METHOD FOR DATA ANALYSIS WITH REGARD TO THE QUANTIFICATION OF THE COOLING POTENTIAL OF A REGION OF A GEOGRAPHIC LOCATION

Description

FIELD OF INVENTION

[0001] The present invention relates to a tool to aid in visualization and decision-making for carrying out work in an outdoor geographical location.

[0002] The tool allows visualization of the geographical site and identification of areas within the site where it is recommended to carry out work, the work being aimed at helping to cool the site in case of high temperatures, for example in case of a heat wave.

[0003] The invention also aims to quantify the effects of work carried out in the geographical area of ​​the site, in order to determine the impact of work carried out in the event of high temperatures. STATE OF THE ART

[0004] Urban areas contain numerous infrastructures and have a high population density. These areas possess their own unique ecosystems due to the coexistence of buildings with vegetation or bodies of water. The combination of these parameters leads to the formation of a microclimate in urban areas and contributes to the "urban heat island" effect. This phenomenon is induced by the structure of cities, which have many impermeable surfaces that reduce the presence of water and vegetation, thus limiting evapotranspiration and / or evaporation; and by the slowing of winds by buildings, as well as by air temperature; all of these factors contribute to the creation of local heat zones.

[0005] In the context of global warming and the challenges of managing high temperatures in urban areas, numerous interventions involving urban transformations are being implemented, such as modifying building facades or adding vegetation to cities, to counteract the "urban heat island" effect locally. In particular, these transformations to building facades help cool the air and reduce the temperature in localized heat zones.

[0006] Thus, numerous studies are examining technological solutions to facilitate urban heat management, in order to compare and / or improve the performance of different cooling techniques. For example, the Santamouris document (Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions, Sci Total Environ, 2015 Apr 15; 512-513: 582-598. Doi: 10.1016 / j.scitotenv.2015.01.060. Epub 2015 Jan 31) describes cooling materials, which include reflective, emissive, permeable, and phase change materials (PCMs).

[0007] Similarly, urban greening has been extensively studied and the document Bowler (A systematic review of evidence for the added benefits to health of exposure to natural environments, August 2010 BMC Public Health 10(1):456, DOI:10.1186 / 1471-2458-10-456) describes the benefits of planting trees, developing green facades or roofs, planters and greening the bases of trees or creating parks.

[0008] All the studies described highlight technological solutions for mitigating the negative effects of urban heat. However, these solutions do not allow for the development of an urban cooling strategy for their respective territories.

[0009] Indeed, integrating different solutions is difficult in spatial analyses without resorting to simulations that are generally slow, cumbersome, and complex, exceeding the expertise of operational staff (and the computation time is generally incompatible with the timeframe of real projects). Furthermore, geographic information system (GIS) tools are very common in urban studies, but little research aims to bridge the gap between known physics and this type of tool.

[0010] To assess the impact of these transformations, atmospheric observation systems are developed to gather quantitative information about the altered area. Data is collected to confirm the effectiveness of the transformations and to identify areas for improvement. These observation systems must make assumptions during data acquisition to process the data accurately.

[0011] For example, in the article Yamagata et al. (Heat island mitigation using water responsive pavement sprinkled with reclaimed waste water, 2008, January, Water science and technology: a journal of the International Association on Water Pollution Research 57(5), 763-771), the observation system works with the assumption that two points close to each other in an urban environment have identical climatic conditions, i.e. that the microclimate is uniform in the city for weather stations located close to each other.

[0012] The document Aaron C. Petri, Bev Wilson, Andrew Koeser, Planning the urban forest: Adding microclimate simulation to the planner's toolkit, Land Use Policy, Volume 88, 2019, 104117, ISSN 0264-8377, describes the impact of street trees on a microclimate simulation.

[0013] Mohamed Dardir, Umberto Berardi, Development of microclimate modeling for enhancing neighborhood thermal performance through urban greenery cover, Energy and Buildings, Volume 252, 2021, 111428, ISSN 0378-7788, discusses the impact of green infrastructure, for example canopies and green facades, on the microclimate using a model.

[0014] In other observation systems, the assumption is that the data collected before and after the transformation are independent. In reality, these assumptions are not valid in urban environments, and the results measuring the impact of these transformations are not representative of reality; thus, the quantification of the effects of the transformations is not accurate. DESCRIPTION OF THE INVENTION

[0015] The present invention aims to solve these technical problems by providing those involved in urban works with a tool to visualize the potential of each area of ​​a given geographical site to undergo transformations that would cool said area, in order to prevent urban heat island phenomena and thus provide thermal comfort / cooling to people present in the area in case of high heat.

[0016] To do this, the tool proposed by the invention implements a process which allows each zone of the geographical site concerned to be represented in a certain way, the way of representing the zones being dependent on a coefficient calculated in accordance with the invention and which indicates the refresh potential of elementary cells forming the geographical area concerned.

[0017] In other words, a first objective of the invention is to establish an image of the given site where each given area is represented in such a way as to visualize the cooling potentials that have been evaluated for each cell it contains (the area being divided into elementary cells).

[0018] The invention relates, firstly, to a method for calculating the cooling potential of an elementary grid cell of an outdoor geographical site, said cooling potential being used to quantify the performance of said cell located in an area subject to thermal stress that leads to the formation of a heat island effect during a heat wave. The method according to the invention is remarkable in that it comprises the following steps: A digital elevation model of the site is created, and the site is divided into elementary cells. in that, a first set of information is collected including: latitude information for the site, surface summer sunshine information for the site, said surface summer sunshine information being a value calculated in particular from said latitude information for the site and the number of sunny days for the site over a given period of time, sky view factor information, corresponding to a dimensionless number between 0 and 1 which represents the exposure of a surface to the celestial vault, said sky view factor information being obtained by numerical calculation, in that for each grid cell, the following information is collected: albedo information for the mineral surfaces present in said grid cell, said albedo information corresponding to a coefficient of reflection of solar radiation by materials identified on said mineral surfaces present in the grid cell,obtained by on-site or laboratory measurement a coefficient representing anthropogenic heat emissions, said coefficient identifying the points of anthropogenic heat release in said grid cell, in that a second, or equivalently a second, set of information relating to the presence of vegetation in the grid cell is collected from said digital elevation model produced, said second set of information comprising: a first piece of information (δ low vegetation) of vegetation presence, relating to the presence of a first layer of vegetation extending below a height above a ground level of said grid cell (m 1 , mn ), a second piece of information (δ high vegetation) of vegetation presence, relating to the presence of a second layer of vegetation extending above said ground level of said grid cell (m 1 , mn ), an irrigation piece of information (δ irrigation ),relating to the irrigation of natural areas present on said grid cell (m 1 , mn ), in that a vegetation score (Vegetation Score) is calculated from said second set of information comprising the first and second information on the presence of vegetation and said irrigation information, and in that said cooling potential is calculated for said grid cell with the following formula: , Potentiel de rafra î chissement = 1 − Score v é g é tation + β 1 − α . SVF . I cumul é + Q F I incident I incident being the horizontal incident solar flux density without obstruction and being provided by numerical calculation for the given site, and where β is a numerical value between 0 and 1 allowing to establish the microclimatic equivalence between vegetation and urban materials, the said calculated cooling potential ranging from 0 to 1, 0 characterizing a grid offering maximum cooling performance with ideal bioclimatic conditions while 1 characterizes a grid offering no cooling performance.

[0019] Advantageously, the vegetation score is calculated as follows: Score v é g é tation = 0 , 6 + 0 , 1 . δ EP + 0.2 . δ irrigation ⋅ δ v é g é tation haute ⋅ 1 − δ v é g é tation basse + 0 , 2 + 0 , 1 . δ EP + 0.2 . δ irrigation ⋅ δ v é g é tation basse ⋅ 1 − δ v é g é tation haute + 0 , 8 + 0 , 1 . δ EP + 0.2 . δ irrigation ⋅ δ v é g é tation basse ⋅ δ v é g é tation haute Or : δ low vegetation is information about the presence of vegetation, relating to the presence of a first layer of vegetation that extends below a certain height above the ground of said grid cell; δ tall vegetation is information about the presence of vegetation, relating to the presence of a second layer of vegetation that extends above said height above the ground of said grid cell; δ irrigation is information about irrigation, relating to the irrigation of natural surfaces present on the grid cell; δ low vegetation is equal to 1 when the ground is covered with low vegetation, otherwise 0; δ tall vegetation is equal to 1 when the ground is covered with tall vegetation, otherwise 0; δ EP is equal to 1 when the vegetation of the elementary grid cell (m1, mn) has access to rainwater from roofs or surrounding surfaces in addition to standard rainfall;and δ irrigation being equal to 1 when the vegetation of the elementary grid has continuous access to water during the summer, either by active irrigation, or by a water reservoir system or other integrated design ensuring this continuous access to water for the plants. ;

[0020] Preferably, according to the method of the invention, a third set of information is collected, including: building information, taking into account the number of buildings present on the site area including said grid, as well as the ground area occupied by said number of buildings, and possibly air conditioning information, indicating the presence or absence of air conditioners equipping said number of buildings, and possibly underground infrastructure information, indicating the presence or absence of underground infrastructure if said underground infrastructure is likely to release heat to the surface, said third set of information indicating the thermal power released.

[0021] In addition, said surface summer sunshine information and / or said incident sunshine flux density information corresponds to a cumulative sunshine value from sunrise to sunset over one day, or corresponds to a weighted average sunshine value from sunrise to sunset which is calculated from three sunshine days, preferably said day or days being selected between June 15 and September 15.

[0022] The invention also relates to a method for delivering a visual representation of a geographical site, said visual representation allowing for the visual identification of geographical areas of said site likely to require work, said method for delivering a visual representation being remarkable in that it comprises the following steps: creation of a model of said geographical site, and division of said model into model cells, each model cell corresponding to a representation of one of said cells of said geographical site, for each cell, corresponding to each model cell, the cooling potential is calculated according to the process, the cooling potential calculated for each cell is associated with each model cell, in that each cooling potential or range of cooling potentials is associated with a visual representation characteristic, and in that each model cell is represented with said visual representation characteristic associated with the cooling potential or range of cooling potentials calculated for said cell, so as to obtain a visual representation of the external geographic site with geographic site cells visually identified with the visual representation attributed to the cooling potential calculated for each geographic site cell.

[0023] The invention also relates to a method for quantifying the microclimatic impact of construction work on an area of ​​an outdoor geographical site, said site comprising several geographical areas adjacent to one another, the work being such as to modify the cooling potential of a geographical area of ​​said site. The method for quantifying a microclimatic impact according to the invention is remarkable in that it comprises the following steps: Before carrying out any work on the area of ​​said site, a visual representation of the site is carried out in accordance with the procedure as defined above, in order to visually identify at least one set of grid cells of an area of ​​the site associated with the highest overall cooling potential among said grid cells of said areas of the site, a first weather station for study is positioned in said identified area of ​​the site, a second weather station for study is positioned in a second area of ​​said site, preferably said weather station of said second area of ​​said site being less than 1 km (or less than substantially 1 km) from said first weather station of the identified area, and / or an overall cooling potential of said second area of ​​said site having at most a difference of value of 15% with an overall cooling potential of the area in which said first weather station is positioned, and in that said method comprises the following steps: obtaining a first set of data M study, before for the first weather station and a second set of data M control, before for the second weather station, before the work is carried out, obtaining a third set of data M study, after for the first weather station and a fourth set of data M control, after for the second weather station, after the work is carried out, calculating a fifth set of data ΔM before and a sixth set of data ΔM after from the first, second, third and fourth sets of data by the following formula: ΔM t = M é tude , t − M t é moin , t "t" taking the value "before" intervention or "after" intervention, and, quantification of the microclimatic impact of the work on said area of ​​the external site given by the following formula: I = ΔM apr è s − ΔM avant .

[0024] Preferably, the datasets M study, before, M control, before, M study, after, and M control, after obtained from the first and second weather stations include air temperature, relative humidity, mean radiant temperature, wind speed, and the universal thermal climate index.

[0025] In addition, the datasets M study, before, M control, before, M study, after, and M control, after obtained by the first and second weather stations can be collected at regular intervals over a 24-hour period, and in that, if at least 80% of the wind speed data are less than or equal to 4 m. s-1, or preferably less than or equal to 3 ms-1, then the datasets M study, before, M control, before, M study, after, and M control, after are retained and recorded.

[0026] Advantageously, the datasets M study, before, M control, before, M study, after, and M control, after obtained by the first and second weather stations are collected at regular intervals over a 24-hour period, and if at least 70% of the cloud cover data are less than or equal to 3 Octas, then the datasets M study, before, M control, before, M study, after, and M control, after are retained and recorded.

[0027] Furthermore, the entire dataset can be collected at regular intervals at least every hour, preferably at least every ten minutes, and preferably even more between 1 and 10 minutes.

[0028] Preferably, we smooth the data from the datasets M study, before, M control, before, M study, after, and M control, after collected by performing a moving average of the raw data.

[0029] According to an embodiment which will be presented later, after obtaining the first second, third and fourth datasets, a preprocessing of the data is carried out, said preprocessing consisting of removing erroneous data and adding additional data to the dataset obtained if missing data is found.

[0030] Advantageously, the method includes a final data verification step, whereby a linear fixed-effects model with an indicator δ is performed after the post-intervention state, said final step comprising the following calculation: ΔM t = ΔM avant + I . δ apr è s , if t=after then δ after =1, if t=before then δ after =0.

[0031] The final data verification step may consist of performing a linear mixed-effects model, the model combining: said linear fixed-effect model, which models the microclimatic impact of the intervention and random effects which model external parameters (P).

[0032] Finally, the external parameters P are isolated, the final step including the following calculation: ΔM t = ΔM avant + ∑ i P i , avant + I + ∑ i P i , apr è s δ apr è s

[0033] "i" being a variable equal to the number of external parameters. DESCRIPTION OF THE FIGURES

[0034] Other objectives, features, and benefits will be detailed in the following description with reference to the drawings provided for illustrative purposes only and not as a limitation, including: There figure 1 is a figure that represents a 2D model of an urban site with young trees, based on the cooling potential for each elementary cell; The figure 2 is a figure that represents a 2D model of the urban site shown in figure 1with large trees based on the cooling potential for each elementary cell; The figure 3 is a figure that represents a 2D model of the urban site shown in figure 1 with young trees and sunshade structures based on the cooling potential of each elementary grid cell; The figure 4 is yet another figure that represents a 2D model of the urban site shown in figure 1 with large trees and sunshade structures depending on the cooling potential for each elementary grid cell; The figure 5 is a schematic perspective representation of a site, before work begins, in which a geographical area is identified by dotted lines. figure 6 is a schematic perspective representation of the site shown in figure 5 , after having carried out work in the geographical area marked with dotted lines, The figure 7is a functional diagram illustrating the steps of a method according to the invention, for quantifying a microclimatic impact of work carried out on an area of ​​an outdoor geographical site, and The figure 8 illustrates schematically, in more detail, the sub-steps of a step in the method (step schematized by rectangle VIII) marked by dotted lines on the figure 7 . DETAILED DESCRIPTION OF ONE METHOD OF IMPLEMENTING THE INVENTION

[0035] As stated above, one of the objectives of the invention is to provide urban designers with a tool that allows them to visually identify areas of a site that do or do not require urban rejuvenation work.

[0036] Another objective of the invention is to enable the quantification (or ascertainment with numerical data) of the effects of work carried out in certain areas of the site, so as to determine, objectively, whether the work carried out has had an impact, or not, in the area in which it was done.

[0037] In this description, we will first focus on the design of the tool and the method according to the invention which the tool implements, to allow visual identification on an outdoor site of areas requiring work to avoid or reduce urban heat islands.

[0038] Then, in a second step, we will look at the means according to the invention, and the method according to the invention, which allow us to quantify the impacts of the work carried out.

[0039] To create the tool for visually identifying areas requiring work, the invention proposes a method that takes into account indicators specific to each grid cell of each zone of the site concerned (the zones of the site, or the entire site, being divided into elementary grid cells; see in particular the Figures 5 And 6 (where the meshes m1 to mn are schematically illustrated) to evaluate a potential of

[0040] cooling of each mesh for hot weather conditions, for example in the event of a heat wave.

[0041] THE figures 1 to 4 illustrate examples of images that can be obtained with the tool implementing the process according to the invention.

[0042] Note that each image shown in figures 1 to 4 represents the same site S, with Z zones illustrated in shades of grey of varying darkness.

[0043] The intensity of the grey gradient corresponds to the scale shown on each of the figures, presenting six grey intensities, each grey intensity corresponding to a coefficient of 0; 0.1; 0.275; 0.450; 0.625; and 0.8.

[0044] A refresh rate potential of 0.1 is identified by reference A, which is shown in the figures.

[0045] The cooling potential of 0.450 is identified by reference B which is shown in the figures.

[0046] The refresh rate potential of 0.8 is identified by the reference C which is shown in the figures.

[0047] A cooling potential of 0 characterizes a space offering maximum cooling performance, while a coefficient of 1 characterizes a space offering no cooling performance.

[0048] Thus, in the context of the illustrated example, the mn meshes identified in the image by the shade of grey bearing the reference C correspond to a mesh that presents the worst cooling performance while the mn meshes identified in the image by the shade of coarse bearing the reference A have the best cooling performance.

[0049] The meshes in the areas identified by the shade of grey bearing the reference B have an average cooling performance, less good than those identified by the shade of grey of reference A.

[0050] The cooling potential provides an indication of both: on the level of thermal stress offered by the site to a pedestrian, that is to say, it reveals a space where thermal conditions reach values ​​considered uncomfortable, or even dangerous, for humans; on whether an area can be subject to cooling improvements to reduce this potential.

[0051] Reference will now be made to the method according to the invention, which makes it possible to obtain such images: According to a first aspect of the invention, we seek to represent the site S in order to visualize areas (Z) of the site where the cooling potential is highest (i.e. areas where it would be appropriate to carry out work to avoid heat islands in the event of a heat wave).

[0052] The geographical site in question S is considered to be formed by several zones adjacent to each other: each visual representation of a zone on the overall visual representation of the site will correspond to a geographical zone of said geographical site.

[0053] The process thus includes a modeling step for the said geographical site to obtain a kind of map of the site S: for example, a top-down image of the said geographical site can be obtained. The image is then divided into image areas, each image area corresponding to a top-down representation of the said geographical area of ​​the said site.

[0054] It should be understood that the invention is not limited to the delivery of an image: any visual representation would conform to the invention, provided that the visual representation of site S includes areas which are represented with characteristics specific to identifying areas with high cooling potential (i.e. areas with the most meshes whose cooling potential is closer to 100% than to 0%).

[0055] The modeling stage can be carried out by any means known to a person skilled in the art, for example by using a topography (digital model of the terrain, of the site).

[0056] Each image area Z and more generally the entire site S is therefore divided into elementary cells and the refresh potential is calculated for each elementary cell in accordance with the process which will be explained below.

[0057] Each cooling potential, or a range of cooling potential values, is associated with a visual representation characteristic: on the figures 1 to 4 , it is a gradient of colors or of grey, as previously stated.

[0058] It should be understood that the representation associated with each cooling potential or each range of cooling potentials could be different without going out of scope of the invention (color, representation with stripes or dots etc).

[0059] The method thus involves creating a visual representation (for example, an image on a screen, 2D, 3D) where each area of ​​the geographic site associated with zone Z on the visual representation is visually represented according to the refresh rate calculated for each of its cells, so as to obtain said visual representation of the external geographic site with geographic site zones

[0060] visually identified based on the cooling potential of the meshes they contain.

[0061] The cooling potential is obtained as follows: A digital elevation model of the Z zones of site S is created, as represented for example in figure 5 .

[0062] We also create a mesh of the entire site, that is to say that the site is decomposed (or tiled) into elementary cells m 1 , m 2 , m 3 ... mn , forming a network.

[0063] Advantageously, the size of the elementary meshes can be chosen according to the size of the site S and / or the desired spatial resolution.

[0064] Preferably, the size of the elementary meshes is defined in relation to the size of a pedestrian: i.e., a mesh on the order of a meter (0.5 m typically).

[0065] The pattern of the mesh mn can be a geometric shape such that the tiling covers the entire surface of the site S, without overlap between two contiguous elementary patterns mn and m n+1 (see Figures 5 And 6 for example, m1 and m2).

[0066] We then collect an initial set of information including: a latitude information φ of the site S, a cumulative surface summer sunshine information I of the site, said cumulative surface summer sunshine information I being a value calculated in particular from said latitude information φ of the site and the number of sunshine days of the site over a given period of time, said cumulative surface summer sunshine information (I) being calculated in particular from data provided by at least one weather station D1 (or D2) located on the geographical site S.

[0067] More specifically, the cumulative surface summer sunshine information I is calculated from the latitude and morphology of the site, by a weighted average over several days of sunshine.

[0068] Calculating sunshine from a weighted average allows us to have an average sunshine value for the summer or for any period of the year deemed relevant for the analysis of cooling potential.

[0069] Preferably, the cumulative surface summer sunshine information I is based on a weighted average of at least 1 day of sunshine, advantageously at least 3 days, advantageously at least 5 days, advantageously at least 10 days, advantageously up to 100 days.

[0070] Advantageously, the calculation of sunshine duration is based on a weighted average of 11 days, 12 days, 13 days, 14 days, 15 days, 16 days, 17 days, 18 days, 19 days, 20 days, 21 days, 22 days, 23 days, 24 days, 25 days, 26 days, 27 days, 28 days, 29 days, 30 days, 31 days, 32 days, 33 days, 34 days, 35 days, 36 days, 37 days, 38 days, 39 days, 40 days, 41 days, 42 days, 43 days, 44 days, 45 days, 46 days, 47 days, 48 days, 49 days, 50 days, 51 days, 52 days, 53 days, 54 days, 55 days, 56 days, 57 days, 58 days, 59 days, 60 days, 61 days, 62 days, 63 days, 64 days, 65 days, 66 days, 67d, 68d, 69d, 70d, 71d, 72d, 73d, 74d, 75d, 76d, 77d, 78d, 79d, 80d, 81d, 82d, 83d, 84d, 85d, 86d, 87d, 88d, 89 days, 90 days, 91 days, 92 days, 93 days, 94 days, 95 days, 96 days, 97 days, 98 days or 99 days.

[0071] For example, the overall sunshine duration should be representative of a clear (cloudless) day for the period extending from June 15 to September 15.

[0072] By default, a weighted average is calculated from three days, for example, with the weighting indicated in parentheses for each: 17 / 07 (52 days), 17 / 08 (21 days), and 06 / 09 (19 days). This means that over the period from June 15th to September 15th, there were 52 days with the same amount of sunshine as July 17th of this year, 21 days with the same amount of sunshine as August 17th of this year, and 19 days with the same amount of sunshine as September 6th of this year, resulting in the following formula: I cumul é = 52 . I 17 juillet + 21 . I 17 août + 19 . I 6 septembre 92

[0073] Advantageously, the solar irradiance simulation can be adapted according to the period of interest.

[0074] By default, the study is carried out over a whole day, depending on the traffic on the site, advantageously shorter periods of a day can be used.

[0075] For example, a space left vacant in the evening or late afternoon would benefit from an adjustment of the study period to the 6 a.m. to 2 p.m. time slot. This involves calculating the cumulative sunlight, from sunrise until the end of the usage period or until sunset.

[0076] To calculate the cooling potential of a grid cell in zone Z, other information is also collected, and in particular: an albedo information of the mineral surfaces (α) present in said grid, said albedo information (α) corresponding to a coefficient of reflection of solar radiation by materials identified on said mineral surfaces present in the grid, a sky view factor (SVF) information, a coefficient (QF) representing anthropogenic heat emissions, said coefficient (QF) identifying the points of anthropogenic heat release in said grid.

[0077] The term "albedo of mineral surfaces (α)" refers to the solar radiation reflection coefficient of a surface, as measured by applicable standards (such as ASTM E1918-16 or ASTM E903-12). Advantageously, the albedo can be determined based on knowledge of the materials used at the site under study. It allows for the calculation of solar energy absorbed by the sunlit surfaces. If the actual albedo of the intended materials is unknown, a value from the literature can be used, provided that a sensitivity analysis for this parameter is performed.

[0078] For the purposes of the invention, a sensitivity study is a study aimed at quantifying the magnitude of the error that can be induced by incorrect parameterization.

[0079] In other words, how much the results are modified if we change the parameter whose sensitivity we are trying to determine (albedo in this case).

[0080] The term "sky view factor" will be understood as a dimensionless number between 0 and 1 that represents the exposure of a surface to the celestial vault.

[0081] This parameter is important for assessing the suitability of reflective or emissive solutions, that is, the use of surfaces or treatments with higher albedo or emissivity than the initially considered reference surface. Indeed, these solutions only provide benefits if the reflected or emitted radiation reaches the sky, rather than surrounding buildings, for example.

[0082] The term "Coefficient (QF)" refers to a coefficient that qualifies anthropogenic heat emissions (QF): The method according to the invention makes it possible to identify points of anthropogenic heat emission in areas frequented by users, for example, air conditioners. This also includes, where applicable, the presence of underground infrastructure likely to release heat to the surface (for example: district heating networks, ground-level ventilation outlets, etc.).

[0083] Energy efficiency measures are also important, particularly those aimed at limiting the impact of air conditioning. In addition to emitting the equivalent of their electricity consumption as heat, air conditioners also expel the heat they extract from the air-conditioned space. The ratio between heat expelled and electricity consumed is therefore greater than 1 and is generally around 3. The more limited the use of air conditioning, the more these significant emissions are reduced.

[0084] According to the invention, to calculate the cooling potential of zone Z, a second set of information relating to the presence of vegetation in the zone is also collected from said digital elevation model.

[0085] Indeed, the digital elevation model must be built from a digital terrain model (topography) coupled with a building elevation model and

[0086] vegetation height allowing for consideration of bushes and trees casting shade (see in particular the Figures 5 And 6 illustrating schematically the vegetation and its height).

[0087] The second set of information then includes: a first piece of information (δ low vegetation) regarding the presence of vegetation, relating to the presence of a first layer of vegetation extending below a certain height above the ground level of the grid mn (n being the reference of the grid concerned), a second piece of information (δ high vegetation) regarding the presence of vegetation, relating to the presence of a second layer of vegetation extending above said ground level of the grid mn, an irrigation piece of information (δ irrigation), relating to the irrigation of natural surfaces present on the grid mn, and we calculate a vegetation score (Score vegetation ) from said second set of information comprising the first and second information on the presence of vegetation and said irrigation information.

[0088] δ veg is a numerical value equal to 1 in the presence of vegetation; β is a numerical value between 0 and 1 allowing the establishment of the microclimatic equivalence between vegetation and urban materials.

[0089] Advantageously, δveg is equal to the sum of δlow vegetation and δhigh vegetation.7

[0090] Advantageously, β is a factor that equals 0.4.

[0091] The vegetation of non-mineralized surfaces is distinguished between upper (tree) and lower (herbaceous and shrubby) strata. This allows for clarification of whether or not herbaceous and / or tree vegetation layers overlap. The combination of two strata maximizes the cooling effect. A modulation of the vegetation score ( ) allows for consideration of summer irrigation of plants or the contribution of a design that enhances rainwater (RW), for example by supplying vegetated areas.

[0092] Advantageously, the vegetation score( ) follows the following formula: δ low vegetation being equal to 1 when the ground is covered with low vegetation, otherwise 0; δ tall vegetation being equal to 1 when the ground is covered with tall vegetation, otherwise 0; δ EP being equal to 1 when the vegetation of the elementary grid has access to rainwater from roofs or surrounding surfaces in addition to standard rainfall; δ irrigation being equal to 1 when the vegetation of the elementary grid has continuous access to water during the summer, either by active irrigation, or by a water reservoir system or other integrated design to ensure this continuous access to water for the plants.

[0093] Advantageously, δ EP and δ irrigation cannot be equal to 1 at the same time.

[0094] In a second aspect of the invention, the vegetation score ( ) follows the following formula: γ v é g = X v é g + 0 , 1 . δ EP + 0 , 2 . δ irrigation X veg being equal to 0.2 when the soil is covered with low vegetation, equal to 0.6 when the soil is covered with high vegetation, or equal to 0.8 when the soil is covered with both low and high vegetation; δ EP being equal to 0 when the soil has no water input, equal to 1 when the vegetation of the elementary grid has access to rainwater from roofs or surrounding surfaces in addition to standard rainfall, or equal to 2 when the vegetation of the elementary grid has continuous access to water during the summer, either through active irrigation, a water reservoir system, or other integrated design to ensure this continuous access to water for plants.

[0095] Once all this data has been collected, the cooling potential for the area can be calculated using the following formula: Potentiel de rafra î chissement = 1 − Score v é g é tation + β 1 − α . SVF . I cumul é + Q F I incident

[0096] I incident being the horizontal incident solar flux density without obstacles and being provided by numerical calculation or from weather station data.

[0097] Subsequently, "absorbed flux density" will be understood to mean the illumination received by the area of ​​the geographical site concerned.

[0098] The horizontal incident flux density without obstruction means that the horizontal incident flux density is taken into account without the presence of a mask.

[0099] Once calculated, the cooling potential is determined and ranges from 0 to 1.

[0100] 0 characterizing a mesh offering maximum cooling performance with ideal bioclimatic conditions, while 1 characterizing a mesh offering no cooling performance.

[0101] Following the analysis method, a 2D (or even 2.5D or 3D) representation of the site is possible. On this representation, indicators allow for the scaling of cooling potential values, as illustrated in the... figures 1 to 4 .

[0102] Cooling potential can define a thermal stress or a site improvement coefficient.

[0103] Once each grid in each zone has been assigned a cooling potential, it is visually easy to choose an area of ​​the site to carry out work.

[0104] As can be seen, the method according to the invention makes it possible to define a cooling potential based on independent data of parameters whose variation over time is significant and which consequently introduces bias in the analysis of the daily heat of a given site. Advantageously, wind and site temperature do not influence the calculation of the cooling potential.

[0105] The calculation method and parameters used in the formula according to the invention have the advantage of being easily accessible to local authorities, thus facilitating decision-making related to urbanization or the design of urban spaces, particularly with regard to cooling. A decision-making tool implementing the method according to the invention also enables land-use planning to create cooling spaces in countries with high levels of sunshine and / or high temperatures.

[0106] The "cooling potential" indicator characterizes an outdoor space in terms of its potential to alleviate the heat stress it generates for a user during a heat wave. This indicator quantifies a value that is not measured by standard devices.

[0107] The cooling potential is designed for two main uses: Design assistance on the one hand, aimed at designers (architects / urban planners) of outdoor spaces to help them identify the areas of a project with the greatest potential for improvement and to choose the cooling techniques best suited to their project; and decision support on the other hand, aimed at managers of urban outdoor spaces to help them identify the areas most unfavorable to the thermal stress of users or the highest priority for a cooling treatment in their territory.

[0108] The cooling potential is calculated from the general material and morphological characteristics of the site studied. This allows for simplified calculations and greater analytical efficiency, unlike conventional multiphysics tools (CFD, etc.).

[0109] The indicator can be used at the territorial level to carry out a diagnosis of targeted outdoor spaces (for example, public spaces). As such, it allows for the identification of spaces most likely to benefit from urban cooling initiatives.

[0110] Advantageously, the data analyzed to calculate the cooling potential can include additional data: number and surface area of ​​B1 buildings (see Figures 5 And 6 ), height and type of vegetation, air conditioners, underground infrastructure.

[0111] For the purposes of the invention, "intermediate parameters" means any surface accessible to users such as floors, terraces, pavements, roads, roof terraces, pedestrian areas, etc.

[0112] Another aspect of the invention is the ability to quantify the microclimatic impact caused by site modifications (by works).

[0113] THE Figures 5 And 6 illustrate site S before and after works respectively: a parking lot located in zone Z has indeed been transformed into a park with rows of trees.

[0114] The invention makes it possible to quantify the cooling effects obtained following the implementation of these measures in order to improve performance during heat waves. It is a method for evaluating the performance of heat wave adaptation techniques implemented on a site.

[0115] We will now describe a method according to the invention which allows such effects to be quantified.

[0116] Advantageously, a meteorological station D1, capable of quantifying a microclimatic impact (I) of an intervention (e.g. vegetation, reflective materials, creation of shade, presence of water, ...) on the given outdoor site S, of such a nature as to modify the cooling potential of the site, is placed at a chosen location (in the zone Z of the site S) where the difference between a cooling potential calculated before the intervention and a cooling potential calculated after the intervention simulated by the modeling, is greater than or equal to a given threshold.

[0117] Schematically, for each mesh mn of the relevant Z zone of site S, a first cooling potential was calculated according to the method according to the invention, then an intervention was simulated via a processor, and then a second cooling potential was calculated.

[0118] One aspect of the invention is based on the difference between the two cooling potentials. The method was repeated for different interventions in order to establish the most effective intervention(s) for zone Z.

[0119] For example, by comparing the figure 1 with the figure 2 , it is possible to identify the places likely to be effectively influenced by a transformation of the urban heat island.

[0120] Calculating the cooling potential before and after an intervention made it possible to evaluate, by a difference, the most suitable location for an effective intervention providing the best possible cooling.

[0121] The placement of a weather station D1 at this location (zone Z in the example illustrated in Figures 5 And 6 ) allows us to confirm the prediction of the cooling potential calculation that was made.

[0122] On the Figures 5 And 6 We note a first weather station D1 which is placed in zone Z and a second weather station D2 is placed in an area close to zone Z, for the purposes of implementing the method according to the invention.

[0123] Advantageously, weather stations D1 and D2 include a dry-bulb thermometer and hygrometer in an unventilated shelter, a black globe, and a 2D ultrasonic anemometer. Weather stations D1 and D2 can be protected by a metal cage with dimensions such as 2 m high and 1 m in diameter. Additional meteorological sensors can be added and analyzed using the same methodology.

[0124] The weather station D1 or D2 measures data over at least three heights.

[0125] Advantageously at 1.5m, station D1 (also D2) measures temperature, humidity, black globe temperature; at about 5cm deep and close to the ground, station D1 (or also D2) measures temperature and heat flow; at about 4m, station D1 (or also D2) measures wind, temperature, humidity, presence of rain, sunshine.

[0126] In one example of an embodiment of the invention (not illustrated), each station may include a black globe which is located at 1.5m and an anemometer which is located at 4m from the ground.

[0127] According to one embodiment of the invention, the sun exposure of the weather station can be recovered through a pyranometer or by measuring the voltage of a photovoltaic solar panel.

[0128] Advantageously, weather stations D1 and D2 collect data at a frequency chosen between 1 and 60 minutes.

[0129] Advantageously, weather stations D1 and D2 collect data at a frequency of 2 min, 3 min, 4 min, 5 min, 6 min, 7 min, 8 min, 9 min, 10 min, 11 min, 12 min, 13 min, 14 min, 15 min, 16 min, 17 min, 18 min, 19 min, 20 min, 21 min, 22 min, 23 min, 24 min, 25 min, 26 min,

[0130] 27min, 28min, 29min, 30min, 31min, 32min, 33min, 34min, 35min, 36min, 37min, 38min, 39min, 40min, 41min, 42min, 43min, 44min, 45min, 46min, 47min, 48min, 49min, 50min, 51min, 52min, 53min, 54min, 55min, 56min, 57min, 58min, or 59min.

[0131] For the purposes of the invention, a chosen location (i.e., a zone Z) is a space present in the site S, the space being spatially delimited by the field of action of the intervention.

[0132] Advantageously, the given threshold is defined by at least one of the following characteristics: the largest difference between the cooling potential calculated before the intervention and a cooling potential calculated after the intervention simulated by modeling on an elementary mesh; the maximum quartile of all the calculated differences; the mean of all the calculated differences; the variance of all the calculated differences.

[0133] Advantageously, the given threshold is defined as 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49% or 50% of the most significant differences.

[0134] Advantageously, once the location is chosen, the method uses both weather stations D1 and D2: a D2 meteorological control station placed in a reference location, which is not subject to any intervention,

[0135] The reference location is situated less than 1 km from the chosen location, and the grid cell on which station D2 is positioned is chosen such that its cooling potential differs from the cooling potential of the grid cell on which station D1 is positioned by a maximum of 15%. a meteorological station D1 placed in the chosen location, undergoing intervention.

[0136] In summary, the method can be considered to involve the following steps: data acquisition (M study, before and M control, before) from at least two stations before the intervention; data acquisition (M study, after and M control, after) from at least two stations after the intervention; filtering of processed data; data analysis: retrieval of the ΔM t value using the following formula: ΔM t = M é tude , t − M t é moin , t "t" taking the value "before" intervention or "after" intervention, determination of the microclimatic impact (I) of the intervention on the urban site, by the following formula: I = ΔM apr è s − ΔM avant

[0137] And verification of the statistical robustness of the analysis.

[0138] There figure 7 illustrates these steps schematically.

[0139] The elements M study, after, and M control, after, ΔM t and I will be described later.

[0140] For the purposes of the invention, the reference location (where at least one control weather station D2 is positioned) is a spatially delimited place that does not receive any human intervention or transformation, so as to be able to appreciate the changes or modifications made in the chosen location.

[0141] The first station D1 retrieves information from a site undergoing an intervention aimed at modifying the refresh potential of the meshes of zone Z of site S and the second station D2 retrieves information from an area of ​​the site not undergoing any intervention but whose parameters specific to the reference site are similar to the initial parameters specific to zone Z of the site undergoing the intervention.

[0142] Comparing the parameters of the two zones of site S allows us to assess the impact of the intervention (impact of the works).

[0143] It should be understood that the method according to the invention could implement more than two meteorological stations, in which case the following scenarios are possible: case i) a meteorological station D1 placed in the chosen location and at least two control meteorological stations D2; case ii) at least two meteorological stations D1 placed in the chosen location and one control meteorological station D2; case iii) at least two meteorological stations D1 placed in the chosen location and at least two control meteorological stations D2.

[0144] In case i) (i.e., "a weather station D1 placed in the chosen location and at least two control weather stations D1"), the method includes: a substep a1) located before step b) and a substep b1) located before step c), in which the data collected by the at least two control weather stations D1 and D2 are averaged; or a step f) requiring the repetition of steps a) to e) with each control weather station, to obtain N microclimatic impacts corresponding to each control weather station (N corresponding to the number of control weather stations), and a step g) averaging the N microclimatic impacts.

[0145] In case ii) (i.e., "at least 2 weather stations located in the chosen location and 1 control weather station"), the method includes: a substep a1) located before step b) and a substep b1) located before step c), in which the data collected by the at least two meteorological stations placed in the chosen location are averaged; or a step f) requiring the repetition of steps a) to e) with each meteorological station placed in the chosen location, to obtain N1 microclimatic impact corresponding to each meteorological station placed in the chosen location (N1 corresponding to the number of meteorological stations placed in the chosen location), and a step g) averaging the N1 microclimatic impacts.

[0146] In case iii) (i.e., "at least 2 weather stations placed in the chosen location and at least 2 control weather stations"), the method includes: a substep a1) located before step b) and a substep b1) located before step c), in which the data collected by the at least two meteorological stations placed in the chosen location are averaged and the data collected by the at least 2 control meteorological stations are averaged; or a step f) requiring the repetition of steps a) to e) with each control meteorological station and with each meteorological station placed in the chosen location, to obtain N x N1 microclimatic impact corresponding to each combination of meteorological station (N corresponding to the number of control meteorological stations and N1 corresponding to the number of meteorological stations placed in the chosen location), and a step g) averaging the N x N1 microclimatic impacts.

[0147] The average difference between ΔM before and ΔM after directly gives an estimate of the average microclimatic impact I. The impact can be calculated over 24 hours on the one hand, and then on a finer scale, for example at hourly intervals or at the measurement frequency.

[0148] Advantageously, the data obtained M study, t and M control, t represent the universal thermal climate index.

[0149] Advantageously, the data obtained M study, t and M control, t represent the air temperature (T° air ), the relative humidity (H relative ), the mean radiant temperature (T° average radiant) and the wind speed (V wind ).

[0150] In the context of the invention, the mean radiant temperature (TgR) is a meteorological parameter that reflects the radiative balance at the measured point. The mean radiant temperature (TgR) is such that the total incident radiative exchanges (irradiance) measured from all directions at the measurement point are equal to that which would be measured at the center of a sphere whose surface was uniformly at the mean radiant temperature.

[0151] The data obtained M study, t and M control, t are parameters allowing us to evaluate the heat felt by a pedestrian on a site.

[0152] The method may include an intermediate step (pretreatment) on the figure 8 ) which precedes the filtering of step c), the intermediate step being a verification step of the data sets (M 0, study and M 0, control, before and after) aimed at filling any data gaps or measurement errors.

[0153] Indeed, the data is transmitted by sensors, and it can happen that the transmitted data is either erroneous or aberrant (the transmitted data takes on an error value corresponding to an error code) or absent. Without adding dummy data, gaps and formatting errors are corrected, even if it means leaving blank lines. Subsequently, the data is smoothed (a moving average over several minutes).

[0154] A dataset (M1, study and M1, control, before and after) is then obtained after preprocessing and is subjected to filtering.

[0155] Advantageously, step c) retains the data from so-called radiative days; a day is considered radiative when two conditions are met: clear skies and low wind speed, the conditions being defined respectively by: cloud cover less than 3 Octas (condition C1, figure 8: the universal thermal climate index corresponds to the UCTI reference on the figure 7 ), wind speeds below 4m / s (condition C2, figure 8 ).

[0156] The conditions can be modified by artificial intelligence, that is to say by machine learning methods with training on a manually constructed dataset.

[0157] The method retains only data from days meeting both conditions in order to assess the impact solely on days with high heat production potential. This data is identified on the figure 8 by (M 2, study and M 2, control, before and after).

[0158] Advantageously, step c) includes smoothing the collected data (M2, study and M2, control, before and after) in order to obtain the data (M study before and after, and M control, before and after) used in step d).

[0159] Advantageously, step d) analysis by a linear mixed-effects model, the model combining: the fixed effect which models the microclimatic impact of the intervention and random effects which model external parameters.

[0160] The combination of the two effects allows us to get as close as possible to the actual conditions of the site.

[0161] For the purposes of the invention, external parameters are parameters that may vary the measured impact to a lesser extent.

[0162] Advantageously, the external parameters represent the summer period, the sunshine of the urban site, and / or unplanned interventions on the site.

[0163] In the context of the invention, interventions are unplanned when they are not scheduled in the initial work. This may include emergency repairs or any other type of unplanned intervention, whether construction or otherwise, that could impact the microclimate of the site.

[0164] Advantageously, in step d), the data are analyzed at the acquisition frequency of stations D1 and D2 and on the data obtained from the current day.

[0165] For the purposes of the invention, the summer period is defined by the date of data acquisition.

[0166] Advantageously, step d) models a linear fixed-effects model with δ following the post-intervention state indicator, and uses this equation: ΔM t = ΔM avant + I . δ apr è s , if t=after then δ after =1, if t=before then δ after =0.

[0167] Advantageously, step d) allows us to isolate the external parameters P, so step d) then follows this equation: ΔM t = ΔM avant + ∑ i P i , avant + I + ∑ i P i , apr è s δ apr è s

[0168] "i" is a variable equal to the number of external parameters.

[0169] Advantageously, in step d) the statistical robustness of the impact is verified for the measurand under study when the value of the slope I is estimated with a risk of error of less than 5%.

[0170] Advantageously, step d) provides the value of I and an indication of the robustness of its estimation by recovering the slope of the linear regression between the observations ΔM before and ΔM after.

[0171] The statistical robustness of the microclimatic impact is verified for the measurand under study when the value of the slope of the microclimatic impact I is estimated with a risk of error of less than 5%. Robustness on the average 24-hour microclimatic impact is a prerequisite for studying robustness at finer temporal scales, for example, at hourly intervals or measurement frequencies.

[0172] Advantageously, step d) requires a minimum of 10 observations, advantageously a minimum of 30 observations.

[0173] The analysis method makes it possible to obtain the impact of the different data, that is to say the impact on air temperature, the impact on relative humidity, the impact on mean radiant temperature and the impact on wind speed.

[0174] In one possible embodiment, the method includes (see figure 8 ): a preprocessing of the data obtained before / after intervention on the sites (step c), a filtering of the data carried out by an artificial intelligence aimed at selecting the "radiative" days (step c), an analysis of these data by a linear mixed effects model (step d) in which: the fixed effect models the impact of the intervention; the random effects model other parameters; the statistical robustness of the analysis is verified in two steps: on the daily data; on the data recorded at the acquisition frequency of the stations; the cross-referencing of these elements makes it possible to quantify the effects with an estimate of the statistical significance of the results.

[0175] Step c of the filtration process is performed by artificial intelligence. More specifically,

[0176] Prior to the following steps, a data set verification step is possible, aimed at filling any data gaps or measurement errors; in other words, this is a data series cleaning.

[0177] When the measurement frequency is less than 10 minutes, data smoothing over this horizon can be performed.

[0178] A 24-hour observation day is defined from sunrise on day J (for example 6 a.m. in summer) until sunrise on J+1 (5:59 a.m. on J+1).

[0179] Data filtering is carried out as follows: Advantageously, the processed data can be collected on days with so-called radiative conditions, i.e. with clear skies and low wind speeds.

[0180] These conditions ensure that the thermal contrasts and meteorological parameters observed will depend primarily on the immediate environment of each weather station and not on areas located beyond the sites studied.

[0181] Advantageously, the conditions are representative of heat waves and periods of strong urban heat island effects. As explained above, the precise criteria for selecting an observation day depend on the specific characteristics of the regional climate and the precise location of at least two meteorological stations: statistically low wind speed; and cloud cover less than 3 Octas.

[0182] For the purposes of the invention, cloud cover of less than 3 Octas defines a clear sky (condition C1 on the figure 8 ).

[0183] Advantageously, cloud cover should take into account the evolution of the sun's trajectory throughout the year and the specific characteristics of the site.

[0184] The site's specific characteristics may include the presence of solar shading, a factor affecting visibility from the sky, etc.

[0185] A 24-hour observation day can be considered provided that the 3 Octas threshold is respected 80% or 70% of the time.

[0186] For the purposes of the invention, the threshold of 3 Octas respected 80% of the time over a 24-hour observation day is defined as an ideal threshold; and the threshold of 3 Octas respected 70% of the time over a 24-hour observation day is defined as a degraded threshold.

[0187] In a possible embodiment of the present invention, if this condition is not met, the series of intraday observations elapsed since sunrise which continuously meets the conditions is preserved.

[0188] Advantageously, the criteria can be defined by machine learning methods with training on a manually constructed dataset.

[0189] Thus, the method makes it possible to establish criteria even though these two criteria are very dependent on the specificities of the site and make it difficult to apply universal thresholds.

[0190] Advantageously, the statistically low wind speed criterion (condition C2) is divided into two speed thresholds. These two speed thresholds can be defined as ideal or degraded, corresponding to the 1st and 2nd deciles of observed speeds over the study period, i.e., the lowest 10% and 20%.

[0191] In one embodiment of the invention, if 80% of the wind speeds observed over 24 hours are below the thresholds thus defined, the day is retained for this criterion.

[0192] Advantageously, cloud cover assessment is carried out manually based on hourly observations of cloud cover conditions above the site. Observation is performed with a direct line of sight within 10 km of the site; failing that, it is assessed based on measurements of short-wavelength and long-wavelength radiation, respectively 0.3-3 µm and 3-100 µm, using a pyranometer and a pyrgeometer, representative of the study site, or a measurement of net radiation.

Claims

1. A method for calculating a cooling potential of an elementary mesh (m1, mn) of an external geographical site (S), said cooling potential aiming to quantify a performance of said mesh (m1, mn) located in a zone (Z) subjected to a thermal stress source of formation of a heat island in the event of a heat wave, said method being characterised in that it comprises the following steps of: - making a numerical elevation model of the site (S), - dividing the site into elementary meshes (m1, mn), in that a first set of information is collected comprising: - information on the latitude (φ) of the site (S), - surface summer sunlight information (Icumulative) of the site (S), said surface summer sunlight information (Icumulative) being a value calculated in particular from said latitude information of the site (S) and the number of days of sunlight of the site (S) over a given period of time, - sky view factor (SVF) information, corresponding to a dimensionless number between 0 and 1 that represents the exposure of a surface to the celestial vault, said sky view factor (SVF) information being obtained by numerical calculation, in that for each mesh (m1, mn) the following is collected: - albedo information of the mineral surfaces (α) present in said mesh (m1, mn), said albedo information (α) corresponding to a reflection coefficient of the solar radiation by materials identified on said mineral surfaces present in the mesh (m1, mn), obtained by measurement on site or in a laboratory, - a coefficient (QF) representing anthropic heat emissions, said coefficient (QF) identifying the anthropic heat release points in said mesh (m1, mn), in that a second set of information relating to the presence of vegetation in the mesh (m1, mn) is collected, from said numerical elevation model made, said second set of information comprising: - first information (δlow vegetation) of the presence of vegetation, relating to the presence of a first layer of vegetation that extends below a height above a soil of said mesh (m1, mn), - second information (δhigh vegetation) of the presence of vegetation, relating to the presence of a second layer of vegetation that extends above said height of the soil of said mesh (m1, mn), - irrigation information (δirrigation), relating to the irrigation of the natural surfaces present on said mesh (m1, mn), in that a vegetation score (Scorevegetation) is calculated from said second set of information comprising the first and second information of the presence of vegetation and said irrigation information, and in that said cooling potential for said mesh is calculated with the following formula: Cooling potential = 1 − Score vegetation + β 1 − α . SVF . I cumulative + Q F I incident I incident being the horizontal incident sunlight flux density without obstacle and being provided by numerical calculation for the given site, and where β is a numerical value between 0 and 1 allowing establishing the microclimatic equivalence between vegetation and urban materials said calculated cooling potential ranging from 0 to 1, 0 characterising a mesh (m1, mn) offering maximum cooling performance with ideal bioclimatic conditions whereas 1 characterises a mesh (m1, mn) offering no cooling performance.

2. The method according to claim 1, wherein said vegetation score (Scorevegetation) is calculated as follows: Where: δlow vegetation is the information of the presence of vegetation, relating to the presence of a first layer of vegetation that extends below a height above a soil of said mesh (m1, mn), δhigh vegetation is the information of the presence of vegetation, relating to the presence of a second layer of vegetation that extends above said height above the soil of said mesh (m1, mn), δirrigation is the irrigation information, relating to the irrigation of the natural surfaces present on the mesh (m1, mn), δlow vegetation being equal to 1 when the soil is covered by low vegetation, otherwise 0; δhigh vegetation being equal to 1 when the soil is covered by high vegetation, otherwise 0; δEP being equal to 1 when the vegetation of the elementary mesh (m1, mn) has access to rainwater from roofs or surrounding surfaces in addition to standard rain; and δirrigation being equal to 1 when the vegetation of the elementary mesh (m1, mn) has continuous access to water during the summer, either by active irrigation, or by a water reservoir system or other integrated design making it possible to ensure this continuous access to water for plants.

3. The method according to claim 1 or 2, wherein a third set of information is collected, among which: - information on buildings (B), taking the number of buildings present in the zone (Z) of the site (S) comprising said mesh (m1, mn), as well as the soil surface occupied by said number of buildings into consideration, and possibly - air conditioner information, indicating the presence or absence of air conditioners equipping said number of buildings, and possibly, - underground infrastructure information, indicating the presence or absence of underground infrastructure if said underground infrastructure is likely to release heat to the surface, and in that said third set of information indicates the thermal power QF released.

4. The method according to claim 1, 2 or 3, wherein said surface summer sunlight information (Icumulative) and / or said incident sunlight flux density information (Iincident), corresponds to a cumulative sunlight value from sunrise to sunset over a day, or corresponds to a weighted average sunlight value from sunrise to sunset that is calculated from three sunlight days, preferably said day(s) being selected between June 15th, and September 15th.

5. A method for delivering a visual representation of a geographical site (S), said visual representation making it possible to visually identify geographical zones (Z) of said site likely to require work, said method for delivering a visual representation being characterised in that it comprises the following steps of: - making a model of said geographical site, and dividing said model into model meshes, each model mesh corresponding to a representation of one of said meshes (m1, mn) of said geographical site (S), - for each mesh (m1, mn), corresponding to each model mesh, calculating the cooling potential in accordance with the method according to any one of the preceding claims, - associating the cooling potential calculated for each mesh (m1, mn) with each model mesh, in that each cooling potential or a range of cooling potentials is associated with a visual representation characteristic (A, B, C), and in that each model mesh is represented with said visual representation characteristic associated with the cooling potential or with a range of cooling potentials calculated for said mesh (m1, mn), so as to obtain a visual representation of the external geographical site with geographical site meshes (m1, mn) visually identified with the visual representation (A, B, C) assigned to the cooling potential calculated for each mesh (m1, mn) of the geographical site (S).

6. A method for quantifying a microclimatic impact of works on a zone (Z) of an external geographical site (S), said site (S) comprising several geographical zones (Z) adjacent to one another, the works possibly modifying a cooling potential of a geographical zone (Z) of said site (S), the method being characterised in that it comprises the following steps of: - before carrying out work on the zone of said site, making a visual representation of the site in accordance with the method according to claim 5, to visually identify at least one set of meshes (m1, mn) of a zone (Z) of the site (S) which are associated with the highest overall cooling potential among said meshes (m1, mn) of said zones of the site, - positioning a first study weather station (D1) in said zone (Z) identified in the site (S), - positioning a second study weather station (D2) in a second zone of said site, preferably said weather station (D2) of said second zone of said site being less than 1 km away from said first weather station (D1) of the zone (Z) identified, and / or an overall cooling potential of said second zone of said site having a maximum value difference of 15% with an overall cooling potential of the zone (Z) in which said first weather station (D1) is positioned, and in that said method comprises the following steps of: - obtaining a first study data set Mstudy, before for the first weather station (D1) and a second data set Mcontrol, before for the second weather station (D2), before carrying out the works, - obtaining a third data set Mstudy, after for the first weather station and a fourth set of data Mcontrol, after for the second weather station, after carrying out the works, - calculating a fifth data set (ΔMbefore) and a sixth data set (ΔMafter) from the first, second, third and fourth data sets by the following formula: ΔM t = M study , t − M control , t "t" taking on the value "before" intervention or "after" intervention, and, - quantifying the microclimatic impact (I) of the works on said zone of the external site given by the following formula: I = ΔM after − ΔM before 7. The method according to claim 6, wherein the data sets Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after, obtained by the first and second weather stations (D1, D), comprise the air temperature, the relative humidity, the mean radiant temperature, the wind speed and the universal thermal climate index.

8. The method according to claim 7, wherein the data sets Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after, obtained by the first and second weather stations are collected at regular intervals, over a period of 24 hours, and in that, if at least 80% of the wind speed data are less than or equal to 4 m.s-1, or preferably less than or equal to 3 m.s-1, then all the data Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after are retained and recorded.

9. The method according to claim 7 or claim 8, wherein the data sets Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after obtained by the first and second weather stations (D1, D2) are collected at regular intervals, over a period of 24 hours, and in that, if at least 70% of the cloudiness data is less than or equal to 3 Octas, then all the data Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after are retained and recorded.

10. The method according to any one of claims 7, 8 or 9, wherein all the data are picked up at regular intervals at least every hour, preferably at least every ten minutes, and still preferably between 1 and 10 minutes.

11. The method according to any one of claims 6 to 10, wherein smoothing of the data sets Mstudy, before, Mcontrol, before, Mstudy, after, and Mcontrol, after collected is carried out by making a moving average of the raw data.

12. The method according to any one of claims 6 to 11, wherein, after obtaining the first, second, third and fourth data sets, pre-processing of the data is carried out, said pre-processing comprising deleting erroneous data and adding additional data to the data set obtained if a missing piece of data is observed.

13. The method according to any one of claims 6 to 11, comprising a final step of verifying the data, according to which a fixed-effect linear model is made with an indicator δafter of the post-intervention state, said final step comprising the following calculation: ΔM t = ΔM before + I . δ after , if t = after then δ after = 1 , if t = before then δ after = 0 .

14. The method according to claim 12, wherein said final step of verifying the data comprises making a mixed-effect linear model, the model combining: - said fixed-effect linear model, which models the microclimatic impact of the intervention and - random effects that model external parameters (P).

15. The method according to claim 14, wherein the external parameters (P) are isolated, the final step comprising the following calculation: ΔM t = ΔM before + Σ i P i , front + I + Σ i P i , after δ after "i" being a variable equal to the numbers of external parameters.