=Paper=
{{Paper
|id=Vol-3081/05paper
|storemode=property
|title=Real-time building performance monitoring using semantic digital twins
|pdfUrl=https://ceur-ws.org/Vol-3081/05paper.pdf
|volume=Vol-3081
|authors=Alex Donkers,Dujuan Yang,Bauke de Vries,Nico Baken
|dblpUrl=https://dblp.org/rec/conf/ldac/DonkersYVB21
}}
==Real-time building performance monitoring using semantic digital twins==
https://ceur-ws.org/Vol-3081/05paper.pdf
Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Real-Time Building Performance Monitoring using
Semantic Digital Twins
Alex Donkers1[0000-0002-8809-3277] and Dujuan Yang1[0000-0003-3124-6604], Bauke de Vries1[0000-
and Nico Baken1
0002-7885-8221]
1 Eindhoven University of Technology, Groene Loper 6, 5612AZ Eindhoven, The Netherlands
a.j.a.donkers@tue.nl
Abstract. Higher building performance saves operational costs, helps sustaina-
bility goals, and increases the comfort, satisfaction, productivity, and health of
occupants. However, models simulating the occupants’ perceived performance
of indoor environments require highly heterogeneous input data. This paper aims
to show how semantic web technologies can help to overcome problems deriving
from this heterogeneity issue. Literature is studied to categorize the necessary
input information for modeling occupants’ perceived building performance. Two
perspectives were consulted, namely the Indoor Environmental Quality approach
and the Healing Environments approach. Based on the results, a core IEQ data
model, integrating building topology, static properties, and dynamic properties,
is proposed. We introduced a method to integrate this information by using the
Building Performance Ontology (BOP). This method was tested by manually
transforming the Open Smart Home (OSH) data to BOP vocabulary. Time-series
data from various sensors were stored in InfluxDB and connected to the linked
building data. A custom-built python tool was created to compute the thermal
comfort of the OSH using the PMV/PPD method. The results indicate that a com-
mon data structure for static and dynamic properties helps to integrate the heter-
ogeneity input data. Furthermore, we showed that adding occupants’ data or sys-
tem information could improve thermal comfort through multiple reactive ap-
proaches.
Keywords: Linked Data, Semantic Web, Building Performance Ontology, Ther-
mal Comfort, Building Performance
1 Introduction
Monitoring building performance is essential to improve quality of life. Apart from
static performance indicators, such as office layout, colors, and materials [1, 2], dy-
namic performance indicators such as the indoor environmental quality (IEQ) heavily
influence the occupant’s satisfaction with a building.
Kim and Dear found that workplace satisfaction is largely influenced by noise dis-
traction [3], temperature, air quality, and visual comfort [1]. The indoor climate also
influences productivity. Lee et al. [4] found a correlation between IEQ complaints and
student learning performance. Office workers’ productivity is amongst others affected
Copyright 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0)
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
by thermal comfort, indoor air quality, noise, acoustics, and lighting [2]. Experiments
by Geng et al. [5] show that the optimal productivity is reached at a neutral or slightly
cool temperature. Next to productivity issues, multiple researchers identified health-
related problems due to bad indoor conditions. Fabian et al. [6] found that improving
the IEQ significantly reduces asthma events. Patino and Siegel [7] concluded that poor
IEQ in social housing negatively affects health conditions. Fisk et al. [8] found how
low thermal comfort and air quality could increase the short-absence of knowledge
workers. Eventually, poor buildings could lead to symptoms of the Sick Building Syn-
drome (SBS) [8].
Creating a better indoor environment should therefore be a key strategy in real estate.
However, multiple challenges exist. Currently, building managers in commercial build-
ings have no disposal to the full set of tools that is necessary to monitor, control, and
optimize the indoor environment for its occupants [9]. According to Adeleke and
Moodley [10], monitoring IEQ is often cumbersome, capital intensive, and dependent
on manual processing.
The booming development of the IoT domain is promising. Multiple literature re-
views [11, 12] evaluated the opportunities of the integration of IoT and BIM. Boje et
al. [12] explain how such semantic enrichment of building data could result in dynamic
cyber-physical models, or Digital Twins. Those models help to improve building per-
formance [13], facility management [14], monitoring [12], energy management [15],
and many more. However, the data to create those Digital Twins often originates from
heterogeneous sources and is stored in heterogeneous data formats [12].
This paper aims to show how semantic web technologies could be applied to inte-
grate heterogenous data for monitoring building performance cases. Section 2 describes
the state-of-the-art building performance monitoring and the IEQ parameters accompa-
nied with this. It ends with a summary of semantic web ontologies related to this do-
main, and how they could be used to integrate the different types of data. Section 3
describes the method of this study, including a short description of the BOP ontology
and the data used in this study. Chapter 4 discusses the results. The paper ends with a
conclusion and discussion.
2 Building performance and the semantic web
Multiple literature studies indicated the potential of integrating the IoT domain with
building information. One of the major assets of IoT development is the possibility to
sense the indoor environment using sensors. Building performance indicators related to
sensor data are often part of the indoor environmental quality (IEQ)-umbrella. Al Horr
et al. [16] extensively studied how building performance indicators related to IEQ in-
fluence occupants’ well-being, and categorized these into indoor air quality (IAQ), sick
building syndrome (SBS), thermal comfort, acoustic comfort, and visual comfort.
Central in the IEQ approach is the use of physical quantities - often measured by
sensors – to compute people’s comfort [17]. The performance is often expressed using
a calculated or measured value and could be compared against guidelines or other build-
ings [17, 18]. These values could be calculated using various methods depending on
the building and its available systems, often varying in spatial and temporal procedures
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
[19]. Current semantic web ontologies in the AEC domain often lack the expressivity
to address this spatiotemporal resolution.
The deterministic nature of IEQ methods results in unambiguous methods to im-
prove the building performance; a building manager simply needs to improve the vari-
ables used in their calculation methods. Variation in layout, function, and systems does
require a tailored approach for each building, however, multiple ontologies in the AEC
domain have proven to add the semantic expressivity related to these variations [20–
22]. Common strategies to improve the IEQ during the operational phase of a building
are reducing polluting sources and improving natural ventilation (to improve air qual-
ity) [16], changing temperature or air velocity (to improve thermal comfort) [16, 18,
23], reduce noises and improve communication privacy (to improve acoustic comfort)
[16] and increase (day-)light and reduce glare (to improve visual comfort) [16].
Frontczak et al. [24] argue that the influence of IEQ parameters differs per indivi-
dual, and also varies for different countries (with different outdoor climates). Paradox-
ically, occupants prefer to have individual control over the IEQ, even if this leads to
lower task performance [25].
Willems et al. [17] argue that the IEQ paradigm approaches environmental percep-
tion as a passive, deterministic process, monitored by sensors. They researched the
healing environments (HE) approach as a counterpart of IEQ, focusing more on the
dynamic interaction between mind, body, and space. In HE, perception of the environ-
ment is intertwined with the actions, emotions, and cognitive system of individuals
[17], as well as their emotions. Different affective phenomena (such as preferences,
attitudes, mood, affect dispositions, and interpersonal stances) influence the emotions
and thus the experience of space [26]. This experience is always triggered by multiple
sensations simultaneously and approaches to model this experience should therefore be
multi-sensory [17]. Richer information about people’s preferences would be needed to
model such epistemic acts.
A gap between designed, measured, and perceived building performance could be
identified [27]. High scores on various environmental assessment methods (LEED [28],
BREEAM [29]) were found to not significantly improve perceived comfort levels.
Combining IEQ and HE methods is expected to reduce this gap [17]. A combination of
those methods could only be achieved by integrating a wide range of heterogeneous
data. Based on the reviewed methods related to IEQ and HE, a core linked data model
for IEQ should integrate both building topology (e.g. by using BOT [20]), static prop-
erties (e.g. by using BOP [30] or PROPS [31]) and dynamic properties (using for ex-
ample SOSA [21], OPM [22] or BOP [30]).
Boje et al. [12] emphasize the opportunities of data integration to better adapt buil-
ding performance to occupant needs while simultaneously optimizing resources. There
has not been scientific consensus on the optimal data integration method for building
information models with time-series data yet. Tang et al. [11] mention five integration
methods. Three of these methods don’t use semantic web technologies and either store
BIM data in relational databases or directly query from IFC models. Since these meth-
ods don’t solve the heterogeneity issues of a building’s lifecycle data, two semantic
web approaches have been proposed by Tang et al. [11].
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
In the first SWT approach, both the BIM data and the sensor data are converted to
RDF. Using unique identifiers (for example, GUIDs), the data in both RDF files could
be integrated. A python script, a web application, or any other custom-built tool could
query information from both datasets. Multiple SWT developments hint at using this
approach. Almost any sensor-related ontology describes classes for storing time-series
data (e.g. sosa:Result, seas:Evaluation, saref:Measurement), including a
literal and a unit. A general argumentation to do so is that RDF is well able to describe
the context of a time-series measurement, adding the necessary semantic richness. Mo-
reira et al. [32] however argue that such representations are not suitable for exchanging
real-time sensor data, as the payload per message would be too high. Therefore, they
introduced the s4ehaw:TimeSeriesMeasurements class which links to an array of
xsd:float values using an s4ehaw:hasValues datatype property.
The second SWT approach does store building data using RDF but keeps the time-
series data in its original format (storing it in either a relational or time-series database).
Hu et al. [33] used this approach to integrate sensor data and BIM data using the sensor
ID. They described the context of the measurement using SSN but stored the measure-
ments in a relational database. Similar approaches were applied by Van Gool et al. [34]
and Petrova et al [35]. Esnaola-Gonzalez and Diez [36] argue that time-series databases
are most suitable to store IoT data, as this result in the best querying performance.
The integration of static properties with topological BIM data using RDF seems
common research practice. Some ontologies advocate a one-file approach (e.g.
ifcOWL), while others follow the decentral nature of the semantic web, combining
multiple domain ontologies [9, 33, 34]. Different levels of detail could be found in the
various approaches describing static properties. The shortest route links a feature of
interest directly to a literal, using the object property to describe the measured property
(e.g. in simpleBIM [37] or PROPS [22]). Multiple ontologies (BIMDO [38], BPO [39])
introduced an intermediate ‘property’ class to support easier querying. The OPM on-
tology of Rasmussen et al. [22] introduces a second intermediate class
(opm:PropertyState) allowing the description of temporary properties, also imple-
mented by OMG [40].
A unified approach to integrating the different heterogeneous data categories is cur-
rently lacking. The complexity of building performance models is therefore not yet
covered by semantic web technologies. Data integration might lead to reducing the gap
between designed, measured, and perceived building performance.
3 Method
The concept of linked data for building performance will be demonstrated in a case
study monitoring the thermal comfort of the Open Smart Home (OSH), an existing
apartment in Nuremberg, Germany. The original dataset [41] was edited in Revit to add
multiple sensors. An RDF Turtle file [42] (following the BOT [20] ontology for topol-
ogy and the BOP [30] and QUDT [43] ontologies for properties) has been manually
created and stored in Ontotext GraphDB. To ease the process, this conversion is in-
spired by a conversion using the LBD-to-RDF converter [31]. The added properties
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
include thermal resistance, geometry, and orientation. The file semantically describes
the context of the sensors, such as the properties they measure and the databases their
data is stored in. Following the second SWT approach (chapter 2), the sensor data of
the OSH – which originally came as .csv files – was converted to the InfluxDB line
protocol format and imported to the InfluxDB database mentioned in the RDF Turtle
file of the OSH.
A custom-built python tool was developed to query the different datasets from their
databases. The tool first queries information from GraphDB using the SPARQLWrap-
per 1 library. The results are used to query sensor data from InfluxDB using the in-
fluxdb 2 library. The predicted mean vote and percentage of people dissatisfied
(PMV/PPD) methods [18] were used to calculate thermal comfort. Using the pyther-
malcomfort [44] library, the PMV/PPD could be calculated by using the dry-bulb tem-
perature, mean radiant temperature (Tmr), the air velocity, relative humidity, metabolic
rate of the occupant, and clothing insulation of the occupant. The dry-bulb temperature
and relative humidity were acquired from sensors and the air velocity is set to 0.1 m/s
by default. The Tmr could be calculated using the equation 1:
∑𝑛𝑛
𝑖𝑖=0(𝑇𝑇𝑠𝑠,𝑖𝑖 ∗𝐴𝐴𝑠𝑠,𝑖𝑖 )
𝑇𝑇𝑇𝑇𝑇𝑇 = ∑𝑛𝑛
(1)
𝑖𝑖=0(𝐴𝐴𝑠𝑠,𝑖𝑖 )
where: Tmr = mean radiant temperature [°C]
Ts,i = indoor surface temperature [°C]
As,i = surface area [m2]
The indoor surface temperature was computed by using the dry-bulb temperature
and relative humidity (returned from the sensors), area, tilt and solar azimuth of walls,
windows, doors, floors, and ceilings (queried from the OSH graph), meteorological data
(accessed via local weather stations) and the insulating properties of all the boundary
faces of the room (queried from the OSH graph). Weather data has been acquired via
CustomWeather [45]. Different from [34], this paper uses bot:Interface to create
interfaces between walls and spaces to determine the surface area, to overcome the ge-
ometric issue of walls covering multiple spaces in IFC.
Personal influences, such as metabolic rate and clothing insulation, remain variables
in the tool to allow personalization of the thermal comfort assessment. By default, they
were set to 1.0 met and 0.7 clo respectively.
The results of the PMV/PPD calculations were compared against the ASHRAE-55
standard [18] based on work by Loomans et al. [46]. Next to this, personal influences
on the IEQ will be tested by advising the occupant to wear extra clothes and by activat-
ing the radiator. A visual overview of the workflow is given in figure 1.
1 https://pypi.org/project/SPARQLWrapper/
2 https://github.com/influxdata/influxdb-python
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Fig. 1 Heterogeneous data conversion workflow to calculate the thermal comfort
The Open Smart Home has been converted to RDF using multiple domain ontolo-
gies. The topology of the building is described following the BOT [20] ontology. To
integrate all the static and dynamic properties with the topology, the BOP [30] ontology
has been used (figure 2). The ontology allows static property results to be stored directly
in the RDF file. The ontology does store the necessary context of the time-series data
in RDF but stores the actual measurements in a time-series database. To represent dif-
ferent types of properties, QUDT’s quantitykind and unit ontologies [43] were used.
Fig. 2 The Building Performance Ontology (BOP)
Figure 3 shows the resulting linked data structure of the OSH. The colors represent
the rdf:type’s of each instance, adopted from the BOP ontology. Both static and dy-
namic properties are described similarly and could be queried using similar SPARQL
queries. BOT has been used as a core ontology to describe the room and its building
elements. In practice, those elements often overlap with the bop:FeatureOfInter-
est. Since bot:Element and bop:FeatureOfInterest are not necessarily the
same things, the ontologies are used next to each other instead of aligning them using
alignment modules. Interfaces are used to represent the boundaries between a space and
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
a building element, such as walls, windows, and doors. Static and dynamic properties
are both described as instances of bop:Property. The state of each property is de-
scribed using bop:Result and is linked to the property using the bop:hasProper-
tyState object property. Dynamic properties hold information about the time-series
database their measured states are stored in. Only one unit-instance is used to describe
the units of the entire time series.
The sensors in this example measure the properties of the room while being hosted
at a wall. By separating the property (connected to :Bedroom) and the sensor (con-
nected to :Wall_01), the graph adds information about the viewpoint of the sensor,
adding the necessary spatial resolution of the sensor data for complex IEQ computa-
tions.
Fig. 3 Representation of static and dynamic properties of the Open Smart Home
4 Results
Loomans et al. [46] presented multiple assessment methods for assessing the thermal
comfort of a building. Their most detailed method for buildings in operation assesses
the PPD, PMV, and the stability of the environment. While the PPD and PMV could be
calculated at any given time, the stability metric measures the percentage of the oper-
ating/occupation time in which the PPD and PMV criteria are met. The criteria are
given in table 1.
Table 1 Assessment criteria of indoor thermal comfort
Assessment PPD [%] PMV [-] Stability [%]
Excellent <6 -0.2 < PMV < 0.2 ≥ 95%
Good <6 -0.2 < PMV < 0.2 ≥ 80%
Adequate < 10 -0.5 < PMV < 0.5 ≤ 80%
Acceptable < 15 -0.7 < PMV < 0.7 ≤ 80%
Poor ≥ 15 PMV < -0.7 or PMV > 0.7 ≤ 80%
Using the custom-built python script and the assessment criteria in table 1, the PPD
and PMV were assessed over a full day (June 1st, 2017). Figure 4 shows how the initial
situation scores poorly. As the figure indicates, there is a direct relationship between
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
the indoor temperature measured by :TemperatureSensor_01 and the PPD; once
the temperature drops, the PPD rises.
Fig. 4 Temperature and the assessed PMV/PPD
Two strategies to improve thermal comfort were tested. Since the temperature affects
the thermal comfort directly, the first approach tries to automatically trigger the radiator
to increase the temperature. The radiator in this room is connected to the same temper-
ature property as the sensor by using the bop:actsOn object property. The script has
been adapted to set the room temperature to 23.5 °C once the PPD or PMV assessment
drops to ‘poor’. Figure 5 shows how this could increase the PMV and decrease the PPD.
After increasing the room temperature, the PPD and PMV levels stayed within ‘ade-
quate’ boundaries for 100% of the time.
Instead of controlling systems, the linked building data could also be used to give
personal advice to the occupant, for example using a mobile device or an in-home dash-
board. Both metabolism and clothing insulation are personal factors influencing ther-
mal comfort; clothing insulation is the easiest variable to change. By advising the oc-
cupant to wear an extra long-sleeve shirt, his/her clothing insulation would increase by
approximately 0.3 [23, 44]. Figure 5 shows how such advice, triggered by an increasing
PPD (at 04:18), could improve the perceived thermal comfort directly. After putting on
an extra shirt, the PMV and PPD stayed between the ‘excellent’ boundaries for 99,1%
of the time, resulting in an ‘excellent’ thermal comfort assessment.
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
Improvement strategies for PPD
14
12
10
8
6
4
2
0
00:06
00:46
01:36
02:27
03:17
04:07
04:58
05:48
06:38
07:28
08:19
09:09
09:59
10:49
11:40
12:30
13:20
14:10
15:00
15:50
16:40
17:29
18:18
19:09
19:59
20:50
21:40
PPD [%]
Radiator Extra t-shirt
Fig. 5 Improving the PPD at 04:18, radiator and extra t-shirt scenarios
5 Conclusion and discussion
Monitoring building performance is a crucial step towards improving the quality of life
inside buildings. The complex building performance indicators which simulate indoor
environmental quality require the integration of a wide range of information. Current
practice showed that this information is often siloed and heterogeneous. This paper
aimed to show how semantic web technology could be used to integrate the myriad of
information to automate the process of monitoring building performance.
By representing the Open Smart Home as linked data – using the BOT and BOP
ontologies – a custom-built python tool was able to query the topology, static proper-
ties, and dynamic properties from a graph database and automatically query the corre-
sponding sensor data from a time-series database. By doing so, the thermal comfort of
a space could be computed in real-time. While this paper focused on IEQ, the workflow
presented in figure 1, in which linked building data is used to navigate to the correct
time-series data, could be generalized and used for various use-cases such as energy
management, facility management or smart building control.
Using linked data, the python tool can provide the user with different opportunities
to improve the building performance. Both a reactive method (triggering the radiator)
and a recommendation (advising the occupant to put on an extra t-shirt) were applied,
resulting in a drop of dissatisfaction with the thermal comfort.
Improving the building performance is an interesting research direction that deserves
further attention in the near future. To improve the current methods, future research
should aim at individualization of the monitoring and control of buildings. Research
should also aim at predictive methods to shift from reactive to proactive improvement
methods.
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Proceedings of the 9th Linked Data in Architecture and Construction Workshop - LDAC2021
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