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Urban morphology, energy needs and artificial intelligence
Roberto Boghetti, University of Pisa, IT
Tuesday, May 7, 2019 · 10:51 a.m. · 14m 16s
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so good morning meaning is her back to him from my my student at the university of
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these am i would search about to be morphology energy needs an artificial intelligence and in other words
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i want to show you how we can estimate the energy consumption the other group of buildings
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uh using artificial intelligence and how your bumper meters uh can help
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us in the process in the process like for having more precise results
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so imagine for example he wants to estimate the
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energy consumption all that colour drop group of buildings uh
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we we need to use uh some uh no one data some
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no uh energy demanded from uh the other buildings in the city
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so just well why do we want to do this well um the first reason is optimisation so
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we want to have a a models that are more a more precise in order to
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i know the for planners and designers to make a a more conscious decision in
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the project and of course uh in this
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way reducing d. energy waste uh and uh
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together with the energy ways that would like to also we'd use the winners guess emission cars
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as you might know a building sector is the
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may or one of the main your um codes obvious
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yeah i'll just giving the greenhouse gas emissions so the second reason is complexity uh the problem
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is really complex 'cause there are a lot of a complex phenomena in boulder like for example
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he thailand's or a wooden canyons are are to model a and the result so
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i'm a high level of buttons are thinking that is given by the user behaviour
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so basically we don't know how the user will behave inside our building we can build some model to estimate it
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but uh it's an estimation and it uh and those
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two factors user really to a big performance gap between the
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measured and the predicted energy consumption of buildings the third
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reason is the lack of data uh it's more like a lack of details 'cause if we look at the
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problem uh at you been scale uh we usually have
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some g. i. yes they today lack some information so like
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uh the getting ratio of the buildings or a um delay years that the heavenly walls
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and so we need to make some assumption we've physics these model to uh fill those gaps
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and on his obsession that maybe just wrong and increases the the performance gap
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so on this is the typical work flow
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wharf uh on machine learning model so we need
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to have the some data for as an input um the model with
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them prepare these data uh it means that you will clean it ten
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try to find meaningful information trying to extract meaning full information from it
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then the model is selected ah 'cause you we have um the choice between
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a more multiple models and uh depending on the case study
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some models may be better or some models maybe worse but
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we don't know it a priori so we need to figure it out before using the
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actual model that the model the string and wages are an output that will be our
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uh energy demand so we basically uh as you said we have
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a training set the production said they had this same database structure
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uh and of course the only difference is that for
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our training set we know would be upset energy consumption
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uh 'cause the model need to learn from it so on and we can call these
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like the basic requirements so the basic information we need to have a precise a model
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if you miss one of those information uh you re lab are really bad estimation down that's
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um the geometry uh it's the it's a the
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g. i. yes um all the of the uh
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uh all the c. d. where you have a all the points of the footprint of the buildings
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uh the type of buildings that is uh and cody
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doesn't number buddies like for example you can assign a
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um hospital two one um church to to or whatever the
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year of construction the hate and the number of floors of
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course you can give more information if you have it and
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uh hopefully the model will be more precise with more informations
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uh so is as we said we can take this part of the seating
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the previous example as the training set and that part of the provision set
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um so the second step is to prepare the that as we said uh the uh um
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i mean what do we take we extract some information from d. uh
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from our inputs uh we we had some the scale parameters that i'm
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not gonna cover because they are pretty self explanatory so we get the
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perimeter from the put green to the every our biggest volume et cetera
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and then we have some um human scale parameters
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them some morphological parameters at neighbourhood scale needs are
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small area around the building and that you've been scale that is um wider area around the building
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so then it would scale uh it's mostly used to make a is to
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as the meat the uh how much sharper will building get
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in every direction so i will take um i take um
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and now around that this was arrayed use is the right
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from the latitude of this you get from the highest building and
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the city so yeah that's basically used to tackling today and
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maximum length of the shadow they you can have on the building
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yeah in that the billing is you can cast and um
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uh i divide is uh i ran into slices according to some parent number of slices is
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it's not a fix it number but its main any good according to some parameter but i'm not gonna cover it right now
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and um basically with the former we can uh calculated the a shot
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with portion of every uh uh on every face on building in every direction
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uh uh and of course for every slice we take the maximum bad is good we want tunnel
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uh uh the maximum a shot with portion of the face
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but you're been scale and uh uh we take an already these
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three times the previous area uh the radius it's three times a um
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we um we're trying to you you must get parameters there are a bit coverage ratio
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is the proportion uh of these they're ready does all complied by the footprint of buildings
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the aspect ratio is the ratio between the hate all the uh building that we're considering
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and the mean we've all the road so it's the
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stance from other buildings and entered building a it's a it's
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the average building the average rate of the buildings in that area um we
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study those parameters we've the pretty technical editor you know it's a investing tuning
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and uh we're uh we're currently uh making
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a research to estimate near impact on the end
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and on a different models and we're trying to find other meaning full
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with ascii parameters but uh for now and i just implementing this three
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so the next step is to select the model uh i'm going to using a matter the
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school the shop full speed so we basically take the training set we divide this training set tina
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uh are are into some set one for training and one for testing
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and we pass the and and um randomly we
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could these two random this too so subsets randomly
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and we estimate the uh precision of every algorithm on those two subsets
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uh and we do this multiple time in a case like one hundred times
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and we said the and which parameters a score the better uh
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results so i the better performance something which she choose the that parameter
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to uh for our uh estimation
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then these uh let me use the full training set to train the model and the
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we and finally have some outputs may case i i i've bow to examples to
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case studies one is bro a a small town in the canton everybody in switzerland
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um and the other is doing a big city in italy
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so uh it was interesting to see the difference between using a big c. d. and a small town
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uh also because in this small town you have
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of course a lot less training data while uh
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in this case for example we had a um twenty buildings
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for the training and the ten buildings for our estimations d. um
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use also very good but uh are uh
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really dependent in this case on which building we
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choose for training and which meeting views for
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our provision 'cause we have really needed data um
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in the second case because of touring it's a little bit
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different we have um some very good results uh and uh
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the other room lot less dependent from what we choose what
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me would we choose to uh um as a case study
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uh because of course having more data uh help us a lot
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in uh and maintaining these good performances uh as you can see the
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uh and uh you can see the uh arrow or that
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is a performance arrows of ah physics base
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model uh essentially um basics phase uh based software
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uh i'm in those two cases the act option intelligence as being
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a more precise in the single cases away more precise we can say
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and the possible reasons for these uh our uh as you said a
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beginning a lack of the kind of you been a a scale uh
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and they can uh of course we can of meat some
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wrong assumptions one uh considering the the uh basically physically is model
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i'm also the fuzzy matches 'cause made
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a bayes software uh could be uh in
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some cases it depend on the software but usually can to need a using some
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the only time in this case we didn't tune it we didn't uh
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feed the uh we didn't have it we anything else than the input data
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and i'm also the a and the third reason could be um
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uh the fact that the model might not grasp some original difference you so for example in the
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use of behaviour and there are some places where
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user are more conscious on the importance off uh
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keep in for example the windows closed when you are using
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your room and not wasting energy them someplace in might be uh
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in some places people may be less aware of this problem so inmate waste more energy
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um uh finally uh the influence of the context
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uh so the influence of d. u. been uh parameters
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uh when the h. two probably studies we need more data cause
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we have a a lot of four meters sec specially for the child with portion
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so the more like the more um details we add the more
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data we have the better but in my case we had um
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a pretty the persian was improved by a one or a between one and seven percent uh
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especially in the case of doing we bro he was kind off i told me is depending on
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which uh building we were choosing chorus uh of course we had battery uh we had
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jess twenty buildings a wedding today we had
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five hundred buildings so that's a big difference but
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uh creates some problem when you will uh extending eating a lot of features
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it does get more features than a case study them that the point
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um uh improvement uh uh there was a big
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improvement we've uh buildings that and other big error so
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in some cases we had the an improvement of the twenty five to around twenty five
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percent and that's really a lot uh and this could be an interesting uh thing to study
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um and yeah that's it so i'll thank you very much
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