1.
THE CASE FOR OPEN DATASETS FROM
IOT-CONNECTED SCHOOL BUILDINGS
Georgios Mylonas, Dimitrios Amaxilatis
CTI Diophantus, Patras, Greece
BRAINS Workshop, AmI 2019, Rome, November 13th, 2019

2.
Why datasets from educational buildings?
• Educational buildings (i.e., primary/secondary/high schools and
universities, research laboratories, professional training
activities) constitute 17% of the EU non-residential building
stock (in m2)
• The issue of becoming a more sustainable society as a whole
has sparked a general growth of interest towards building
energy efficiency, related datasets and benchmarks
• We argue that producing datasets from public buildings, such as
school buildings, has several benefits and such datasets can be
used in a number of useful ways:
• For research
• In education
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3.
GAIA – Green Awareness In Action
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GREECE
22 SCHOOLS
5500 STUDENTS
SWEDEN
1 TECHNICAL HIGH
SCHOOL
1000 STUDENTS
ITALY
1 TECHNICAL HIGH
SCHOOL
1 UNIVERSITY
1000 STUDENTS
• We base our argumentation on our
own experience from GAIA
• 3 countries: Greece, Italy, Sweden
• February 2016 – May 2019
• 25 school buildings
• It has built a large-scale IoT
deployment with over 1300 sensing
endpoints

4.
Educational approach and rationale
• Use real-world data generated inside schools to:
• Enable more interesting lectures
• Let students discover things on their own
• Learn while doing cool stuff!
• Apart from results inside class, students tend to carry
changes in their behavior on to their home
environment
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5.
• Power consumption
• Temperature
• Relative humidity
• Luminosity
• Motion detection
• Noise levels
• CO2 concentration
• Weather parameters
Monitored in school buildings

6.
IoT devices used
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Power Consumption In classroomEnvironmental Sensor

7.
Benchmarking buildings for energy efficiency and comfort
• Some indicative examples of what we can do (also in real-time)
• Thermal comfort is one of the most important parameters for
inside a public building to make it “better” for its everyday users
• The condition of mind that expresses satisfaction with the
thermal environment and is assessed mainly subjectively, but
can be objectified to some extent
• Main factors that influence thermal comfort are those that
determine heat gain and loss
• We focus on a single metric, the PMV model
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Predicted Mean Vote (PMV)
• A particular combination of air temperature, mean radiant
temperature, relative humidity, air speed, metabolic rate, and
clothing insulation in a seven-point scale from cold (−3) to hot
(+3).
• A PMV equal to zero represents thermal neutrality
• The comfort zone is defined by the combinations of the six
parameters for which the PMV is between−0.5 and +0.5
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• For this building, PMV is
within acceptable limits
• Going over similar data
over greater time
periods can provide
more useful insights
regarding conditions
inside such a building
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10.
Comparative view of the PMV in two buildings
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Generating such data and exporting them as datasets shared with other researchers can
help to identify other approaches, or ideas that can further our understanding and
increase the practical usefulness of IoT installations.

11.
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% of time in which temperature was outside of comfortable levels in
21 school buildings, during winter 2018-19
Can be used by e.g., local government agencies to detect differences between the way
buildings in the same region behave and spot the “weaker” ones, or spot the “best” ones
that should serve as an example of operation for the rest.

12.
Data-driven education for energy awareness
• GAIA: awareness, observation, experimentation and action
• Inform IoT-enabled education activities
• Focus on helping students and teachers to get a better
understanding of the energy-related processes taking place
inside their school building
• How do their actions and routine contribute to their power
consumption
• How do they compare with other similar schools in the same or
other countries
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Open up many possibilities for educators
• Schools and educators can utilize data within in-class activities
according to particularities of their own school environment.
• Very few “officially-approved” educational activities focused on
sustainability.
• Growing awareness among young people.
• A starting point to make students aware of aspects that did not
interest them in practice before.
• Such data series provide the opportunity to study natural
phenomena, e.g., building orientation, presence of students and
teachers across parts of their school, energy consumption.
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14.
In practice
• In GAIA, educators used such possibilities in various ways
• Even educators that do not have a technical background can
find ways to integrate them into their workflow.
• The openness and availability of the data in the GAIA provided
this kind of flexibility.
• Serve two aspects at the same time:
• i) work with data that relate to the students’ everyday surroundings
• ii) work in a sustainability-related context.
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15.
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Lab kits
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Installations

17.
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Summing up
• Open datasets from IoT-enabled school buildings provide plenty
of opportunities to work on multiple “fronts” in parallel
• Input for research-related cases, e.g., energy consumption
patterns, or algorithms for energy disaggregation
• Educational context, providing students with something that
relates to their immediate environment, and which can be used
as input in educational activities
• Future work: provide more substantial and analytic datasets to
the community
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19.
http://gaia-project.eu
https://e2data.eu
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This work has been partially supported by the “Green Awareness In Action” (GAIA) project, funded by the European Commission and the
EASME under H2020 and contract number 696029, and the “European Extreme Performing Big Data Stacks” (E2Data) project, funded by the
European Commission under H2020 and contract number 780245