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00:00:01
i e. i. e. e. e. e. for or ah uh any sorry e.
00:00:14
uh_huh uh_huh
00:00:19
oh yeah but in this case in this room the the results aren't
00:00:24
soul that the are very see very similar um zero two concentration so
00:00:31
uh that's size the uh from the the floor at the ceiling yeah
00:00:40
i know that we we put the um
00:00:44
we tested with the people inside and then we take off the people for example
00:00:50
and to got to to know the big a for example and the
00:00:54
all the results and the the the all the sensors are very similar
00:01:00
the the changes are not um significant it between sensors in the floor
00:01:06
in the ceiling and also in the hall for the the the room
00:01:13
of course uh the companies want to to make that want to
00:01:17
to put to the this answers not in the floor of course because
00:01:20
we have we have seats you have bad that uh uh can influence
00:01:25
these results so this is just that i passed but yeah we we we we know
00:01:31
uh for for the results that is not so um so much
00:01:35
variable this you two in the in these tests uh for now yeah
00:01:44
yes
00:01:48
ooh
00:01:50
yeah that for now is not uh uh this
00:01:55
is the premier that amy lee mean our tests
00:01:58
we this year to a question models but after that yeah we we
00:02:02
we we you used to compile the all the data from the sensors
00:02:07
uh and this is the the the next steps of course uh are not uh uh uh
00:02:14
uh made now but uh we pretend yeah you use the uh machine learning techniques
00:02:20
uh to to compile and the combine does
00:02:24
the all these data uh and and and um
00:02:28
of course bake the analysis to eve feedbacks for a
00:02:33
short term or long term actions for the users yeah
00:02:42
i
00:02:46
yeah okay but you should uh i. e. e. e. o. e. i. e. s. a. i would say yeah
00:03:06
as c. e. s. a. m. e.
00:03:16
e. e. or uh_huh uh_huh so uh the first quick question um
00:03:26
yeah uh uh we pretended to the u. i. and the c. o. two is like the the indicator
00:03:32
of the quality of care so we pretended to to to advise
00:03:38
with the with our studies and with the uh with the simulation um
00:03:43
to provide the user the the uh that warnings that i show you and
00:03:50
the four then we need of course uh at the the kind of the um
00:03:57
the um actions to do uh when the the c. o. two are
00:04:02
about about leaving my limit for example is actions like you open the windows
00:04:07
open the the title window the fully window
00:04:11
and the uh or uh in case of a
00:04:14
um ventilation systems you can increase uh a little
00:04:18
bit or more depend on the the warning of course
00:04:22
uh the the etchings rate uh all the all the room using the ventilation system
00:04:28
and so of course uh and we have this problem if
00:04:33
you uh open the window for them can do in the winter you open the window
00:04:37
but uh you know that if you open the we know you can gain the fresh air
00:04:43
but also you will increase the energy consumption of
00:04:47
building because the the building a could be the
00:04:52
the cold after some time the the opening windows so the opposite if
00:04:58
he's combine these using the artificial intelligence
00:05:01
machine learning for example um to combine
00:05:07
all the the the the parameters the energy consumption see a indoor air
00:05:12
quality and they also they're more comfort uh to give us of course
00:05:18
the time in this case that you need to open the window
00:05:23
without a um a increase the a lot of the energy consumption
00:05:28
and the also of course decrease the c. o. two levels and
00:05:35
okay
00:05:38
uh_huh question ah yeah i think very we can uh huh huh huh huh
00:05:56
oh yeah that's uh
00:06:01
she counts yeah well if i did that for now
00:06:15
yeah um of course with the the the these
00:06:21
uh these buildings need to connect the for the
00:06:25
the actual uh whether uh uh and data
00:06:30
so we can use the actual weather data
00:06:33
to use uh to um to give the
00:06:38
the in the short term actions for the moment
00:06:42
and the uh for the long term actions we pretend to use the
00:06:47
weather forecasting uh for example we can find that in the in several
00:06:53
um companies that provide forecast the weather forecast for the
00:06:58
i don't know six hours one day or more uh um and
00:07:04
be these data we can of course use it for the um to
00:07:08
predicts the the the the the energy conception and
00:07:12
the other parameters in building and a forecast way
00:07:20
i think the last question from supervisors i. e.
00:07:32
well
00:07:42
yeah i'm i'm one of the all cities is a uh
00:07:48
trying to shape of course the user uh be able because it's not easy uh and to um
00:07:55
to know all the user be a over a along on a along all the time so um
00:08:03
yeah do also we we want to the make these like if i uh if the the um
00:08:11
the um users have references like uh the the chamber to
00:08:17
row of uh the the best amplitude for for for them
00:08:21
and we can of course compile the these preferences
00:08:26
and the uh these preferences are the like the first objectives to um
00:08:32
to use in the the framework so we can use the preferences of users
00:08:39
to compile the the and insinuation in also in the uh the short term actions
00:08:45
or a a longterm actions um to comply with the requirements that they they want
00:08:57
oh yeah yeah this is the yeah this is the part on
00:09:03
m. t. v. showing balances yeah that we want to um use
00:09:08
uh and to lighten with the with the scandals with the
00:09:11
the presence in the the athletes of the the users yeah
00:09:20
okay
00:09:29
if
00:09:35
oh oh oh oh i
00:09:46
i i i i oh yeah yeah yeah yeah uh
00:10:03
oh yeah i'll uh we didn't not to to use uh
00:10:10
like a one level of the the the air quality
00:10:14
for example or four oh one level of the demo comfort
00:10:18
so we we want to to leave we we want to to use like uh
00:10:23
different ranges of the air quality different ranges
00:10:27
of the ten more comfort and the um
00:10:32
because these is not uh is uh mm is part to to put
00:10:38
uh it just on like a twenty degrees or twenty five degrees in matching so uh we pretend
00:10:44
also uh put some ranges and the of course
00:10:48
uh connected with the uh different uh the um
00:10:54
the quality quality of the term or comfort or the indoor air quality

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Conference Program

Intelligent feed-back on user comfort in low energy buildings
Rui Oliveira, University of Aveiro, PT
7 May 2019 · 10:09 a.m.
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Urban morphology and building PV energy production
Kin Ho Poon, National University of Singapore, SG
7 May 2019 · 10:30 a.m.
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Kin Ho Poon, National University of Singapore, SG
7 May 2019 · 10:44 a.m.
Urban morphology, energy needs and artificial intelligence
Roberto Boghetti, University of Pisa, IT
7 May 2019 · 10:51 a.m.
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Roberto Boghetti, University of Pisa, IT
7 May 2019 · 11:05 a.m.
Distributed simulation applied to multi-networks urban energy systems design
Pablo Puerto, CREM / Mines d’Albi, F
7 May 2019 · 11:13 a.m.
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Pablo Puerto, CREM / Mines d’Albi, F
7 May 2019 · 11:27 a.m.
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Jérôme Kämpf, Idiap Research Institute
7 May 2019 · 11:32 a.m.
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7 May 2019 · 11:54 a.m.
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7 May 2019 · 1:45 p.m.
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7 May 2019 · 1:46 p.m.
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7 May 2019 · 2:01 p.m.
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7 May 2019 · 2:07 p.m.
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7 May 2019 · 2:23 p.m.
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7 May 2019 · 2:47 p.m.
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7 May 2019 · 2:52 p.m.
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7 May 2019 · 3:10 p.m.
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7 May 2019 · 3:14 p.m.
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Diane von Gunten, CREM
7 May 2019 · 3:28 p.m.
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7 May 2019 · 3:37 p.m.