=Paper=
{{Paper
|id=Vol-3824/paper4
|storemode=property
|title=Web of Simulation Ontology (WoSO): Integration of Building Performance Simulations in IoT Systems
|pdfUrl=https://ceur-ws.org/Vol-3824/paper4.pdf
|volume=Vol-3824
|authors=Zehor Hounas,Maxime Lefrançois,Antoine Zimmermann,Bruno Traverson
|dblpUrl=https://dblp.org/rec/conf/ldac/HounasLZT24
}}
==Web of Simulation Ontology (WoSO): Integration of Building Performance Simulations in IoT Systems==
https://ceur-ws.org/Vol-3824/paper4.pdf
Web of Simulation Ontology (WoSO): Integration of
Building Performance Simulations in IoT Systems
Zehor Hounas1,2,* , Maxime Lefrançois1 , Antoine Zimmermann1 and Bruno Traverson2
1
Mines Saint-Etienne, Univ Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, F-42023,
Saint-Étienne, France.
2
Électricité de France Recherche et Développement (EDF R&D), Palaiseau, France.
Abstract
Buildings are the single largest energy consumer in Europe. therefore, it’s crucial to increase their energy
efficiency. In this context, however, building performance simulations (BPSs) can play an important
role in supporting energy-efficient design and operations of buildings. Furthermore, the integration of
Internet of Things (IoT) systems into building management can enable significant improvement in energy
efficiency strategies. The synergy between BPSs and IoT systems holds great potential for optimizing
energy management in buildings, paving the way for a significant reduction in energy consumption.
For this vision to come true, BPSs and IoT systems need to interoperate as part of a smart building
management system. This paper addresses this interoperability challenge at the semantic level, by
introducing the Web of Simulations Ontology (WoSO) as a high-level description of BPSs and IoT system.
WoSO focuses on capturing interaction between simulations and IoT systems by extending a reference
IoT ontology (SAREF) to include simulations as a component of the extended IoT system. Simulation
modeling builds upon the Functional Mock-up Interface (FMI) specification, a widely adopted standard
for describing simulation functionalities.
Keywords
Ontology, Simulation model, Building performance simulation, Internet of things.
1. Introduction
The building is the most energy-consuming sector, amounting to 42% of final energy consump-
tion in France in 2021 [1]. Therefore, acting on the management of energy consumption is
key to saving energy building sector. In this context, we investigate the possible optimization
approaches of the Internet of Things (IoT) control system of smart buildings. We identified
three factors that have a significant impact on energy consumption in IoT control systems: (1)
weather, (2) human activity, and (3) physical phenomena that occur in smart buildings. For
instance, thermal transfers occur between different zones of the building, such as from the office
to the hallway.
Physical phenomena occurring in a complex and heterogeneous connected Cyber-Physical
System (CPS), e.g. smart building, are poorly taken into account in current IoT applications.
These physical phenomena are often represented by Building Performance Simulations (BPS)
LDAC 2024: 12th Linked Data in Architecture and Construction Workshop, June 13–14, 2024, Bochum, Germany
*
Corresponding author.
$ zehor.hounas@emse.fr (Z. Hounas); maxime.lefrancois@emse.fr (M. Lefrançois); antoine.zimmermann@emse.fr
(A. Zimmermann); bruno.traverson@edf.fr (B. Traverson)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
50
which uses mathematical models to simulate the dynamics of the CPS based on observations of
connected objects.
Integrating this class of models to IoT systems reinforces a well identified issue facing the
IoT field: heterogeneity. Indeed, complex CPS such as smart buildings are composed of several
interacting heterogeneous subsystems. This heterogeneity makes the IoT system prone to the
challenge of interoperability [2]. The exchange of the data between the cyber and physical
components and its understanding have been identified as major challenge in the literature [3].
Extensive researches are led in the IoT field, by different academic and industrial entities,
focusing on semantic interoperability issue. They leverage semantic web technologies (such as
ontologies) to tackle the high fragmentation of IoT systems. For example, the World Wide Web
Consortium (W3C)1 introduces the Web of Things (WoT) [4, 5] as a standard architecture/model
for semantic interoperability.
For the building performance simulation, Functional Mock-up Interface (FMI) [6, 7] stands
out as the leading interoperability standard in the simulation industry. It addresses diversity of
modeling tools, and therefore of modeling formats and languages, studied in several research [8,
9, 10] by facilitating the exchange of dynamic simulation models among various tools in
a standardized format. It also allows the same model to be executed independently of the
modeling tool. Nevertheless, addressing the interoperability between simulation models and
other applications, such as IoT applications, remains a pending challenge [11, 12].
To overcome this challenge, BPSs and IoT systems need to interoperate as part of a smart
building management system. This requires effective data exchange between these heteroge-
neous systems. Reaching a consensus on shared data model (ontologies) enables to ensure
semantic interoperability among different components of the smart building management sys-
tem. Additionally, leveraging linked data principles enhances the interoperability further, as it
enables the creation of a web of interconnected and interrelated data, fostering a more holistic
and integrated approach to smart building management.
In this paper, we presents the Web of Simulations Ontology (WoSO) as a core vocabulary
providing a high-level description of BPS, modeling two different facets. For the IoT aspect,
the simulation is viewed as a component of the extended IoT system, relying on the SAREF
ontology standard for its representation. For the BPS aspect, it relies on the Functional Mock-up
Interface Specification to identify and describe information related to the core functionality of a
simulation.
Organization The rest of this paper is organized as follows. Section 2 presents concrete and
practical scenarios. Section 3 introduces the ontology objectives and requirements. Section 4
presents some of the relevant research works that propose ontologies in both IoT and BPS
domains. Section 5 describes the main steps of the ontology development process and the
overview of the ontology. In Section 6 “case study” we implement the WoSO ontology to the
scenario A depicted in the second section. Finally, in Section 7, we conclude with synthesis of
the work already done and future work.
1
World Wide Web Consortium (W3C) https://www.w3.org/
51
2. Motivating Scenarios
We present a concrete and practical context in which interoperability between BPS and IoT
systems is required to optimize energy management. By extension, requires the definition of
WoSO. Through these examples, we describe the tasks that need to be supported by WoSO and
serve as a basis for defining its requirements and evaluation tests.
Scenario A: Office heating control Mr. John works in Office 123 that has a heating control
system consisting of a thermostat and a heater. Mr. John is typically in his office from 8 a.m. to
6 p.m. on working days, but some days he is out of the office. His calendar is shared on the Web.
The temperature setpoint is 15°C during the night, and 19°C when Mr. John is in his office. The
temperature setpoint should be reached before Mr. John arrives, but not too early before so as
to save energy. To do so, the heating control system determines when Mr. John will arrive based
on his calendar, and how long it would take for the office to reach the temperature setpoint
based on a thermal simulation of the office.
Scenario B: Energy performance monitoring Ms. Smith lives in a house powered by
photovoltaic panels and equipped with an IoT control system that provides real-time information
about the house’s energy production and consumption. She wants to increase the temperature
of the house by 3°C without consuming more energy than it produces so as not consume the
energy reserves. To do so, the IoT control system determines how much energy the house
will consume if the temperature is raised, and how much energy the photovoltaic panels will
produce according to the weather forecast, based on the energy performance simulation.
3. Requirements
The main contribution of the paper consists of an ontology, called Web of Simulations Ontology
(WoSO). It provides a common vocabulary and structured representation of building performance
simulations, enabling a shared and standardized understanding of the forecasts and predictions
made based on BPS and its relationships with the data of the IoT system. This promotes more
effective aggregation and integration of the BPS data in the decision making process of the IoT
system.
The design and development choices that have been made in the development of the WoSO
ontology are driven by the following requirements:
Req.1: The ontology module has to enable the representation of the simulations described by
the FMI standard.
Req.2: The ontology module has to be compliant with the reference ontologies in IoT.
Req.3: The ontology module has to manage the data exchange between simulations.
The commitment to adhering to these requirements ensures that our ontology can be seam-
lessly integrated with other systems and applications that adhere to the same standards, pro-
moting compatibility and data exchange. overcome data interoperability challenges in both IoT
and BPS domain.
52
4. Related ontologies for the IoT and BPS domains
In this section we identify reference ontologies in the IoT domain and select the one we align our
ontology with. We, also, explore existing BPS ontologies to evaluate their relevance and coverage
according to our requirements and positions our research to fill gaps in current knowledge
representation in BPS field.
4.1. IoT ontologies
With the ongoing expansion of components within the IoT landscape, there is a continual
emergence of new solutions aimed at addressing their heterogeneity and allow interoperability
across platforms, ecosystems, and devices. As a result, a multitude of ontologies have been
developed to meet the requirements according to context-specific needs of IoT Applications [13,
14, 15].
Great works and efforts have been made in past years to comprehensively encompass the IoT
domain in a standardized way. For example, 58 ontologies with IoT tag are referenced on the
Linked Open Vocabulary (LOV).
The selected ontologies for our study are: Sensor, Observation, Sensing, Actuation / Semantic
Sensor Network (SOSA/SSN [16]) and The Smart Applications REFerence (SAREF [17, 18]).
SSN/SOSA, porposed by the joint W3C and Open Geospatial Consortium (OGC)2 , is specifi-
cally crafted for modeling and representing sensor and actuator networks, observations, actions
and related entities, providing standardized depiction of sensors, actuators, elements such as
samples and their relationships within networks. it finds application in scenarios where accurate
and standardized representation of sensor network is crucial [19].
SAREF, proposed by European Telecommunications Standards Institute (ETSI)3 , on the other
hand, is designed for the semantic modeling and representation of smart appliances and devices
within the IoT landscape, providing a standardized way to describe the devices, the appliances
and their services, functions, and interactions. The primary focus of SAREF is on smart devices
commonly found in scenarios where the representation of smart appliances is essential for
seamless integration and communication within the IoT application.
While they share common goals of providing semantic representations for IoT concepts and
both being considered as references from standards bodies, each of them models different aspects
of the Internet of Things (IoT) and sensor-related domains. The specialized application of SAREF
in domains like home automation and smart buildings, where semantic clarity regarding smart
appliances is paramount, makes SAREF more tailored to the energy management applications
targeted in our research.
4.2. BPS ontologies
The complexity of building systems and the need for interdisciplinary collaboration have led to
the adoption of ontologies as a means to enhance knowledge representation and interoperability
within the BPS domain.
2
Open Geospatial Consortium (OGC) https://www.ogc.org/
3
European Telecommunications Standards Institute (ETSI) https://www.etsi.org/
53
Pritoni et al [20] present a survey of ontologies for building design, energy modeling, occu-
pants and behavior and building energy applications across the building life cycle. Regarding
energy modeling, it presents ontologies that describes Building Information Modeling (BIM),
for instance: Industry Foundation Classes (IFC), Green Building XML (gbXML), ifcOWL, and
IoT ontologies as described in the previous section 4.1. However, it does not mention ontologies
that represent the Building performance simulation itself.
Indeed, The data exchange for the extended building-simulation domain and even for the
entire Architecture, Engineering, Construction, Owner, Operator (AECOO) industry is widely
covered topic in the literature [21, 22]
In [23], the authors propose an ontology-based automatic framework which can integrate
data from different sources and generate Building Energy Management (BEM) models with
thermal zoning automatically. In their approach, they consider four key information domains
for BEM, weather, building, internal heat gain and HVAC system to integrate data from various
information sources in a single Data model: Building Energy Management model (BEM). Ac-
cordingly, four ontology components are designed and constitute the whole ontology model of
BEM: Brick schema, Building Topology Ontology (BOT), weather ontology model and building
energy models. A similar effort was conducted by Bjorskov et al. [24], they propose a framework
for automated and adaptable energy model development to provide the simulation models re-
quired by building DTs. The framework builds upon the Smart Applications REFerence (SAREF)
ontology to ensure interoperability.
The purpose of both frameworks [23, 24] is to provide a single data model that represent all
the data resources needed for simulation to be execution, but don’t include the representation of
simulation data in the data model. However, it is necessary for the simulation to be represented
in the data model so that it can be used as a resource by the other components of the system, as
advocated in our approach.
The Physics-based Simulation Ontology (PSO) [25] models physical phenomena based on the
perspective of classical mechanics involving partial differential equations and the information
artefacts that are about the physical phenomena. This representation focuses on physics
problems that govern the physical phenomena modeled in the simulation models and don’t
include their relationships with other domains.
The FMUont ontology [26] is the closest to what we want to achieve. It focuses on the
interoperability between the simulations, it is developed in order to derive connections between
FMUs. the structure of FMUont is designed to relate variables of single FMU to pre-defined
objects to other domains, established ontologies that are linked to ensure compatibility with
other fields. However, this ontology considers only FMU format for simulation models and it
doesn’t define some concepts for instance: model and simulation.
5. Web of Simulation Ontology (WoSO)
Our approach seeks to provide domain-agnostic solution and focuses on two vertical domains:
IoT and BPS, which are common in the Smart Building domain. Therefore, we introduce WoSO:
an ontology that represents the semantic description of the BPS domain and the relationship
between BPS and IoT domains.
54
We applied the ACIMOV methodology [27], which is an agile ontology development method-
ology that adopts DevOps principles and provides tools (e.g. GitLab template) to improve
accessibility and organization of the ontology development process.
The rest of this section is organised as follow:
Competency questions In this section, we defines a list of competency questions that illus-
trate the technical requirements. Accordingly, the scope and the limits of the ontology
are defined.
Overview of WoSO ontology The conceptualisation is the process of enumerating the terms
and entities within the scope. then these terms, entities and relationships are illustrated
in a diagram.
Evaluation In this section, the correctness and the completeness of the ontology is assessed
according to the ability of the model to answer the CQs.
WoSO is published at the following URL:
https://purl.org/woso#
5.1. Competency questions
Based on the scenarios depicted in the Motivating Scenarios section and the requirements
listed in the section 3, we raised a total of 10 competency questions, including 5 BPS-oriented
competency questions, and 5 IoT-oriented ones. They are available in the supplementary
material of this article, an some of them are listed below.
BPS-oriented competency questions are based on the model description of the FMI specifica-
tion:
CQ1 What is the model executed by the simulation?
CQ2 What are the inputs, outputs, and parameters, of the simulation?
CQ3 What are the start time, end time, and duration, of the simulation?
CQ4 What tool was the simulation model generated with, and when?
CQ5 What is the format of the model?
To ensure the alignment to the latest version V3.2.1 of SAREF [17], we adapt the SAREF
reference ontology patterns [28] to building performance simulations. SAREF focuses on the
concept of device, which is defined as a tangible object designed to accomplish a particular task
in IoT system. Therefore, a saref:Device offers a service (the saref:Service saref:isOfferedBy min
1 saref:Device). Moreover, a saref:Device can measure a property, such as saref:Temperature
and saref:Energy.
Even though a simulation model is not a tangible object, it is still a component of the
IoT system that produces and consumes data. A BPS accomplish the task of simulating, and
55
offers a function. It can also act upon some features or properties, such as forecasting and
predicting values for these properties. Moreover, a simulation may consist of other simulations
(co-simulations). A simulation model can be executed by a device.
Accordingly, we formulate the following competency questions:
CQ6 Which features of interest are represented by the simulation model?
CQ7 What device made the execution of the simulations?
CQ8 What properties the simulation model predicts?
CQ9 Which function the simulations accomplishes?
CQ10 Which services the simulations offers?
5.2. Overview of the WoSO ontology
The WoSO ontology is a OWL 2 DL ontology that consists of 5 classes, 6 object properties, and
15 data properties. WoSO has two main classes highlighted in bold in Figure 1:
woso:SimulationModel This class refers to a mathematical model for the calculation of the
system state variables based on equations describing a physical or abstract system, it
has data properties to describe the metadata listed in FMI specification. For exemple:
woso:hasName, woso:hasVersion, woso:generationTool, woso:generationDateAndTime
and so one.
woso:Simulation A simulation is the execution of the woso:SimulationModel under certain
condition, it also has data properties: woso:hasName, woso:hasExecutionStartTime,
woso:hasExecutionEndTime. Object property woso:isExecutionOf links a woso:Simulation
to the woso:SimulationModel it is an execution of.
The inputs, outputs, and parameters of a simulation model are described in natural lan-
guages using datatype properties woso:hasInputDescription, woso:hasOutputDescription, and
woso:hasParameterDescription. SAREF object properties saref:hasInput and hasOutput are
also applicable, if the description of inputs and outputs requires more structure.
A simulation is linked to its actual inputs, outputs, and parameters, using object prop-
erties saref:hasInput, saref:hasOutput, and woso:hasParameter. WoSO defines the class
woso:SimulationVariable that may be used to type objects of these properties.
A woso:PredictingFunction is a function (saref:Function) that allows to transmit data from
or to a woso:Simulation such as its inputs and outputs.It is linked to the woso:Simulation
with the object property saref:hasFunction. A woso:PredictionService is a type of service
(saref:Service) that exposes the woso:PredictingFunction on a network. A Service is offered by
(saref:isOfferedBy) a woso:Simulation.
The high level terminology of the ontology is shown in the diagram depicted in Figure 1.
56
Figure 1: Overview of the WoSO ontology using the Chowlk visual notation [29].
5.3. Evaluation
To ensure the overall quality and effectiveness of the WoSO ontology, we evaluate the ontology to
assess its correctness and completeness. The correctness evaluations focus on logical consistency
and semantic integrity, the completeness evaluations assess the coverage of all the needed
concepts and relations.
To do so, we verify that the competency questions defined at the beginning of the development
of WoSO are covered by the classes and properties and the queries responses are the ones
expected. We first translate the CQs listed in the section 5.1 into SPARQL queries, then execute
them over the ontology. The following list shows the CQs and the corresponding SPARQL
queries.
CQ1 What is the model executed by a simulation?
SELECT * WHERE { $s woso:isExecutionOf ?m }
CQ2 What are the inputs, outputs, and parameters, of the simulation?
SELECT ?i WHERE {?s saref:hasIntput ?i}
SELECT ?o WHERE {?s saref:hasOutput ?o}
SELECT ?i WHERE {?s woso:hasparameter ?p}
57
CQ3 What are the start time, end time, and duration, of the simulation?
SELECT ?s ?st ?et ?d WHERE {?s woso:hasExecutionStartTime ?st.?s woso:
hasExecutionEndTime ?et.?s woso:hasExecutionDuration ?d}
CQ4 What tool was the simulation model generated with, and when?
SELECT ?m ?gt ?gdt WHERE { $m woso:generationTool ?gt . $m woso:
generationDateAndTime ?gdt }
CQ5 What is the format of the model?
SELECT * WHERE { $m woso:format ?f }
CQ6 Which features of interest are represented by the simulation model ?
SELECT * WHERE { ?m woso:models ?f}
CQ7 What device made the execution of the simulations?
SELECT * WHERE { ?d saref:madeExecution ?s }
CQ8 What properties the simulation model is related to?
SELECT * WHERE { ?m woso:isRelatedToProperty ?pr }
CQ9 Which functions the simulation accomplishes?
SELECT * WHERE { ?s saref:hasFunction ?fn }
CQ10 Which services the simulation offers?
SELECT * WHERE { ?s saref:offers ?srv }
58
6. Case study
In this section, we implement the ontology WoSO according to the first scenario depicted in the
section 2. We created a fictive knowledge graph, with instances of the elements described in
the scenario. Then, we execute the SPARQL queries over the knowledge graph to verify that
the answers match the expected ones.
Listing 1 instantiates the ontology to represent the first scenario of Section 2.
a saref:FeatureOfInterest ;
rdfs:label "Office 123"@en .
a saref:Device ;
rdfs:label "Thermostat"@en .
a woso:SimulationVariable ;
rdfs:label "Current Temperature"@en .
a woso:SimulationVariable ;
rdfs:label "Day time Temperature Set Point"@en .
a woso:SimulationVariable ;
rdfs:label "Night Time Temperature Set Point"@en .
a woso:SimulationVariable ;
rdfs:label "Heater State"@en .
a woso:SimulationVariable ;
rdfs:label "Simulation Step"@en .
a saref:Function ;
rdfs:label "Heater State Prediction"@en .
a woso:PredictionService ;
rdfs:label "Heating Control Prediction"@en .
a woso:SimulationModel ;
rdfs:label "Thermal Model"@en ;
woso:models .
a woso:Simulation ;
rdfs:label "Thermal Simulation"@en ;
woso:isExecutionOf ;
saref:hasInput ,
,
;
saref:hasOutput ;
woso:hasParameter ;
saref:offers ;
saref:hasFunction ;
saref:madeBy .
Listing 1: Instantiation of WoSO according to the scenario A “Office heating control”.
59
7. Conclusion and future works
In this paper, we introduced the Web of Simulation Ontology (WoSO) as a foundational frame-
work for integrating Building Performance Simulations (BPS) into Internet of Things (IoT)
systems, with the aim of optimizing energy management in smart buildings. WoSO provides a
high-level description of BPS and IoT systems, relying on ontology reference of the IoT domain:
SAREF and a widely used standard in the BPS domain: the FMI standard. It addresses the
interoperability challenge at the semantic level, enabling effective data exchange and interaction
between these two domains.
The ontology development process follow ACIMOV ontology engineering methodology and
involves ontology engineers and domain experts. The formal model is constructed from the
conceptual model using OWL and Turtle, we also generate a documentation.
We have several perspectives for this work. First, we update the competency questions
continuously to maintain the ontology and extend it. Then, we assess the execution time and
the results of the queries that require reasoning capabilities. In parallel, we are working on
the implementation of WoSO for the use case of building energy management efficiency. The
building is a tertiary building located on EDF R&D site, and is equipped with an IoT control
system and a building thermal model. We have access to the data space where the IoT data
is stored and to the library of BPS: BuildSysPro. The aim is to assess its effectiveness and its
performance in data exchange between IoT and BPS.
Current ontology development relies primarily on FMI standard and SAREF ontology and
focuses only on Physics-based Simulations. A potential improvement is to explore other
standards and solutions of model exchange (other than FMI), in order to broaden the ontology
application scope. For instance include human activity simulation.
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