A Basis for Adaptable Intelligent Buildings
Hubert Grzybek*, Stephen Gulliver and Zhuotao Huang
Informatics Research Centre, University of Reading, Ground floor, Building 42, Whiteknights, Reading,RG6 6WB, U.K.
Keywords: Intelligent buildings, Industry Foundation Classes, Temporal database, Dynamic Building Model.
Abstract: Current ‘intelligent’ buildings (IB) lack the crucial requirement of being adaptable. Without being dynamic
and adaptable, buildings can only be called ‘responsive’ or ‘automated’, and therefore continue to perform
sub-optimally due to their reliance on manual calibration and inability to automatically recognise patterns in
building usage. It is proposed that the development of an open, dynamic and temporal building model will
alleviate many of these problems. This will facilitate IB software to automatically and continuously re-
calibrate itself in order to achieve personalised occupant, environmental and energy consumption goals. The
aim of this paper is to examine the main standards and technologies that are currently used in the field of
intelligent buildings, and highlight deficiencies in their ability to support adaptable intelligence. In addition
we propose a novel solution and prototype, combining IFC and temporal database theory, in order to discuss
the implications and application of practical deployment.
There is currently no universally accepted definition
for ‘intelligent buildings’ (IB). Various definitions
differ in focus and emphasis, however most IB
authors share the belief that an IB should use
modern technology to improve resource efficiency
and provide a comfortable environment for
occupants. The concept of ‘intelligent’ systems,
however, stipulates that such systems should be
capable of ‘learning’ and automatically calibrating
itself to react to changes in its environment, in order
to continuously ensure optimal running (Fritz,
2010). This notion contrasts, however, with current
IB software, which focuses entirely on building
automation and visualisation of building system
states by responding to a given set of conditions with
pre-programmed actions. Current IB software is
subsequently unable to dynamically respond to
changes in its environment and adapt its behaviour
to varying usage patterns over time. For the purpose
of this paper, adaptability and flexibility relate to a
building’s ability to automatically recognise and
respond to changes in its environment, including the
‘learning’ of recognised patterns in recorded data.
Within a dynamic environment, responsive systems
will therefore require continual reconfiguration if
they are not to become ineffectual or obsolete.
If dynamic adaptability is essential within the
'intelligent building' definition, we would have to
agree that the first IB is yet to be designed. Current
IB software is incapable of supporting such
intelligent buildings, due to their inability to create
and make use of historical data relating to the
changing state of the building, its lack of
information concerning objects with the space, and a
lack of a machine-readable semantic building model
to allow information to be placed in context of the
building space. Historical data is crucial to
supporting ‘learning’, as it supports data mining and
the recognition of patterns in use of resources and
space (Cheng, 2006). This lack of historical data and
its link with a semantic building model is currently a
huge obstacle preventing buildings from responding
dynamically to the changing patterns of building
usage and dynamic occupant behaviour.
Whilst current intelligent building technology is
capable of improving building performance in terms
of resource efficiency and the provision of a
comfortable environment for occupants, they do not
meet the requirements of adaptability and flexibility.
Changes in business process, organisational
Grzybek H., Gulliver S. and Huang Z.
DOI: 10.5220/0003259000240031
In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page
ISBN: 978-989-8425-26-3
2010 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
structure and/or building use risks impacting
building performance, yet can only be optimised via
constant manual reconfiguration. To automatically
recognise and respond to change, buildings must be
capable of benchmarking critical performance
metrics, in relation to a set of building goals, and
have the ability to recognise when the building’s
performance is sub-optimal as a result of changes in
its environment. The authors believe that, to
understand and adapt to such changes, IB software
must maintain an accurate temporal model of the
building to provide a complete current and historical
representation of: the building’s structure; the
location and state of its contained spaces; occupants
or objects of interest; as well as the changing
relationships between these occupants and objects.
Such records support data mining and the
recognition of patterns in occupant behaviour and
space usage, which is critical to supporting building
adaptability and learning.
This paper examines current standards and
technologies in the field of IB and explains why the
current technology is insufficient. Moreover we
propose a novel solution, via the introduction of a
temporal building model prototype that we hope will
stimulate research ideas in this area. The rest of the
paper is divided into the following sections: in
section 2 we discuss current building standards and
technologies (including BMS, BIM and IFC) and
relate them to the requirements of adaptability. In
section 3 we give a summary of temporal database
theory, its applicability to IB, and a description of
the proposed building model standard - including
expected advantages and potential drawbacks.
Finally, in sections 4 and 5, we present our
prototype, which showcases the feasibility of the
proposed IB model. We conclude by summarising
the key points of the paper and expanding the
implications of this technology.
2.1 Building Management Systems
BMS products support the integration of the
building’s current systems, i.e. HVAC, lighting
security, etc. and the automation of specific actions,
such as the locking all entrances at a specified time.
A BMS can be defined as a system for centralising
and optimising the monitoring, operating, and
managing of a building. Services may include
heating, cooling, ventilation, lighting, security, and
energy management.
The functional focus of BMS products is on
peripheral connectivity and actuator responses to
pre-defined sensor inputs or timers. BMS systems
support communication between the software system
and the building’s sensors and actuators through a
communication bus, and provide building managers
with the capability of setting automated behaviours
related to these peripherals, and viewing the state of
the building’s systems through a friendly user-
interface (Knibbe, 1996).
Whilst standards in communications protocols
exist, for example LonWorks and BACnet, BMS
vendors still focus on the development of rule-based
systems, and do not support self-adapting model
based systems. The installation of typical BMS
products involves manual calibration, typically
involving the setting of building-specific rules and
the maintenance of the various parts of the system.
Once running, the system is incapable of
benchmarking its own performance, recognising
patterns in the building’s usage and suggesting
improvements that will meet the occupant’s
objectives in terms of resource usage and occupant
comfort. As a result, current BMS products do not
fulfil the adaptable/flexible requirement of truly
intelligent buildings (Clements-Croome, 2004). It is
possible, however, that existing BMS system data
could be used by a temporal building model to track
building usage over time, and store this historical
data to support data mining and learning.
2.2 Building Information Modelling
BIM aims to shift the construction industry away
from 2D CAD (Computer Aided Design) building
blueprints to full 3D, object-based models backed by
semantic information. It also aims to provide a
paradigm shift in the way that building project
stakeholders interact, distribute responsibility, share
information and collaborate in order to reduce costs,
increase value for the customer and remove the
current legislative culture (Smith et al, 2009).
BIM tools and techniques aim to support
construction project stakeholders in collaboratively
developing a complete virtual building model, which
includes all details relating to project management,
e.g. the building’s structure, construction schedule,
tasks and deadlines, etc. The benefits include
increased time and resource efficiency, as all
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stakeholders can create and share information that
would otherwise lead to repeated data collection.
BIM tools typically support the development of 3D
visualisations of buildings through the open and
object-based IFC format.
By moving away from 2D drawings, which
include basic CAD objects such as lines and arcs,
towards software objects, which represent physical
parts of a building, its contents, and how objects
relate to each other, the model is able to carry much
more semantic information for use by both people
and computers. It is this semantic information, not
the 3D visualisation, which we believe to be critical
to achieving truly adaptable systems. By allowing
computers to understand building models, BIM
software is capable of achieving advanced features,
such as automated rule-based building design and
standards checking. Parametric objects, for example,
can automatically re-build themselves according to
simple embedded rules, such as requiring a window
to be wholly within a wall. As the objects are
machine readable, spatial conflicts in a building
model can be checked automatically, resulting in
greatly reduced quantity of errors and change orders
(Eastman, 2009).
Object-based semantic models have great
potential in terms of adding dynamic properties to
BMS, and support the potential of truly intelligent
buildings, however current use of BIM is limited to
use by Facilities Management (FM) in tasks such as:
scheduling routine maintenance, finding objects
within a building, and checking maintenance-related
It is clear that an ‘as-built’ BIM model could
provide most of the data required for a dynamic
building model. However, to support the adaptable
requirement this data would first need to be
extracted into software that supports the
management of this information in a temporal
fashion, i.e. to allow for the information to change
whilst recording information relating to all past
states to support learning. The structural information
for such a model is defined in BIM IFC files, which
will be discussed in more depth in section 2.3.
2.3 Industry Foundation Classes (IFC)
To reduce data duplication and inconsistency, BIM
proponents use a single building model per project,
which is then distributed amongst multiple
specialised applications which are designed to
collaborate. BIM applications typically store this
building data using the open IFC object model. The
IFC model provides an object-oriented description
of the building and related services, enabling
interoperability between different vendors of
Architectural, Engineering and Construction /
Facilities Management software (Spearman, 2007).
The model supports both step formatted text and
XML, and therefore it can be used by software tools
across all platforms. The format is supported by
numerous CAD software vendors, including
Graphisoft , AutoDesk and Bentley Systems . These
CAD tools allow for the development of 3D building
models, which are semantically marked-up, i.e.
window objects are added to the model instead of
abstract block references made up of individual
An excellent example of the potential of
semantic models within the construction industry is
described by Wu et al (2004) who developed a
system for generating space model graphs based on
data from IFC files. This was possible primarily
because the IFC format describes the relationships
between objects, e.g. doors and spaces – a function
that is not possible without semantic modelling. This
functionality allows for quicker and easier analysis
of building space, but the same technique can be
extended to support automated path creation,
facilitating user navigation support of complex
buildings. Whilst the design of the IFC format
clearly provides potential for sophisticated and
powerful functionality, it is currently severely
hampered by the support of a static building model,
i.e. it is not currently possible to include the
dimension of time, thus facilitating the storage and
usage of historical data. Spearpoint (2003) identified
that the IFC model only provides a static view of a
building and that this is insufficient for the purpose
of running building simulations.
We propose a solution that merges the
semantically rich, yet static, building information
provided within IFC files with the innately dynamic
capabilities of temporal databases and object-
oriented software, thus supporting the development
of truly intelligent buildings. Such models would
allow IB software to mine, learn and dynamically
respond to all sensed building state changes as well
as user interactions.
Temporal databases differ from typical ‘snapshot’
databases by supporting the management and
querying of historical data. Snapshot databases store
only the current state of records i.e. each change in
an object’s state replacing the previous state through
SQL (Structured Query Language) ‘update’
statements. Likewise, SQL delete statements are
used in order to remove records that are no longer
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
current, e.g. a customer cancelling their subscription
to a particular service which effectively implies that
the customer never had a relationship with the
company. In contrast, historical databases do not
delete records (without administrator intervention)
and only update and insert new records in order to
maintain an object’s historical audit trail. This
supports data mining and the recognition of trends as
results of object states and aggregate statistics can be
compared at different time points.
Temporal databases manage time values in one
of two ways: i) valid time and ii) transaction time.
Valid time denotes the time period during which a
fact is true with respect to the real world.
Transaction time is the time period during which a
fact is stored in the database. Some databases also
support a bi-temporal mode by including both valid
time and transaction time support.
In terms of building information, a temporal
database could be used, for example, to record
temperature changes within defined spaces. By
assigning a ‘start’ and ‘end’ (valid time) timestamp
to each record, it is possible to develop a complete
history of temperature fluctuations in different
rooms, and these records could be searched in order
to find the temperature of a given room at any past
point in time, or used to find the average
temperature during a period of time. This is one,
fairly simplistic example, however, the potential to
creating temporal relationships gives rise to much
more advanced functionality, including the ability to
support mobile sensors. If sensors in a building were
able to move between spaces, their readings could be
linked to the correct space objects. In this instance it
would be possible for the sensor to create records for
each temperature reading, but for each reading to be
attributed to the correct room. As a result of the
potential to create temporary relationships between
objects, and track the full history of all object
interactions, IB software would be provided with a
wealth of knowledge that could be mined to
recognise patterns to support resource efficiency and
user well-being optimisation.
There are, however, several difficulties to be
overcome in the development of a temporal database
based IB model, including: the complexity of design,
implementation and mining of data, as well as the
potential performance problems due to the complex
requirements of managing records. Perhaps the
greatest obstacle to the adoption of temporal
databases, however, is the lack of support from
database vendors.
Whilst some vendors, particularly Oracle, have
recently made efforts to include temporal features
into their database products, no single product
supports all the required features which are required
for a true temporal database. As a result the
management and querying of temporal data is still a
cumbersome and complex process due to the lack of
supported temporal features in existing RDBMS
(Relational Database Management System)
products. This state of affairs is due, at least in part,
to the exclusion of temporal features from the SQL
ISO standard.
In addition to the technical difficulties, there are
also social obstacles to be overcome, especially
when attempting to use such a database for the
purpose of tracking and recording occupant
behaviour, as such a system threatens damaging the
trust relationship between staff and management.
Moran et al. (2010) investigated this problem and
provided suggestions as to how organisations should
deal with the issues including: how data is collected
and for what purpose, access rights to the data and
the length of time temporal data is held by the
In response to the need for a dynamic building
model standard, we propose the use of an adaptive
building model based on IFC and temporal
databases. The IFC model is capable of providing a
complete, yet static, representation of a building’s
structure and content, whilst the temporal database
will handle the recording, management and retrieval
of all object state changes. Figure 1 shows a diagram
of the proposed solution including the relationship
and differences between the proposed building
model and the currently available BMS technology.
A prototype system was developed using object-
oriented technology in order to support a continuous
simulation of the building, where each item has a
corresponding software object. Any change to these
objects, as reported by building sensors, is
immediately updated in the run-time software
objects and the related database records. The current
version of the prototype focuses on the simulation of
sensor instances linked to spaces and supports the
recording of sensor readings in a temporal database
as well as the visualisation of these readings.
For the sake of simplicity, the prototype
currently supports a variable number of temperature
sensors that can be set to create a new reading every
n number of seconds for a specified time period. The
readings are recorded in a temporal database that can
be visualised with a simple web-based interface. The
simulation was developed using an open-source
platform including linux, Java and MySQL. The
visualisation was developed with Apache, PHP and
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Figure 1: Abstract System Architecture.
the PHP GDGraph library, which are connected to
the MySQL database. By using the open-source
platform we have promoted the potential for
distributing the solution to multiple servers, each of
which would be responsible for managing a part of a
building, which would likely be prohibitively
expensive if commercial technologies were used.
The current version of the database schema is
presented in figure 2. The sensor table includes the
following fields: id (The unique id key for the
sensor), current location (the unique id of the
sensor’s current space), sensor type (e.g. fire
detector/ temperature sensor) and start and end
fields, which record the time of the sensor’s
installation and de-commissioning.
The ‘sensor_temp_hist’ table records the state
changes of temperature sensors. Every time a sensor
object reports a different temperature, the database is
updated by ending the old state, i.e. updating the
‘end time’ field and creating a new record which
includes the sensor ID, current temperature reading
and the new start time. The current state of a sensor
always has a ‘NULL’ value in the ‘end time’ field.
The ‘curstate_temp’ table contains one tuple per
sensor object and records that sensor’s current state.
This information is also contained by the
‘sensor_temp_hist’ but has been included in the
‘curstate_temp’ table in order to prevent length full
table scans when searching for current object states.
The same pattern has been used with the
‘sensor_state_hist’ and ‘curstate_state’ tables, which
record the state of the sensors e.g.
active/inactive/malfunctioning, as well as the
‘location’ and ‘loc_history’ tables, which store the
changes in the location of sensors.
The prototype’s functionality is provided by the
following classes:
1. DbManager: provides database Application
Programming Interface (API) functionality
2. EventReading: represents a sensor reading event
3. ReadingManager: An object which manages
received EventReading objects. In future, multiple
ReadingManager objects could be useful in
buffering or distributing the application among a
number of application and/or database servers
4. Sensor: Sensor objects represent real-world
sensors which may be of a number of types and
create EventReading objects which represent their
5. SensorManager: An object which manages a
group of sensors.
6. SensorManagerTask: An object which extends
‘TimerTask’ allowing the simulation to trigger a task
every n seconds
7. SensorSim: The main class which starts and
controls the simulation
The visualisation was developed using
GDGraph, an open source PHP graph library. The
script reads the temporal database records and draws
a temperature graph as a PNG image (see figure 3).
Figure 2: Abstract Database Schema.
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
The following example demonstrates the
applicability of combining temporal databases and
the IFC file content to the dynamic building model;
assuming that the model were to be continuously
updated by accurate sensor data. For this example,
we will use an imaginary office building termed
Building A, which has a dynamic building model
based on a temporal database and semantic
information from the building’s ‘as-built’ IFC file.
Building A is occupied during most of every day
by Bob who is monitored as he enters, moves around
and leaves the building. Bob’s movement between
spaces (possibly rooms or desks in terms of an open-
plan area) as well as the spaces’ temperatures are
recorded in the temporal database and the
relationships between temporal entities, a uniquely
identifiable item object or object state, is maintained
e.g. during a specified period of time Bob is linked
to the record of the space which he occupied during
that period, and, temperature readings are linked to
each space.
Given sufficient time, the system will build up
enough records to support the mining of data in
order to recognise Bob’s typical pattern of
behaviour. For example, the time he normally enters
or leaves the building could be calculated by taking
the average entry and exit times. Likewise, Bob’s
main space (his desk or office) could be
automatically inferred based on the space in which
he spends the highest percentage of his time. The
typical temperature of his most-occupied space
could also be calculated, and assuming that there are
no recorded instances of Bob changing the
temperature settings, we can deduce that the
recorded temperatures are within the range at which
Bob feels comfortable. These simple measurements
can be semantically linked to automatically provide
the system with accurate information relating to
individual occupant behaviour and preferences. This
information could then be used to automatically
configure spaces to meet occupant comfort settings,
in this case: temperature. This principle could,
however, be applied to other factors such as: lighting
and HVAC. Such knowledge could then be used to
plan building ecological strategies, seating plans,
etc., to ensure efficient use of building space.
Other recognised patterns can be used to directly
affect the building’s response to the occupant in
order to provide personalised settings to ensure their
comfort or to ‘intelligently’ respond to the user’s
behaviour in order to save resources (both financial
and energy). For example, if the occupant is
recognised as frequently leaving and returning to his
main space, it would be inefficient for the system to
continuously switch his space’s lighting on and off.
Likewise if the person leaves the building around
their expected leaving time, the system can
automatically switch off the lighting and electronic
appliances in their space in order to reduce energy
wastage. Crucially, by continuously re-examining
these patterns in the context of recent data, the
system can recognise when patterns change and
respond accordingly. For example, if Bob moves to
a different office, even if uninformed the system will
recognise this based on his recent behaviour. Any
automated system actions relating to Bob, such as
the setting of his heating or lighting preferences, will
then take place in the new space.
The recording of users in space and the
recognition of patterns in this data could also be very
beneficial for facilities managers (FM). The role of
FM staff includes space analysis, space usage
optimisation, support for the cleaning and
maintenance of facilities, which is often reliant on
accurate information relating to the number of
occupants in the building, the changes in such trends
and the availability of this information in a timely
manner. Currently, many FM managers struggle to
keep up with these duties due to the varying number
of occupants as a result of the increasing adoption of
flexible working (Lindkvist, 2009). This has lead to
unreliable predictions of occupant numbers. The
system could support these requirements to some
degree, but may struggle with highly dynamic
environments as historical data may be insufficient
to predict occupant behaviour.
Table 1: Sample Data.
Table 1 displays a sample of test data that was
automatically generated by the prototype. The sensor
objects have been set to automatically generate a
random new temperature reading every 3 seconds.
The start and end time fields delimit the various
temperature states. This arrangement can be applied
to all other temporal relations including location,
state (e.g. active/inactive etc) as well as relationships
between objects. The visualisation of recorded data
(see figure 3) is important as it supports human
operators examining changes that take place in the
Start Time End
1 18 17:09:52 17:09:54
2 16 17:09:52 17:09:54
3 18 17:09:52 17:09:54
4 17 17:09:52 17:09:54
1 16 17:09:55 17:09:57
2 15 17:09:55 17:09:57
3 19 17:09:55 17:09:57
4 21 17:09:55 17:09:57
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building and for identifying trends. Table 2: Sample
The main purpose of the prototype was to
demonstrate the applicability of temporal databases
to the built environment and intelligent spaces for
the purpose of supporting IB adaptability. We also
wanted to explore the issues and problems related to
their use in building management and learning.
The temporal database was designed in a manner
that supports frequent access to both historical
records and the current states of objects, as based on
temporal database literature (Gregerson, 1999), with
an extra table holding only the current states to
prevent lengthy full-table-scan database queries
when selecting current states.
The database structure includes the concept of
‘spaces’ which can easily be populated from IFC
files. By establishing a temporal relationship
between spaces and their contained sensors, it is
possible for sensors to be moved between spaces and
for their subsequent readings to be linked with
spaces at the time the readings were taken. This
assumes of course that the location of the sensors is
updated accurately and in a timely fashion. This
prototype functionality, to our knowledge, is
currently unavailable within IB software.
The richness of the building model relies heavily
on accurate input from a wide range of sensors and
sensor types. This has several implications,
including the cost of sensors and related technology,
data security and occupant trust. We believe that the
potential benefits of increased energy efficiency and
provision of personalised space settings will over
time outweigh the disadvantages related to these
Since the demonstrated prototype is still in its
infancy, we plan to extend it in several ways. We
need to extend the database schema to include more
temporal entities, develop test queries that are time-
bound e.g. “In which space was Bob at 11am
yesterday?” as well as develop IFC import
functionality. We are considering the development
of a more abstract API, since this would be useful
when linked to building simulation software (e.g.
fire or lighting). Such an API would support the use
of a dynamic building model for running building
simulations, which has been commented as a
deficiency when using IFC files for such simulations
(Spearpoint, 2007). In addition we also plan to link
this work with a multi-agent system, which will
support various intelligent agents for performing a
multitude of tasks, including data mining.
Other proposed areas of application may also
include development and testing of the system to
support a distributed data structure. This may
involve distributing the model among multiple
servers, for parts of a building, and required research
concerning communication relating to building state
changes. Each server might also be responsible for
mining it’s own set of recorded data for patterns.
The currently proposed prototype is currently
only partly dynamic, as it does not support changes
to the building’s physical structure. This structure is
fixed based on the content of the imported IFC file.
In the future, we plan to allow for the import of
updated IFC files which will allow the dynamic
building model to remain up-to-date with changes in
the building’s structure.
Figure 3: Temperature Graph (X: Time Y: Temperature).
This paper has reviewed a number of state of the art
technologies and tools in intelligent buildings in
relation to their current inability to support dynamic
and historical building models. Such technologies
provide a starting point, and we have shown can be
modified and extended to support the required
functionality. Finally we proposed a novel prototype
based on IFC and a temporal database, and
demonstrated the feasibility of this approach.
The prototype fulfils a number of roles in our
research. These roles include: the development of an
ideal temporal database structure for storing building
information, a test-bed for the creation, recording,
management and mining of temporal data as well as
the model’s software objects. The prototype will aid
us in discovering potential issues and problems
related to the management of temporal building data
and will support us in providing recommendations
for a dynamic building model standard.
We hope that our development of this model will
support future intelligent buildings by allowing them
ICISO 2010 - International Conference on Informatics and Semiotics in Organisations
to dynamically monitor and respond to
environmental changes and thus meet the
requirement of adaptability.
We wish to thank CDC (Central Data Control) and
the EPSRC (Engineering and Physical Sciences
Research Council) for supporting and funding our
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