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Harnessing the potential of AI for housing renovation

Pedro Gomes Lopez
Pedro Gomes Lopes
PhD student at Centre de Recherche en Gestion (i3-CRG*) at École Polytechnique (IP Paris)
Key takeaways
  • AI is being used in several areas of renovation: the most promising of which today are advances are in design, to make automated plans.
  • Decision-makers are faced with new variables such as climate resilience, which have no obvious technical solutions.
  • At early stages of the discussion, AI can automatically generate optimised representations, which save time for decision-makers.
  • Artificial neural networks can be modelled by putting them in problematic situations to develop better strategies to counter them: one will model the urban ecosystem, for example, and the other will play the role of the difficulty (cold, heat, flood).

In renovation operations, whether on the scale of a flat, a building or a neighbourhood, both energy and environmental factors are becoming increasingly important. This raises technical questions that are sometimes difficult for decision-makers to grasp. How can the use of artificial intelligence change this?

Arti­fi­cial intel­li­gence (AI) is increas­ing­ly used in ren­o­va­tion projects. As such, it reminds us that ren­o­va­tion is an area of inno­va­tion for which we can dis­tin­guish four sep­a­rate areas. 

The first is to clas­si­fy to pri­ori­tise. AI is inte­grat­ed into large-mesh diag­nos­tic tools. For exam­ple, it will make it pos­si­ble to iden­ti­fy – based on ener­gy bills in rela­tion to their size – the 1,000 or 10,000 pub­lic build­ings out of 500,000, which will have the great­est impact per euro invest­ed in terms of reduc­ing CO2 emis­sions or ener­gy consumption. 

The sec­ond area is the cre­ation of opti­mised ren­o­va­tion plans, upstream of a build­ing site: they make it pos­si­ble to improve the lay­out of a flat, the ren­o­va­tion of a build­ing or the renew­al of an urban area. The third area, at the moment of imple­men­ta­tion, is the cen­sus. Com­put­er visu­al­i­sa­tion tech­niques, which rely on images from var­i­ous sources (scans, drones etc.), make it pos­si­ble to take stock of every­thing on a site, but also to deter­mine the per­cent­age of progress or to iden­ti­fy risks.

The fourth area is every­thing that hap­pens when the build­ing exists: pre­dic­tive main­te­nance and intel­li­gent man­age­ment. Based on var­i­ous sen­sors, AI makes it pos­si­ble to antic­i­pate break­downs or to bet­ter under­stand the pref­er­ences of the build­ing’s users. Some smart build­ing sys­tems go fur­ther by allow­ing inter­ac­tion between users (or inhab­i­tants) of the build­ing with con­ver­sa­tion­al agents, or by mak­ing pre­dic­tions about the needs of res­i­dents and thus gen­er­at­ing pro­pos­als for per­son­alised services.

In terms of ren­o­va­tion, it is cur­rent­ly the sec­ond area, design, that is see­ing the most promis­ing advances. 

With the rise of eco-design, on the one hand, and increasingly restrictive standard frameworks, on the other, this design work has become extremely complex over the last 20 years. What role does AI play in this complexity?

At the build­ing lev­el, tech­ni­cal stan­dards are mas­tered by pro­fes­sion­als and the con­tri­bu­tion of AI is not yet major. How­ev­er, it will allow for ener­gy per­for­mance, envi­ron­men­tal impact, or cli­mate resilience sim­u­la­tions accord­ing to var­i­ous objec­tives and con­straints. This will con­tribute to opti­mis­ing ren­o­va­tion solu­tions from an eco­nom­ic and envi­ron­men­tal point of view. In a chang­ing envi­ron­ment, where ener­gy and mate­r­i­al prices are evolv­ing rapid­ly as they are now, the use of sim­u­la­tion allows us to reopen the field of pos­si­bil­i­ties and to find ways beyond pro­fes­sion­als’ cur­rent habits. More gen­er­al­ly, automa­tion also allows work to be car­ried out more quickly.

On a neigh­bour­hood scale (more rarely on the scale of an entire city), where things become more com­pli­cat­ed, AI plays a more impor­tant role. Both for ques­tions of resources: the indus­tri­al trades involved are prac­tised by pow­er­ful eco­nom­ic play­ers, often at the fore­front of tech­nol­o­gy; and for ques­tions of needs: it is main­ly at this scale that new vari­ables appear, which were not pre­vi­ous­ly inte­grat­ed and have no obvi­ous “tech­ni­cal solu­tions”, trans­posed into practices.

These issues include the envi­ron­men­tal impact of ren­o­va­tion activ­i­ties and the resilience and adap­ta­tion of infra­struc­tures to cli­mate change. They are added to the more tra­di­tion­al ones that have gov­erned deci­sion-mak­ing until now: cost of the work, com­fort, ener­gy con­sump­tion in the eco­nom­ic sense (pur­chas­ing pow­er of house­holds, costs for man­agers), ener­gy con­sump­tion in the eco­log­i­cal sense (emis­sions). A ren­o­va­tion plan is now a very com­plex matter!

How does AI fit in with human processes?

Basi­cal­ly, we are in the busi­ness of deci­sion sup­port. Let’s take an exam­ple. In France and in Europe, many ren­o­va­tion oper­a­tions con­cern neigh­bour­hoods or build­ings belong­ing to the ‘social’ stock, built for the most part in the thir­ty years fol­low­ing the end of the last World War. These are there­fore col­lec­tive deci­sions, involv­ing pub­lic actors and, of course, pub­lic mon­ey. The process­es are cum­ber­some, the deci­sion-mak­ers numer­ous and the stakes high, because the phys­i­cal dete­ri­o­ra­tion of these neigh­bour­hoods goes hand in hand with social and some­times polit­i­cal prob­lems. More­over, they are most often heat loss areas, and the poor qual­i­ty of the build­ings expos­es the inhab­i­tants to ener­gy shocks (increase in price, dis­com­fort). Final­ly, the design of the neigh­bour­hoods and build­ings makes them vul­ner­a­ble to the con­se­quences of cli­mate change, par­tic­u­lar­ly heat waves and flood­ing. There are car parks in these neigh­bour­hoods that are heat pock­ets, and those on the banks of rivers may be sub­ject to a flood risk that was neg­li­gi­ble when they were built.

Ren­o­va­tion is both an urgent and com­plex sub­ject, which rais­es new issues that are not easy for pub­lic deci­sion-mak­ers to master.

To sum up, ren­o­va­tion is both an urgent and com­plex sub­ject, which rais­es new issues that are not easy for pub­lic deci­sion-mak­ers to mas­ter. One of the essen­tial chal­lenges is there­fore to accel­er­ate and opti­mise the col­lec­tive deci­sion. This is pre­cise­ly what AI allows.

How can it do so? By allow­ing, dur­ing the ear­ly stages of the dis­cus­sion, us to auto­mat­i­cal­ly gen­er­ate opti­mised rep­re­sen­ta­tions, plans for exam­ple, which are not “the” solu­tion, but which allow the dis­cus­sion to progress. These rep­re­sen­ta­tions are what is known in man­age­ment sci­ence as “bound­ary objects”: they are suf­fi­cient­ly con­crete to con­sti­tute an inter­face between social worlds and actors with dif­fer­ent perspectives.

In a deci­sion-mak­ing process that lasts a dozen months, if we take a worst-case sce­nario, AI could save one or two months. This is sig­nif­i­cant, with­out being a rad­i­cal upheaval. We can also assume that the qual­i­ty of the deci­sion will be bet­ter: it is made in a way that allows every­one to under­stand the rea­son­ing of oth­ers, and on the basis of rep­re­sen­ta­tions that are eas­i­er to dis­cuss than columns of fig­ures. AI, by mak­ing it easy to gen­er­ate pro­pos­als, is a facil­i­ta­tor and opti­miser of human decision-making.

Can AI “take part” in the discussion?

Not in the sense that a com­put­er would speak at the table… but a dis­cus­sion dimen­sion can be inte­grat­ed into the design work that involves AI. The area that inter­ests us in the Ren­o­vAIte R&D pro­gramme on which I am work­ing is what is called Adver­so­r­i­al Resilience Learn­ing (ARL), a con­cept that enables arti­fi­cial neur­al net­works to be mod­elled and trained by putting them in com­pet­i­tive sit­u­a­tions. We take two vir­tu­al enti­ties, which will lit­er­al­ly throw them­selves against each oth­er. One will play the role of defend­er; it will be a mod­el of the urban com­plex based on BIM (Build­ing Infor­ma­tion Mod­el­ing: dig­i­tal mod­els that do not rep­re­sent geo­met­ric shapes, but objects) and oth­er known ele­ments. The oth­er will play the role of the attack­er: this can be any cri­sis (cold, heat, flood­ing, with risks on com­fort, on heat­ing sys­tems, risks of cracks or impreg­na­tion, bud­getary con­straints, etc.). After sev­er­al hours, this com­pe­ti­tion will allow each of the enti­ties to devel­op the best strate­gies and come up with a set of opti­mised urban renew­al plan pro­pos­als for decision-makers. 

It is already work­ing: this con­cept has been used in Ger­many in the design of smart grids, to iden­ti­fy mal­func­tions on the net­work and gen­er­ate the appro­pri­ate auto­mat­ic responses.

Interview by Richard Robert

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