ASHRAE Journal - October 2024 - 34

TECHNICAL FEATURE
but with a wider interpretation: where making the
model refers to the actual coding and implementation,1
engineering the model refers more to understanding the
building and devising a control strategy for it. In many
cases, it is not straightforward to come up with a control
strategy for a building, and control engineers need to
make sense of the building before they can design a
control strategy that fits the considered building.
Diagnosing the Smart Building
To illustrate this case, we will compare the smart
building to a human body. Just as organs make up the
respiratory, digestive and nervous systems in the human
body, the individual equipment forming HVAC, water,
electrical, security and other systems in a building serve
as equivalent components. Diagnosing the human body
or the smart building and engineering a solution with
limited context leads to the following problems:
1. Insufficient self-description. In a similar manner
to when a doctor can better assess a patient's symptoms
if they are given a proper description and patient
background, a smart building and its data only gain
value once it is clear what the data represents, where
sensors are located or what is being measured. A
building's data points are just numeric values within
the virtual environment of a building management
system (BMS); however, they represent a state within
the building that is relevant to understand its operation.
Thus, adequate self-description of the data point is
necessary with features such as value description, max,
min, units and location of the sensor.
2. Lack of structural context. In a similar manner to
how doctors understand that symptoms in one organ
may be a result of a flaw in another organ due to their
integration within a larger system, metadata should
represent the existing systemic relationships that
reflect how the data point is part of wider equipment
or systems within the building. Thus, self-description
alone is insufficient, and relational metadata is
necessary. Such an approach changes the traditional
data-oriented approach where values fluctuate, patterns
are perceived, outliers are noticed, yet, it is not known
what is the source of the issue or the action to be taken.
Data points from equipment are part of a bigger system,
and any analyses on individual equipment and the
system should acknowledge the existing relationship
between them.
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ASHRAE JOURNAL ashrae.org O CTO B ER 2024
3. Isolated diagnosis. If Point 1 and Point 2 in this
list are unavailable, no intelligent decisions can be
made in terms of building actuation and operational
interventions (no diagnosis possible, nor active modelpredictive
control [MPC]). In such cases, ignoring
the self-characterization and relational context of
equipment and systems leads to simple reactive fixes
that can spread further trouble to other parts of the
building due to a superficial solution that overlooks
the root cause of the issue. Ignoring systemic changes
could result in equipment breakdown or malfunction.
Consequently, relational metadata allows a holistic
diagnosis that acknowledges physical relationships
between displayed and analyzed data points.
4. Generalization limitation. Having properly
mapped the metadata of a building is like properly
understanding the anatomy of the human body.
Diagnosing a new patient follows a generalizable pattern
applicable to all human bodies, while taking into
account unique differences for each body. Similarly,
in the building sector, the anatomy of the building has
unique features every time and having its metadata is
the equivalent of having access to its unique anatomy
map. Any new actors that want to provide new services
or intervene in a building system will need to map the
relevant systems to their solution, and there is nothing
more valuable to this process than the building's
metadata. When these new solution providers have
access to the buildings' metadata, it relieves resource
allocation from mapping the equipment and systems
during their initial study phase, resulting in a cheaper
and more diligent service. Furthermore, having shared
metadata maps among different actors will allow them
to build upon each others' solutions with reduced
service costs.
These four points relate more to the engineering phase
of a control strategy. They argue that an appropriate
adoption of metadata schemas to add context to the
smart building data can lead to added business and
client value, less evidently in making and running the
control devices, but rather in engineering the control
strategy for a smart building.
A Meaningful Future for Data-Driven Smart Buildings
When considering the use of metadata (the meta
of data) analyzed step by step and outlined in the
September article, one can see that a flexible and
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ASHRAE Journal - October 2024

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