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so when morning uh everyone uh my name is really via
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uh i'm a p. h. d. student from a portable a diversity of operating a my
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work is is provided by john camp remove the same trickle maybe and and long period
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and uh my work is about to the the
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intelligence feedback on user comfort in low energy buildings
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yeah
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ah ha ha ha ha ah okay
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so we know that the the buildings are the um have a significant uh
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a part or the energy demand and the consequently a d. c. o. two emissions
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that's a a lot of incentives and the directives uh um
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and being that a lot but to motivate the energy building renovation
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so at the incision a tower the implementation
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of smart buildings concept um could be a challenge
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so for these we believe that artificial intelligence uh could be the solution
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regarding the oddities of my disease uh the smart
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buildings have a uh a lot of smart sensors
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and they uh produce a lot of data a lot of information uh about the the the buildings
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so the compiler everything that i can map data mining and
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artificial intelligence uh will be need do you need it and the
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several researches are doing the a lot of studies uh about the energy efficient but
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uh also we need a um studies complying
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energy efficient thermal comfort and indoor air quality um
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and then uh taught demise the these requirements um are something
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dark to to apply so and we stick approach is needed
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where the concerns such as well uh l. c. l. c. buildings
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well being quality of life and in our environment quality quality is needed
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so in the past the research uh work um
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i made a lot of work with the uh uh measuring the buildings a timber to
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room e. d. d. eh i made a lot of air tight pants with a border test
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uh to measure the they appear permeability all the
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buildings and also uh them all and the energy simulation
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uh using in this case uh the energy plus a software um
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and also in finally a building organisation such as the organisational
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thermal insulation windows in systems for cooling and for for eating
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moving on uh to use uh the uh uh uh we stick approach
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we want to develop a machine learning a base it on competitive smart algorithm taking account
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uh the real time input data in the buildings from a lot of sensors
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um that we can coupling that sort
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we can capping the the energy in our environment quite tedious
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weather forecasting and of course uh the monitoring data to
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um produce or um that give us the energy efficient
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efficiency either demo environment and also in the final the shaping of the user be able
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so about that and the uh um focus
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on the quality of a indoor air um
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the the goal the all these framework uh that you try
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to build we'll the we need the feedback to the users
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uh uh in the eye perspective a way of the air
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quality of the air quality range um as you know as
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you can see in the table so in a smart man
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or we want to warning words like advisable should masts um
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for the um for the users so uh
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in that short term like uh these actions you need to
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to um to make to make now or in along the um
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we no longer a ten actions that we we can do a um during the time
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so the
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to get these actions and the disease our pair barely mean our tasks
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uh the trying to construct a the zero to model in the in the the the softer
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so we need the test cells in either the tools for example like a city scene that are we trying
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to to use and to make the simulations of c. e. o.'s english uh not show two in the inside
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in this case we we trying to seem like these for one's own
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for now and then we'll we'll uh produce for a a more zones
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more sources of zero to so this is a typical sell best with
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a volume cheaper for with a two point seven uh meters of the eight
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there is a a there is a um is a traditional massive construction okay
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and we use these sensors
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this is a prototype from the single company and that
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they provide us this year who uh in the inside
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the presence of the people inside also the temperature and immediately so
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a lot of parameters in one and a little uh a smart sensor
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is not sufficient he finished okay but is a
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prototype that we trying to test that's you the um
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but to try to do with trying to the best uh these sensors so
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firstly uh we need to know uh um they have changed the right or
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the the the test to use in our uh in zero to uh equation
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so for that we used the matter that all the big a the the decay metal to uh to know
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the air change rate and we that's the the slope of
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the line equation we can get these uh eh change right
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so
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um
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we made a lot of uh um monotony and the the is the study
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for example uh we can leave the instructions to the companies because they need
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uh where is the place the or the the best position of the sensor to catch the
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presence in a presence in the also the other parameters with that trust will uh uh um
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uh that's all a recording so uh we found that uh yeah we we we
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tested in different places and the in different positions of the in a vertical um
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like like in the close to the roof a close to the the thing the pavement
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uh in the aisle for the the height of the the
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room and also close to the ceiling so and the um
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we it's possible to group concluded that this year to
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consideration is not a influenced by the since a position so
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we can put the sensor where we want far away from the kids
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far away from the pets and the of course we that trust will recording
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as a feature to uh works uh we
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will try to uh um apply the framework
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the bayes on relative in using artificial intelligence into
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case studies one case study in speech one in button
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button um that we present a high quality of construction and then in portugal
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uh i second the a case study that represent a low quality or construction pieces
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our silence so how to get the building smarts smarter
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we can get in to see does that to the scene does that uh i think
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this is i think in a microscopic way uh does work with and to contribute for the
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uh the some uh what we would for a
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smart building with a smart algorithm using artificial intelligence
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giving feedback to the user's supporting novel energy control systems
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and and it finally assuring demo comfort and 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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Rui Oliveira, University of Aveiro, PT
7 May 2019 · 10:19 a.m.
Urban morphology and building PV energy production
Kin Ho Poon, National University of Singapore, SG
7 May 2019 · 10:30 a.m.
Q&A
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 · 1:38 p.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 · 2:27 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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7 May 2019 · 3:28 p.m.
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François Foglia, Directeur adjoint de l'institut de recherche Idiap
7 May 2019 · 3:37 p.m.