NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

SmartOp - Smart Buildings Operation
Andrea Costa,
NUI Galway, Civil Engineering Department, Informatics Research Unit for Sustainable
Engineering (IRUSE)
andrea.costa@nuigalway.ie
Abstract
SmartOp focuses on optimal building and HVAC
systems control underpinned by reduced order models.
The focus of the project is on sport facilities which
denote a higly variable usage profiles.
1. Introduction
Current building operation strategies do not account
for the dynamic behaviour of building usage. An
integrated ICT base methodology is required to support
optimal building and systems operation and control.
SmartOp will define an ICT based methodology that is
capable of supporting optimal decision making in
relation to building and system operation. With a vast
array of information not previously available in terms
of building energy performance, SmartOp will develop
the proposed methodology underpinned by novel
mathematical models that will use this data to make
optimal energy management decisions with particular
consideration to the triple dimensions of energy flows
(generation, grid exchange and consumption).
1. Project outline
A large body of research regarding building
operation is focused on the acquisition of higher
resolution data leveraged through the enhancement of
sub metering and smart metering technologies.
SmartOp is focused on the development of
mathematical energy optimisation models that are
capable of capturing and predicting the dynamic
behaviour of building operation. These mathematical
models will be developed and enhanced using high
resolution data sets that can be obtained from both
whole building energy simulation models and smart
metering technologies. These models can provide quick
response in terms of performance prediction that is the
key requirement for dynamic building and system
optimal operation. Artificial Intelligence (AI) concepts
and techniques that include fuzzy logic and neural
networks, provide significant potential in the
development of the mathematical models proposed in
this research.
A case study building will be used to demonstrate
the main research challenges associated with dynamic
building usage with load optimisation, power pricing
and occupancy scheduling, etc… The case study
building chosen for this purpose will be a sport facility.
Sport facilities possess unique features such as:
variable energy demand profiles (timing and peaks)
and usage patterns (long periods of low use and then
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short periods of high use sporting event);
complex environmental conditions (comfort and
ventilation requirements), facility functional
characteristics (e.g. swimming pools, indoor courts,
saunas, and the like) and open spaces (multiple
buildings, complexes, parking areas, lighting, etc.).
The innovative SmartOp methodology is structured
in three layers (Figure 1), the decision making layer,
that represents the main contribution of this work, and
other two supporting layers: the sensing layer and the
controlling layer that will be examined within the
project. The sensing layer objective is to focus on the
required smart metering network to support the
proposed methodology. The controlling layer objective
is to focus on the systems control side (actuation) in
order to implement the decision driven by the sensed
parameters and the energy optimisation models that are
the core of this research work (decision making layer).
As shown in Figure 1, the decision making layer will
take inputs form the facility manager in form of
optimisation scenarios. The facility manager specifies
the optimisation scenarios that he wishes to achieve
such as minimise zonal energy consumption while
maintaining environmental temperature set points, or
maximise the energy sold to the grid. These
optimisation scenarios then act as inputs to the decision
making layer whereby the mathematical models within
this layer perform the optimisation routines.
Sensing layer
Decision making layer
(mathematical
models)
Controlling layer
Optimisation
Scenarios
Figure 1 – SmartOp methodology overview
2. Acknowledgements
This project is funded by IRCSET and D'Appolonia
within the Enterprise Partnership Scheme.