-

proach to BES (Singaravel, 2020), the increase in sys-

tem complexity (e.g., integration of elements of the

so-called Internet of Things) has resulted in a closer

interaction of various disciplines (Wetter, 2011) – ar-

chitecture, engineering, as well as IT and data sci-

ence. Consequently, the models’ transparency, their

ease of share, and common understanding among dif-

ferent experts become fundamental. Furthermore, fa-

cilitating knowledge transfer of BPS processes and

procedures would further spur its adoption across the

whole BES life-cycle (Tucker and Bleil de Souza,

2016). Eventually, a higher model abstraction and

modular approach helps reducing the comprehension

efforts as well as enhance simulation program debug-

ging and, additionally, model maintenance and porta-

bility (Wetter, 2011).

This study aims to illustrate the potential of Ref-

erence Models (RMs) in facing the BPS challenges

named. An RM is a conceptual framework ”for un-

derstanding the signiﬁcant concepts, entities, and re-

lationships of some domain, and therefore a ”founda-

tion” for the considered area” (Camarinha-Matos and

Afsarmanesh, 2008, p. 1).

Arora, S., Ceccolini, C. and Rabe, M.

Approach to Reference Models for Building Performance Simulation.

DOI: 10.5220/0010888800003119

In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2022), pages 271-278

ISBN: 978-989-758-550-0; ISSN: 2184-4348

271

RMs have been applied primarily in the ﬁelds

of information systems (Thomas, 2006; Becker and

Knackstedt, 2002), virtual enterprises (Camarinha-

Matos and Afsarmanesh, 2008), production and lo-

gistics simulations (Altendorfer-Kaiser, 2016; Rabe

et al., 2006), business administration, and informatics

(Bartsch, 2015). In these sectors, the most recurring

beneﬁt is the future time and cost efforts saving dur-

ing the development phase of new models, because an

RM enables the ”Design by Reuse” paradigm (van der

Aalst et al., 2006; Altendorfer-Kaiser, 2016; Rixe

and Augustin, 2020). Moreover, by standardizing

and systematizing best practices (M

uller et al., 2019),

they have proven to increase the quality of the to-be-

realized model (Thomas, 2006; Rabe et al., 2006).

Another recognized beneﬁt is that the use of RMs

leads to recommendations for actions to derive mea-

sures for improvements (Altendorfer-Kaiser, 2016;

Rabe et al., 2020). In addition, an RM fosters the

communication between different experts by bring-

ing together the subjective views (Bartsch, 2015); it

builds a foundation for a common terminology and

common procedures (Camarinha-Matos and Afsar-

manesh, 2008; Rabe et al., 2006). Less noted but still

relevant is that RMs guide simulation of logistic pro-

cesses allowing easier interaction with the simulation

models (M

uller et al., 2019). RMs enable knowledge

transfer (Dietzsch and Esswein, 1998) and serve edu-

cational purposes, such as employee training (Becker

and Knackstedt, 2002). Therefore, RMs for BPS have

the potential to achieve similar beneﬁts.

The authors present a comprehensive overview of

the diversely interpreted deﬁnitions, beneﬁts, and at-

tributes of RMs and the related terms. This inves-

tigation is necessary to clarify the terminology be-

fore transferring the term RM to BPS. As discussed

by Bartsch (2015), Thomas (2006), and Camarinha-

Matos and Afsarmanesh (2008), a generally accepted

understanding of the term RM cannot be found.

This paper is structured as follows: Section 2

presents the state of the art in understanding the term

RM. Section 3 documents the authors’ suggested def-

inition for the term RM in the ﬁeld of BPS as well

as the related attributes, showing the beneﬁts of RMs.

The understanding of RM is supported by an applica-

tion example in Section 4. In Section 5, conclusions

are drawn with an outlook for future scientiﬁc work.

2 UNDERSTANDING OF RMs

The term Reference Model (RM) emerged in the lit-

erature at the end of the 1980ies for the development

of industrial enterprise models and pertains to a class

of words that are often used but seldom clearly un-

derstood (Thomas, 2006). Reference modeling is the

process of developing an RM to be used for different

applications (Becker and Knackstedt, 2002). From

a pure etymological perspective, the term reference

model consists of the words reference and model.

These have respectively the meaning of ”quoting

something” and ”remarkably good example that can

be imitated” (Cambridge Dictionary, 2021c; Cam-

bridge Dictionary, 2021b). Nonetheless, an agreed

understanding of RMs is lacking, and diverse deﬁni-

tions are offered, depending also on the application

ﬁeld. Actually, the denomination RM is sometimes

used without any well-founded qualiﬁcation (Braun

and Esswein, 2006).

A model itself is an abstract formal representation

of a portion of the real world (Dietzsch and Esswein,

1998). A model can be used to understand, explain,

design, and implement a system (Becker et al., 1995;

Camarinha-Matos and Afsarmanesh, 2008).

Van der Aalst et al. (2006) report that RMs pro-

vide generic solutions for developing speciﬁc mod-

els. Bartsch (2015) adds that RMs are understood

as a speciﬁc manifestation of a general type of ab-

stract model having certain characteristics. Further-

more, Pajk et al. (2012) declare that RMs ”are generic

conceptual models that formalize recommended prac-

tices for a certain domain”. Accordingly, Rabe et

al. (2006) deﬁne an RM to be a conceptual frame-

work that includes a standard description of processes

and best-in-class practices. In Camarinha-Matos and

Afsarmanesh (2008) the authors state an RM to be

an ”abstract representation of a large number of pos-

sible systems” (Camarinha-Matos and Afsarmanesh,

2008). Eventually, Thomas (2006) offers a user-

centered deﬁnition: An RM is a user-accepted model

that can be exploited (and re-used) in supporting the

construction of another model. Based on this deﬁni-

tion, an RM requires that at least one application of it

can be found.

It can be noted that while there is no universally

agreed deﬁnition, there are nonetheless commonal-

ities to be found regarding their characteristics and

beneﬁts. Based on the investigated contributions, the

application of an RM is generically illustrated in Fig-

ure 1. First, the required elements of the RM are se-

lected. At the same time, also the required elements

of the model to be created are to be identiﬁed. These

two steps support each other iteratively, hence they

already represent an initial application of the RM.

Subsequently, the latter is to be applied profoundly

by substituting already developed elements from the

RM into the model to be created. Possibly, not all re-

quired elements are covered by the RM. These, there-

MODELSWARD 2022 - 10th International Conference on Model-Driven Engineering and Software Development

272

Figure 1: Exemplary application of a reference model.

fore, may need to be developed afterwards, but with

an overall signiﬁcantly lower effort.

Nevertheless, in order to use the full potentials of

RMs in the ﬁeld of Building Performance Simulation

(BPS), a deep understanding of RMs and their con-

ception is required. To meet the need for more trans-

parency and to later guide the identiﬁcation of an RM

deﬁnition applicable to the ﬁeld of BPS, in the follow-

ing the authors collect the RMs’ attributes and present

the clustering process that leads to their synthesis into

qualities.

2.1 RM Attributes and Qualities

By investigating seventeen relevant contributions, a

total of forty-one attributes of RMs are identiﬁed and

clustered to nine qualities (Figure 2). These qualities,

providing a profound understanding of RMs’ charac-

teristics, are reusable, ﬂexible, reliable, designed sys-

tematically, generally valid, required, user-centered,

comprehensive, and educative.

The attributes adaptable, applicable, customiz-

able, and conﬁgurable enable the RM to its quality of

reusability (Q1). On the one hand, there is a need for a

high abstraction level – abstract from speciﬁc features

– (Bartsch, 2015; Pescholl, 2020; Becker and Knack-

stedt, 2002), as the RM should be applicable to var-

ious homogeneous ﬁelds. On the other hand, a high

level of detail is required (Becker et al., 1997) to offer

guidelines to ensure the RM’s ease of use (Pescholl,

2020; Dietzsch and Esswein, 1998). This conﬂict of

goals goes together with the inconsistency in litera-

ture about whether an RM should be tool-independent

– only referring to them (Rabe and Friedland, 2000) –

or tool-related (Becker et al., 1997).

A modular and hierarchical structure consisting of

a composition of submodels, allowing a wide range of

choices (van der Aalst et al., 2006), leads to ﬂexibility

(Q2) (M

uller et al., 2019; Rixe and Augustin, 2020).

The quality of generally valid (Q3) consists of

the attributes universal, transferable, and valid in a

speciﬁc ﬁeld when meeting corresponding speciﬁed

conditions (Dietzsch and Esswein, 1998; Rabe et al.,

2020; Pescholl, 2020). Therefore, there is no claim to

an absolute universal validity, but to a general validity

in a class of applications (Thomas, 2006).

Noteworthy, qualities Q1, Q2, Q3 present fuzzy

boundaries as their attributes overlap. This is the case,

e.g., for the attribute customizable, which can be en-

tirely associated neither to the quality ﬂexible, nor

reusable, nor generally valid. Moreover, there is a

strong interrelation of the quality Q3 with Q1 as being

generally valid is necessary for the RM to be reusable.

In order for the user to be conﬁdent in applying an

RM, the quality of reliability (Q4) has to be ensured

(Dietzsch and Esswein, 1998). Accordingly, an RM

should be credible, e.g., by observing best practices

(Becker et al., 1997; Becker and Knackstedt, 2002;

Dietzsch and Esswein, 1998), as well as disclosing the

sources cited and the authorship (Camarinha-Matos

and Afsarmanesh, 2008). It should, in the best case,

already be validated or at least validateable (Pescholl,

2020; Bartsch, 2015). Finally, it is necessary that the

user accepts the model as a reference and that the RM

is applied at least in one case (Thomas, 2006).

Another identiﬁed quality is designed systemati-

cally (Q5) (Rabe et al., 2020). The RM should feature

a structured, compact (Pescholl, 2020; Rixe and Au-

gustin, 2020), and methodical design (M

uller et al.,

2019; Pescholl, 2020; Bartsch, 2015).

To justify the RM use, the quality of required (Q6)

is crucial. Attributes of this quality are the usefulness

and utility of the RM (Bartsch, 2015; Becker et al.,

1997) and, if applicable, its innovativeness (Bartsch,

Approach to Reference Models for Building Performance Simulation

273

CREDIBILE

BASED ON BEST

PRACTICES

VALIDATED

COMPACT

CARRY

KNOWLEDGE

METHODICAL

STRUCTURED

HAVE

RECOMMENDATION

CHARACTER

EASE OF USE

USE FORMAL

LANGUAGE

INNOVATIVE

BENEFICIAL

WELL

DOCUMENTED

VISUAL

DEFINE

TERM RM

SYNTACTICALLY

AND SEMANTICALLY

COMPLETE

SHOW

RELATIONSHIPS

PRESENT

SCENARIOS

OFFER

SPECTRUM OF

CHOICES

SUPPORT MODEL

VARIANT

ENABLE

BENCHMARKING

INCLUDE

MULTIPLE

PERSPECTIVES

ACCEPTED AS A

REFERENCE

ENABLE

KNOWLEDGE

TRANSFER

VISIONARY

PROVIDE APPLICATION

EXAMPLES

USED

USER-CENTERED

REQUIRED

EDUCATIVE

DESIGNED

SYSTEMATICALLY

COMPREHENSIVE

RELIABLE

VALID UNDER

SPECIFIC

CONDITIONS

APPLICABLE

TOOL RELATED

DETAILED

FLEXIBLE

GENERALLY

VALID

REUSABLE

COMPOSITE

TRANSFERABLE

ADAPTABLE

CONFIGURABLE

MODULAR

HIERARCHICAL

ABSTRACT

UNIVERSAL

TOOL INDEPENDENT

CUSTOMIZABLE

Figure 2: RM’s attributes clustering. Line marks overlap-

ping qualities: Reusable , Generally valid , Flexible .

2015; Becker and Knackstedt, 2002).

Being user-centered (Q7) is a fundamental qual-

ity of RMs. This quality implies ease of use (M

uller

et al., 2019), visualization character (Becker et al.,

1997; Bartsch, 2015), and providing a deﬁnition of

the meaning and the purpose of the RM (Dietzsch

and Esswein, 1998) together with its correct and ef-

ﬁcient use (Rabe et al., 2006). Ultimately, there is a

need for syntactic (well deﬁned linking and combina-

tion of elements) and semantic (well deﬁned content)

completeness (Rabe et al., 2020; Becker et al., 1997).

This semantic completeness is often supported by a

formal description technique (Becker and Knackstedt,

2002; Schubel et al., 2015; Rixe and Augustin, 2020).

An RM should include the quality of being com-

prehensive (Q8), both in its development and applica-

tion (Becker et al., 1997). By showing relationships

between activities and entities (Becker and Knackst-

edt, 2002), an RM can provide multiple perspectives

and scenarios of application depending on the cur-

rent boundary conditions (Becker et al., 1997; van der

Aalst et al., 2006). Q8 also enables a continuous im-

provement by allowing the benchmark of the as-is and

target status (Altendorfer-Kaiser, 2016; Becker et al.,

1997; Camarinha-Matos and Afsarmanesh, 2008).

The last identiﬁed quality is educative (Q9). RMs

are knowledge carriers (Becker et al., 1997; Becker

and Knackstedt, 2002; Dietzsch and Esswein, 1998)

and, therefore, provide recommendations by pre-

senting a default solution (Altendorfer-Kaiser, 2016;

Bartsch, 2015; Pescholl, 2020; Thomas, 2006).

2.2 Prioritizing the Compiled Qualities

The occurrence of the detected qualities in the respec-

tive contributions is counted to determine their preva-

lence, hence allowing for ranking them. Table 1 re-

ports in each line all the qualities that emerge within

the investigated contributions (i.e., also in their state

of the art chapters).

In particular, each row is intended as a summary

of several referenced publications, which are not pre-

sented individually in this study because of space con-

straints. However, the inclusion of several works both

by Becker et al. and Rabe et al. is justiﬁed because of

the evolutions in the authors’ opinion over time.

Within this investigation, there seems to be a

particular consensus regarding the qualities reusable

(Q1, 94%), generally valid (Q3, 76%), user-centered

(Q7, 71%), educative (Q9, 71%), ﬂexible (59%), and

comprehensive (53%). These qualities, which reach

prevalences above 50%, are, therefore, classiﬁed as

common perception of RMs. At this point, it should

be pointed out again that some qualities have fuzzy

boundaries (see Section 2.1). The quality ﬂexible, for

example, shows a high attribute overlap with reusable

and generally valid.

The remaining identiﬁed qualities are not shared

by the majority and are, thus, seen as additionally

annotated qualities. Regardless, a model should

be reliable (Q4, 35%) and systematic (Q5, 24%),

thus supporting trustworthiness, reusability, and user-

centricity. Increased reliability, for example, can be

achieved by an initial application or even validation

of the developed RM. The lowest-weighted quality is

required (Q6, 18%). This result is to be questioned

critically, as the requirement itself might already be

expressed by the creation of the RM. The detection

and occurence of these qualities is intended to show

the common and uncommon perception of the inves-

tigated contributions, but by no means to exclude un-

common perceptions. Instead, the goal is to provide

the fundamentals for a viable deﬁnition and general

understanding of RMs in order to integrate them to

the ﬁeld of BPS.

3 TRANSFERRING THE RM

APPROACH TO BPS

As stated in Section 2, a model is ”something that a

copy can be based on because it is an extremely good

example of its type” (Cambridge Dictionary, 2021b);

it is an abstract formal representation of the inves-

tigated portion of the world (Dietzsch and Esswein,

1998). Consequently, as a premise to this chapter, the

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274

Table 1: Perspectives on the qualities of a reference model.

Common perception Uncommon perception

Reusable

Generally valid

User-centered

Educative

Flexible

Comprehensive

Reliable

Systematic

Required

van der Aalst et al., 2006 • • • •

Altendorfer-Kaiser, 2016 • • • •

Bartsch, 2015 • • • • • •

Becker et al., 1997 • • • • • • • •

Becker and Knackstedt, 2002 • • • • • • •

Camarinha-Matos and Afsar-

manesh, 2008

• • • • • •

Dietzsch and Esswein, 1998 • • • • • •

uller et al., 2019 • • • •

Pajk et al., 2012 (Pajk et al., 2012) • • •

Pescholl, 2010 • • • • • • •

Rabe and Friedland, 2000 • • •

Rabe et al., 2006 • • • • •

Rabe et al., 2009 (Rabe et al., 2009) • • •

Rabe et al., 2020 • • • •

Schubel et al., 2015 • • •

Thomas, 2006 • • • • • • •

Rixe and Augustin, 2020 • • • • •

Occurrence (%) 94 76 71 71 59 53 35 24 18

authors underline that it should not be misunderstood

as a building or HVAC simulation model, which rep-

resents a digital counterpart of physical phenomena

and is used for predicting and understanding their dy-

namics.

The investigation on the qualities of RMs (see Ta-

ble 1) indicates the presence of ambivalent perspec-

tives, which is partly the result of different needs of

different application ﬁelds. Consequently, Section 3.1

presents the author’s general deﬁnition of RMs (based

on the state of the art), which is applicable likewise to

BPS and is essential for transferring the RM method-

ology to the latter ﬁeld.

3.1 RM Deﬁnition Proposal

An RM is a holistic collection of methodologies sys-

tematically structured in an architecture in which ev-

ery element (e.g., guidelines, methods, procedures,

and entities, ...) is made transparent and outlined as

a generic solution based on both best practices and

innovative approaches.

The main objective of such an RM in the ﬁeld of

BPS is empowering ﬂexible reuse of existing knowl-

edge and practices, spurring the adoption of model-

based engineering, in turn, leading to efﬁcient devel-

opment of high-quality solutions.

Based on this deﬁnition, RMs require a conceptual

architecture (synonym: framework) that itself is an

organized view of the RM (Figure 3).

As reported in Camarinha-Matos and Afsar-

manesh (2008), an architecture deﬁnes a speciﬁc sys-

tem in an abstract way. This architecture is a logical

collection of elements that should be described with

the help of a modeling language, which emerges to

meta models. A meta model is a model of a model

”describing the syntax of the model and generalizing

the semantic” (Rabe et al., 2020, p. 7) with the help of

a meta language (Bartsch, 2015) (e.g., Uniﬁed Mod-

eling Language (UML), System Modeling Language

(SysML), programming languages, ...). As depicted

in Figure 3, meta models can be regarded as the ar-

chitecture’s foundation. There may be different meta

models needed for various purposes depending on the

described element.

Typical architecture’s elements are guidelines

(Camarinha-Matos and Afsarmanesh, 2008), which

suggest predetermined procedures or good practice

methods that can be adopted to ease a particular pro-

cess (Cambridge Dictionary, 2021a). Other com-

mon elements of the architecture are maturity mod-

els, which are usually linked to a process. They are

perceived as benchmark tools to evaluate weaknesses

and strengths of the as-is status that later can be used

to guide optimization or better lead to recommenda-

tions for actions. Exemplary tools can be part of the

architecture, supporting the understanding and appli-

cability of the RM as well as allowing the RM valida-

Approach to Reference Models for Building Performance Simulation

275

GUIDELINES

META MODELS

EXEMPLARY TOOLS

MATURITY MODELS

PROCEDURES

ENTITIES

METHODS

(e.g., develop the simulation

model, commissioning, ...)

(e.g., model validation,

calibration, ...)

(e.g., TRNSYS, MATLAB/Simulink,

EnergyPlus, OpenModelica, ...)

PHYSICAL

(e.g., stakeholders,

technicians, ...)

NON PHYSICAL

(e.g., norms, weather

databases, ...)

FURTHER ELEMENTS

(e.g., benchmark reference)

Figure 3: Reference architecture overview.

tion. Noteworthy, Figure 3 shows that elements can

have different abstraction levels; this is the case, for

example, of the element entities and its sub-elements

physical and non-physical.

A systematic design (Q4) of the RM is enhanced

by an elements’ classiﬁcation, making the latter easier

to apply. Because classiﬁcations highly depend on a

speciﬁc use case, they are not detailed further in this

paper, which addresses a broad BPS perspective.

3.2 Facing the Challenges of BPS

The RM deﬁnition provided in Section 3.1 comprises

the qualities identiﬁed in Section 2.1. Therefore, ap-

plying the approach to an RM in BPS supports facing

the challenges outlined in Section 1.

The RM qualities of being reusable (Q1) and ﬂex-

ible (Q2) allow for overcoming the development ef-

forts related to time and cost. It leads, for example,

to avoid the development of a new simulation model

from scratch. Generally valid (Q3) ensures an easier

and wider transfer of the established practices in a cer-

tain BPS domain to another, e.g., district and urban or

residential and commercial building modeling.

By collecting and systematically presenting

methodologies in a conceptual architecture (Q5) and

by being reliable (Q4), an RM fosters standardiza-

tion, which leads to a quality increase of the result-

ing applications of BPS through the whole BES life-

cycle, as well as cross-study benchmarks (e.g., com-

pare modeling assumptions). Additionally, the ed-

ucative (Q9) and user-centered (Q7) characteristics,

thanks to disclosing the adopted methods and pro-

cedures, can be used not only to transfer knowledge

from experts to non-experts, but also as a teaching

instrument for students or inexperienced employees.

Q7 combined with comprehensive (Q8) lead to more

overall transparency of the simulation models, pro-

moting, in turn, their ease of sharing, understanding,

debugging, and conducting maintenance.

4 AN RM FOR

SIMULATION-BASED TEST

BENCHES

To enhance the understanding of the presented RM

beneﬁts and clarify how an RM application in the ﬁeld

of BPS would look like, in the following, the authors

present a preliminary example.

The performance of newly developed building

control systems can be assessed by the three comple-

mentary approaches (a) ﬁeld-test, (b) emulation, and

of the system to compute the Key Performance In-

dicators (KPIs) and verify as well as benchmark the

tested control’s quality.

This model-based concept is promising to over-

come disadvantages related to approaches (a) and (b),

when regarding test coverage as well as time and

cost efforts. Nevertheless, since such a simulation-

based approach relies on BPS, it faces the challenges

outlined in Section 1. Furthermore, there is the

need for a higher standardization and systematiza-

tion of approach (c) to ensure results generalization,

fair cross-study comparisons, and high test quality

(Stopps et al., 2021). Approaches (a) and (b) are es-

tablished in the industry, while the potential of (c)

tends to be not fully acknowledged.

As presented in Section 3.2, RMs are a viable an-

swer to the stated challenges. Therefore, an RM for

implementing virtual test environments to assess the

performance of building control systems is conceptu-

alized. To ensure the RM ﬂexibility (Q2), the archi-

tecture is modular and consists of four categories and

nine classes (Figure 4). These identiﬁed elements (of

the architecture) result from grouping the procedures

and methods required to develop a virtual testing con-

cept. Moreover, to design the RM architecture, the

trade-off between the level of model abstraction and

granularity has been considered to ensure its reusabil-

ity (Q1) (Rabe et al., 2006). As depicted in Figure 4,

the categories are (1) what-if scenario identiﬁcation,

(2) simulation model design, (3) performance assess-

ment, and (4) results benchmark.

Category (1) describes the methods to identify

the parameters and variables required to generate the

what-if scenarios to ensure a sufﬁcient test cover-

age and quality (avoid the ”garbage-in, garbage-out”

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276

paradigm). In particular, this category contains the

classes test location, occupants, building, and HVAC

system, which identify the representative features of

the built environment. Category (2) guides the design

of the simulation model. Except for the class simula-

tion environment, all the contained classes are covered

also by category (1). The design of the simulation

model affects the input data that are required to char-

acterize the what-if scenarios. Consequently, the au-

thors recognize that categories (1) and (2) must over-

lap to ensure the RM comprehensiveness (Q8). Note-

worthy, the class simulation environment leads the

implementation of the virtual test on software. The

performance assessment methodologies are present in

category (3) that allows deﬁning the reference con-

trol for the benchmark and the evaluation metrics

(KPI selection). Category (4) guides the result post-

processing and benchmark phases: Once the raw data

are available, effective result visualization and a test

report are to be ensured for attracting the stakehold-

ers’ interest and supporting the design phase of the

control under test.

Each class consists of a set of structured steps,

which the user should follow to implement a

simulation-based benchmark in a standardized and

systematic way. Based on the use case scenarios, ﬂow

charts or activity diagrams offer the user a panorama

of choices (Q8) and problem solving know-how (Q9).

TEST LOCATION

OCCUPANTS

BUILDING

HVAC SYSTEM

REFERENCE CONTROL

RESULT VISUALIZATION

TEST REPORT

KPI SELECTION

SIMULATION ENVIRONMENT

CATEGORY (3)

PERFORMANCE

ASSESSMENT

CATEGORY (1)

WHAT-IF SCENARIOS

CATEGORY (2)

SIMULATION MODEL

CATEGORY (4)

RESULTS

BENCHMARK

REFERENCE MODEL

Figure 4: Conceptual architecture of the reference model.

5 CONCLUSIONS

BPS shows optimization potential in terms of mod-

eling effort and costs, practice reusability, and stan-

dardization as well as knowledge transfer; this can be

tackled by applying RMs. However, due to the di-

versely interpreted deﬁnitions, beneﬁts, and attributes

of RMs in different established domains, the prereq-

uisite for transferring RMs to BPS is the creation of

terminological transparency.

For this reason, the state of the art regarding the

understanding of RMs is shown. Further, RMs’ at-

tributes are collected and clustered into nine qualities,

which characterize and distinguish them. The even-

tual transfer of RMs to BPS is achieved by providing

a uniﬁed deﬁnition of RMs, valid also for BPS, based

on the identiﬁed qualities and by matching the latter

with the challenges of BPS.

This study provides a ﬁrst approach to RMs for

BPS, which offer the beneﬁt of collecting both best

practices and innovative approaches with a user-

centered approach, in a structured and systematic

way. However, it shows the need for further scientiﬁc

work. A main research focus is the pursuit of tangi-

ble application of the introduced approach to BPS by

developing mature reference models, e.g., for the in-

troduced example and other promising BPS use cases,

such as data analytics in reliability prognosis.

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