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Q&A
Rui Oliveira, University of Aveiro, PT
Tuesday, May 7, 2019 · 10:19 a.m. · 11m 04s
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i e. i. e. e. e. e. for or ah uh any sorry e.
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uh_huh uh_huh
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oh yeah but in this case in this room the the results aren't
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soul that the are very see very similar um zero two concentration so
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uh that's size the uh from the the floor at the ceiling yeah
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i know that we we put the um
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we tested with the people inside and then we take off the people for example
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and to got to to know the big a for example and the
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all the results and the the the all the sensors are very similar
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the the changes are not um significant it between sensors in the floor
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in the ceiling and also in the hall for the the the room
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of course uh the companies want to to make that want to
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to put to the this answers not in the floor of course because
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we have we have seats you have bad that uh uh can influence
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these results so this is just that i passed but yeah we we we we know
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uh for for the results that is not so um so much
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variable this you two in the in these tests uh for now yeah
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yes
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ooh
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yeah that for now is not uh uh this
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is the premier that amy lee mean our tests
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we this year to a question models but after that yeah we we
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we we you used to compile the all the data from the sensors
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uh and this is the the the next steps of course uh are not uh uh uh
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uh made now but uh we pretend yeah you use the uh machine learning techniques
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uh to to compile and the combine does
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the all these data uh and and and um
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of course bake the analysis to eve feedbacks for a
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short term or long term actions for the users yeah
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i
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yeah okay but you should uh i. e. e. e. o. e. i. e. s. a. i would say yeah
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as c. e. s. a. m. e.
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e. e. or uh_huh uh_huh so uh the first quick question um
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yeah uh uh we pretended to the u. i. and the c. o. two is like the the indicator
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of the quality of care so we pretended to to to advise
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with the with our studies and with the uh with the simulation um
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to provide the user the the uh that warnings that i show you and
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the four then we need of course uh at the the kind of the um
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the um actions to do uh when the the c. o. two are
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about about leaving my limit for example is actions like you open the windows
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open the the title window the fully window
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and the uh or uh in case of a
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um ventilation systems you can increase uh a little
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bit or more depend on the the warning of course
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uh the the etchings rate uh all the all the room using the ventilation system
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and so of course uh and we have this problem if
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you uh open the window for them can do in the winter you open the window
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but uh you know that if you open the we know you can gain the fresh air
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but also you will increase the energy consumption of
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building because the the building a could be the
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the cold after some time the the opening windows so the opposite if
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he's combine these using the artificial intelligence
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machine learning for example um to combine
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all the the the the parameters the energy consumption see a indoor air
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quality and they also they're more comfort uh to give us of course
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the time in this case that you need to open the window
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without a um a increase the a lot of the energy consumption
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and the also of course decrease the c. o. two levels and
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okay
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uh_huh question ah yeah i think very we can uh huh huh huh huh
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oh yeah that's uh
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she counts yeah well if i did that for now
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yeah um of course with the the the these
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uh these buildings need to connect the for the
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the actual uh whether uh uh and data
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so we can use the actual weather data
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to use uh to um to give the
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the in the short term actions for the moment
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and the uh for the long term actions we pretend to use the
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weather forecasting uh for example we can find that in the in several
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um companies that provide forecast the weather forecast for the
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i don't know six hours one day or more uh um and
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be these data we can of course use it for the um to
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predicts the the the the the energy conception and
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the other parameters in building and a forecast way
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i think the last question from supervisors i. e.
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well
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yeah i'm i'm one of the all cities is a uh
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trying to shape of course the user uh be able because it's not easy uh and to um
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to know all the user be a over a along on a along all the time so um
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yeah do also we we want to the make these like if i uh if the the um
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the um users have references like uh the the chamber to
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row of uh the the best amplitude for for for them
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and we can of course compile the these preferences
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and the uh these preferences are the like the first objectives to um
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to use in the the framework so we can use the preferences of users
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to compile the the and insinuation in also in the uh the short term actions
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or a a longterm actions um to comply with the requirements that they they want
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oh yeah yeah this is the yeah this is the part on
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m. t. v. showing balances yeah that we want to um use
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uh and to lighten with the with the scandals with the
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the presence in the the athletes of the the users yeah
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okay
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if
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oh oh oh oh i
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i i i i oh yeah yeah yeah yeah uh
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oh yeah i'll uh we didn't not to to use uh
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like a one level of the the the air quality
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for example or four oh one level of the demo comfort
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so we we want to to leave we we want to to use like uh
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different ranges of the air quality different ranges
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of the ten more comfort and the um
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because these is not uh is uh mm is part to to put
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uh it just on like a twenty degrees or twenty five degrees in matching so uh we pretend
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also uh put some ranges and the of course
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uh connected with the uh different uh the um
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the quality quality of the term or comfort or the indoor air quality
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