Method for generating soiling profiles for photovoltaic modules in a photovoltaic installation

Abstract

Method for generating soiling profiles for photovoltaic modules of a photovoltaic installation The present invention relates to a method for generating soiling profiles representing possible temporal evolutions of the soiling rate of photovoltaic modules, the method comprising: a phase of obtaining data representing the temporal evolution of the soiling rate of photovoltaic modules over a time interval, a phase of processing historical data comprising the following steps: the division of the time interval into sub-intervals, called soiling seasons, the division of each soiling season into temporal periods, the determination of values ​​for characteristic variables of each temporal period, for at least one soiling season, a phase of random generation of soiling profiles as a function of the characteristic variables of each temporal period of said soiling season.Figure for the abridged version: 2.

Description

Title of the invention: Method for generating soiling profiles for photovoltaic modules in a photovoltaic installation

[0001] The present invention relates to a method for generating soiling profiles for photovoltaic modules in a photovoltaic installation. The present invention also relates to a computer program product and an associated readable information medium.

[0002] Production forecasting in the field of photovoltaic installations (also called photovoltaic power plants) is essential for optimizing operational efficiency, planning maintenance, and effectively integrating solar energy into the grid. However, the production of a photovoltaic installation is highly dependent on the fouling level of the installation's photovoltaic modules.

[0003] Various physical or mathematical models exist for evaluating production, but to date there is no universally recognized solution. Each operator therefore uses models on a case-by-case basis according to the specific characteristics of their power plant.

[0004] However, these models are at best based on simplified approaches to assess the soiling rates of photovoltaic modules, which means that the estimated soiling profiles are too far removed from reality. This has an impact on the estimated energy production.

[0005] There is therefore a need for a process that allows for more reliable prediction of fouling rates of photovoltaic modules and that is adaptable to any type of photovoltaic installation.

[0006] To this end, the invention relates to a method for generating soiling profiles representing possible temporal evolutions of the soiling rate of photovoltaic modules in a photovoltaic installation, the method being implemented by computer and comprising: - a phase of obtaining historical data for the photovoltaic installation, the historical data representing the temporal evolution of the fouling rate of the photovoltaic modules over a time interval, called the reference interval, the reference time interval being a succession of days extending over several months, - a historical data processing phase comprising the following steps: • the division of the time interval into sub-intervals, called soiling seasons, based on the evolution of the soiling rate over the time interval, each soiling season corresponding to a different category of soiling than the previous soiling season, • the division of each soiling season into time periods based on the evolution of the soiling rate over the soiling season, the time periods being defined from a list of time periods including at least the following time periods: • a fouling period defined as a succession of days with fouling levels increasing from one day to the next by more than a predetermined value, • a stable period defined as a succession of days with fouling levels differing from each other by no more than a predetermined value, • a cleaning period defined as a day on which the soiling rate is lower than the previous day by at least a predetermined value, • for each soiling season, the determination of values ​​for variables characteristic of each time period, - for at least one soiling season, a phase of random generation of soiling profiles based on the characteristic variables of each time period of said soiling season, each soiling profile being representative of a possible temporal evolution of the soiling rate of photovoltaic modules for a future season corresponding to the at least one soiling season considered.

[0007] According to other advantageous aspects of the invention, the method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0008] - the step of dividing the reference time interval into soiling seasons understand : • the calculation of an average soiling rate for each month based on historical data, and • the identification of a new soiling season whenever the average soiling rate of a month differs from that of the previous month by more than a predetermined value, the month belonging to the same soiling season as the previous month otherwise;

[0009] - the step of dividing each soiling season into time periods includes the identification of the start and end points of said time periods;

[0010] - the list of time periods further includes a slightly unstable period defined as a succession of days where fouling rates vary from day to day without this variation exceeding a predetermined value, the last day of this period being identified when the difference in fouling rate between the last day and the first day exceeds this predetermined value;

[0011] - the random generation phase of soiling profiles is carried out on the basis of Monte Carlo simulations;

[0012] - the characteristic variables of each soiling season are chosen at least among the following variables: a daily ratio of soiling, cleaning efficiency, the duration of the time period, and the frequency of the time period over the soiling season;

[0013] - the random soiling profile generation phase includes, for each Depending on the season of soiling, the following steps apply: a. For a given day of the soiling season, the statistical definition of whether or not the given day belongs to a cleaning period based on the frequency values ​​determined for the temporal periods of the soiling season, b. when the day in question falls within a cleaning period: i. the statistical definition of cleaning efficiency for the day in question based on the values ​​determined for cleaning efficiency over the soiling season, ii. the calculation of a soiling rate for the day in question based on the soiling rate of the previous day and the defined cleaning efficiency, iii. Repeating the previous steps, replacing the day in question with the following day. c. when the day in question does not belong to a cleaning period: i. the statistical definition of the time period to which the day in question belongs among the different time periods of a cleaning period, the statistical definition being carried out according to the frequency values ​​determined for the time periods of the soiling season, ii. the statistical definition of a daily soiling ratio and a duration for the time period to which the day in question belongs, based on the values ​​determined for the daily soiling ratio and the duration of said time period, iii. the calculation of a soiling rate for the day in question based on the soiling rate of the previous day and the defined daily soiling ratio, iv. the repetition of the calculation of a soiling rate for the following days by replacing the day in question with the following day as long as the following day belongs to the duration defined for the time period, v. when the following day does not belong to the period defined for the time period, the repetition of the previous steps by replacing the day in question with the last following day;

[0014] - the process includes an update phase comprising the following steps: a. obtaining additional data relating to environmental forecasts, b. the modification of soiling seasons and / or the temporal periods of soiling seasons based on additional data, and c. for each soiling season, the determination of characteristic variables for each time period of the soiling season,

[0015] the update phase being followed, for at least one soiling season, by a phase of random generation of soiling profiles as a function of the characteristic variables of each time period of said soiling season, each soiling profile being representative of a possible temporal evolution of the soiling rate of the photovoltaic modules for a future season corresponding to the at least one soiling season considered;

[0016] - the additional data include at least forecasts meteorological and / or data relating to circumstantial elements affecting the fouling of the photovoltaic modules of the photovoltaic installation;

[0017] - the additional data include at least forecasts meteorological data, during the update phase, the modification step includes: i. Comparing the frequency of rainfall forecasts with the frequency of cleaning periods during soiling seasons, excluding cleaning interventions, and ii. the modification of the soiling seasons and / or the temporal periods of the soiling seasons according to the result of the comparison;

[0018] - historical data are obtained from measurements taken by sensors on the photovoltaic power plant;

[0019] - the random generation phase is implemented for each soiling season, the process includes, for each season of soiling, a selection phase of a soiling profile among possible soiling profiles based on a predefined acceptable risk level;

[0020] - the process includes a phase of predicting the power plant's production photovoltaic based on the soiling profiles selected for each soiling season, and historical production data from the photovoltaic power plant.

[0021] The invention also relates to a computer program product comprising program instructions recorded on a computer-readable medium, for the execution of a generation process as described above when the computer program is executed on a computer.

[0022] The invention also relates to a readable information medium on which a computer program product as described above is stored.

[0023] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0024] [Fig-1] [Fig. 1] is a schematic view of an example of a computer enabling the implementation of a process for generating soiling profiles for photovoltaic modules of a photovoltaic installation,

[0025] [Fig.2] [Fig.2] is a flowchart of an example of the implementation of a process for generating soiling profiles for photovoltaic modules of a photovoltaic installation,

[0026] [Fig.3] [Fig.3] is an example of a curve representing the temporal evolution of the fouling rate of photovoltaic modules over a reference time period (2022-2023), the reference time period having been divided into different soiling seasons, and

[0027] [Fig.4] [Fig.4] is an example of an algorithm for randomly generating soiling profiles for a given soiling season.

[0028] A calculator 10 and a computer program product 12 are illustrated by [Fig.1].

[0029] Calculator 10 is preferably a computer.

[0030] More generally, the calculator 10 is a self-contained electronic calculator to manipulate and / or transform data represented as electronic or physical quantities in computer registers 10 and / or memories into other similar data corresponding to physical data in memories, registers or other types of display, transmission or storage devices.

[0031] The calculator 10 interacts with the computer program product 12.

[0032] As illustrated by [Fig. 1], the calculator 10 comprises a processor 14 including a data processing unit 16, memories 18 and a reader 20 information support. In the example illustrated by [Fig.1], the calculator 10 also includes user interfaces, including a keyboard 22 and a display unit 24.

[0033] The computer program product 12 includes an information carrier 26.

[0034] The information support 26 is a support readable by the computer 10, usually by the data processing unit 16. The readable information support 26 is a medium suitable for storing electronic instructions and capable of being coupled to a bus of a computer system.

[0035] By way of example, the information medium 26 is a floppy disk or flexible disk (from the English name "Floppy disk"), an optical disk, a CD-ROM, a magneto-optical disk, a ROM memory, a RAM memory, an EPROM memory, an EEPROM memory, a magnetic card or an optical card.

[0036] The computer program 12, comprising program instructions, is stored on the information support 26.

[0037] The computer program 12 is loadable on the data processing unit 16 and is adapted to drive the implementation of a process for generating soiling profiles representing possible temporal evolutions of the soiling rate of photovoltaic modules of a photovoltaic installation, when the computer program 12 is implemented on the processing unit 16 of the computer 10.

[0038] The operation of the calculator 10 will now be described with reference to [Fig. 2], which schematically illustrates an example of the implementation of a process for generating soiling profiles representing possible temporal evolutions of the soiling rate of photovoltaic modules in a photovoltaic installation. Reference is also made to Figures 3 and 4.

[0039] In what follows, the fouling rate is defined as a ratio representing the fouling of photovoltaic modules of a photovoltaic installation compared to photovoltaic modules free of fouling.

[0040] The generation process is implemented by the calculator 10 in interaction with the computer program product 12, i.e. is implemented by computer.

[0041] The generation process includes a phase 100 of obtaining historical data for the photovoltaic installation.

[0042] The historical data represent the temporal evolution of the soiling rate of the photovoltaic modules over a reference time interval. The reference time interval is a succession of days spanning several months. Preferably, the reference time interval extends over at least twelve months.

[0043] Preferably, the historical data are obtained from measurements taken by sensors on the photovoltaic power plant. For example, the measurements are current, voltage, or power measurements taken on the photovoltaic installation. These measurements are then processed to obtain fouling rates forming historical data.

[0044] In an example implementation, field expertise determined that, for various metrological reasons, the most relevant parameter to monitor is the current measured by the inverters on the photovoltaic panel side. This current is designated by the acronyms Icc in French or Isc in English. Based on this current and the irradiance and temperature conditions measured in parallel, we calculate the current value equivalent to the standard test conditions (EN 60904-3 standard...), taking into account the manufacturer's datasheet, which specifies, in particular, the coefficient of variation due to temperature. We eliminate measurements where the irradiance is below a predetermined threshold, preferably 500 W / m². We extrapolate to fill in the gaps.The fouling rate (soiling ratio) or PM (performance metric) is the division of this normalized current by the initial current determined by the module manufacturer and indicated on the label on the back of the module (flash test result, final manufacturing step). We will then smooth these ratios by calculating a moving average over a defined range, preferably plus or minus 14 days. We will then eliminate all "abnormal" points from this moving average, that is, those that do not fall within the top 3 values ​​of the distribution.

[0045] Fig. 3 illustrates an example of the evolution of the fouling rate of photovoltaic modules over the year 2022-2023.

[0046] The generation process includes a phase 200 of historical data processing.

[0047] Phase 200 of the treatment includes a step 210 of dividing the time interval into sub-intervals, called soiling seasons, according to the evolution of the fouling rate over the time interval. Each soiling season corresponds to a different category of soiling than the previous soiling season. Thus, the concept of a soiling season here is not related to climate change, but to the change in the type of soiling profile.

[0048] In one example implementation, step 210 of dividing the reference time interval into soiling seasons includes: - the calculation of an average soiling rate for each month based on historical data, and - The identification of a new soiling season whenever the average soiling rate of a month differs from that of the previous month by more than a predetermined value. The predetermined value is, for example, between 5% and 10%. Otherwise, the month belongs to the same soiling season as the previous month.

[0049] In the example illustrated by [Fig.3], we can thus distinguish 4 seasons of soiling: - a season 1 from the beginning of June to the end of August corresponding to a "dirty" season, - a second season from early September to early January, corresponding to a season "muddy", - a third season from early February to the end of May, corresponding to a "clean" season, and - a fourth season from early June to early September, corresponding to a "dirty" season. Season 4 has the same characteristics in terms of dirtiness as season 1.

[0050] Phase 200 of the treatment includes a step 220 of dividing each soiling season into time periods according to the evolution of the fouling rate over the soiling season.

[0051] The time periods are defined from a list of time periods comprising at least the following time periods: - a fouling period defined as a succession of days with fouling levels increasing from one day to the next by more than a predetermined value. The predetermined value is, for example, determined from all usable historical data, namely, for example, the sum of the 3rd quantile of daily variations and 1.5 times the interquartile range. - a stable period defined as a succession of days with fouling levels differing from each other by no more than a predetermined value. The predetermined value is, for example, determined from all usable historical data, namely, for example, the sum of the 3rd quantile of daily variations and 1.5 times the interquartile range. - a cleaning period defined as a day on which the fouling level is lower than the previous day by at least a predetermined value. The predetermined value is, for example, equal to the third quantile plus 1.5 times the interquartile range of the different fouling variations considered throughout the entire usable history.

[0052] Preferably, the list of time periods further includes a slightly unstable period defined as a succession of days in which fouling levels vary from one day to the next without this variation exceeding a predetermined value. The predetermined value is, for example, determined from all usable historical data, namely, for example, the sum of the third quantile of the daily variations and 1.5 times the interquartile range. The last day of this period is identified when the difference in fouling levels between the last day and the first day of this period exceeds this predetermined value.

[0053] In one example of implementation, the step of dividing each soiling season into time periods includes identifying the start and end points of said time periods. This is done according to the characteristics of each time period.

[0054] Phase 200 of the treatment includes a step 230 of determining, for each soiling season, values ​​for variables characteristic of each time period.

[0055] Preferably, the characteristic variables of each soiling season are chosen at least from among the following variables: a daily soiling ratio, a cleaning efficiency, the duration of the time period, and the frequency of the time period over the soiling season.

[0056] The daily soiling ratio is defined as the difference between the ratio of one day and the previous day.

[0057] In one example, the daily soiling ratio is only determined for fouling and slightly unstable periods.

[0058] For example, for slightly unstable periods, the daily soiling ratio is determined from a second-degree polynomial function. More precisely, the coefficients of this equation are determined from the statistical analysis of the identified slightly unstable periods. More precisely, these coefficients are obtained from a correlational study between the daily loss rate and the duration of the corresponding period. This study generates a second-degree polynomial trend curve.

[0059] For example, during fouling periods, the daily soiling ratio is determined. For instance, we will calculate the different possible daily ratios by comparing the ratios of two days at a time. The resulting difference in ratios, divided by the number of days between these two days, constitutes an estimate of the daily ratio. By compiling all the possibilities, we can determine the maximum or minimum daily ratio. For example, if the number of possibilities exceeds 30, these limits will be calculated from the mean plus or minus three times the standard deviation (sigma).

[0060] Cleaning efficiency is defined as a negative daily soiling ratio over the corresponding time period (here the cleaning period, i.e. one day).

[0061] The duration of a time period is the difference between the end and the beginning of the time period.

[0062] The frequency of a time period over the soiling season is defined as the number of times the time period is repeated over the soiling season.

[0063] In the example illustrated by [Fig. 3], season 3 is a distinct season that begins in February and ends in mid-June 2023. This season is characterized by the variables mentioned in the following table: Period Type Daily Ratio (S.Rate) Efficiency (Eff.) Duration Dur Frequency (Freq.) Fouling 0.1 < S.Rate < 0.2 1 < Dur < 1 0 10% Cleaning 20% ​​< Eff. < 60% 1 Day 60% Stable 0 2 <Dur< 5 10% Légèrement instable 0,02<SR<0,05 2 <Dur <15 15%

[0064] Preferably, all seasons of the reference time interval considered are associated with a set of variable values ​​like that in the preceding table.

[0065] The generation process includes a phase 300 of random generation of soiling profiles, for at least one soiling season, based on the characteristic variables of each time period of said soiling season.

[0066] Each soiling profile is representative of a possible temporal evolution of the soiling rate of the photovoltaic modules for a future season corresponding to at least one soiling season considered.

[0067] Phase 300 of random generation allows a large number of soiling profiles to be generated randomly, typically between 100 and 500.

[0068] Preferably, the random generation phase 300 is implemented for all soiling seasons of the reference time interval considered.

[0069] Preferably, phase 300 of random generation of soiling profiles is carried out on the basis of Monte Carlo simulations.

[0070] In an example of implementation, illustrated in [Fig.4], the random generation phase of soiling profiles comprises, for each soiling season considered, the following steps: - Step 310: for a given day J of the soiling season, the statistical definition of whether or not that day J belongs to a cleaning period based on the frequency values ​​determined for the temporal periods of the soiling season, - Step 320: when the day in question falls within a cleaning period: • the statistical definition of a cleaning efficiency Eff for the day J considered as a function of the values ​​determined for cleaning efficiency over the soiling season, • the calculation of a soiling ratio SRj for the day J considered as a function of the soiling rate SRj-1 of the previous day Jl and the defined cleaning efficiency Eff, • repeating the previous steps, replacing day J in question with the following day J+1, - Step 330: when the day in question does not belong to a cleaning period: • the statistical definition of the time period to which day J belongs among the different time periods of a cleaning period, the statistical definition being carried out according to the frequency values ​​determined for the time periods of the soiling season, • the statistical definition of a daily soiling ratio S.Rate and a duration Dur for the time period to which day J belongs, based on the values ​​determined for the daily soiling ratio and the duration of said time period, • the calculation of a soiling rate SRj for the day J considered as a function of the soiling rate SRj-1 of the previous day Jl and the defined daily soiling ratio S.Rate, • the repetition of the calculation of a soiling rate for the following days by replacing the considered day J with the following day J+l as long as the following day J+l belongs to the duration Dur defined for the time period, • when the following day does not belong to the Dur duration defined for the time period, the repetition of the previous steps replacing the considered day J with the last following day.

[0071] It should be noted that in this algorithm, at initialization, day J is the first day of the reference time interval. This algorithm is thus implemented for every day of the reference time interval in order to obtain a soiling rate for each day of the reference time interval. This evolution of the soiling rate thus forms a soiling profile. The algorithm is then implemented a large number of times to obtain all the soiling profiles.

[0072] Optionally, the generation process includes, for each soiling season, a selection phase 400 of a soiling profile from among the possible soiling profiles according to a predefined acceptable risk level. By For example, selection phase 400 aims to select the most pessimistic soiling profile (highest soiling). Alternatively, it selects the most optimistic soiling profile (lowest soiling) or the most frequent soiling profile. Other selection methods are also possible.

[0073] The selection is preferably carried out automatically based on the predefined risk level. Alternatively, the selection is carried out by an operator.

[0074] Optionally, the process includes a prediction phase of the photovoltaic power plant production based on selected soiling profiles for each soiling season, and historical production data from the photovoltaic power plant.

[0075] Optionally, the process includes an update phase 600. The update phase 600 includes a step 610 of obtaining additional data relating to environmental forecasts.

[0076] The update phase 600 is, for example, carried out after the generation step 300 (and possibly also after phases 400 and 500). Alternatively, the update phase 600 is carried out between phases 200 and 300, allowing the additional data to be taken into account from the generation of the first soiling profiles.

[0077] In an example of implementation, the supplementary data include at least weather forecasts and / or data relating to circumstantial elements affecting the fouling of the photovoltaic modules of the photovoltaic installation.

[0078] Weather forecasts are, for example, derived from the analysis of anomalies identified by models such as, for example, that of "Météo Consult".

[0079] Conjunctural events are, for example, events that generate a lot of dust, such as a construction site.

[0080] The update phase 600 includes a step 620 of modifying the soiling seasons and / or the time periods of the soiling seasons according to the additional data.

[0081] In one example implementation, the supplementary data includes at least weather forecasts. Modification step 620 includes: - a comparison of the frequency of rainfall predicted in weather forecasts with the frequency of cleaning periods during the dirt seasons, excluding scheduled cleaning interventions, and - the modification of the soiling seasons and / or the temporal periods of the soiling seasons according to the result of the comparison.

[0082] For example, if heavy and frequent rainfall is forecast during a "dirty season" that is usually dry, the dirt season will be modified in such a way that the algorithm will consider, for this time period, the characteristics of the "clean season" and not those of the "dirty season".

[0083] In one variant, the modification is carried out by an operator based on additional data. In this case, the operator manually modifies the start and / or end points of the soiling seasons and / or time periods.

[0084] The update phase 600 includes, for each soiling season, a step 630 of determining characteristic variables for each time period of the soiling season.

[0085] The update phase 600 is then preferably followed, for at least one soiling season, by a new phase 700 of random generation of soiling profiles based on the characteristic variables of each time period of said soiling season. Each soiling profile is representative of a possible temporal evolution of the soiling rate of the photovoltaic modules for a future season corresponding to the at least one soiling season under consideration. Phase 700 is implemented, for example, in the same way as phase 300.

[0086] Optionally, the process includes post-update selection and / or prediction phases, which are identical to selection phase 500 and prediction phase 600.

[0087] Thus, the present process makes it possible to quantify and predict the fouling rate of the modules of a power plant and consequently its impact on energy production.

[0088] Dividing the historical data on which the predictions are based into soiling seasons and then into time periods allows for a more reliable estimation of module fouling rates. This, in turn, makes it possible to refine the production predictions of the photovoltaic power plant. Furthermore, such a process can be implemented on any type of photovoltaic installation.

[0089] A person skilled in the art will understand that the embodiments and variants described above can be combined to form new embodiments provided that they are technically compatible.

Claims

1. Demands A method for generating soiling profiles representing possible temporal evolutions of the soiling rate of photovoltaic modules in a photovoltaic installation, the method being implemented by computer and comprising: a phase of obtaining historical data for the photovoltaic installation, the historical data representing the temporal evolution of the fouling rate of the photovoltaic modules over a time interval, called the reference time interval, the reference time interval being a succession of days extending over several months, a phase of processing the historical data comprising the following steps: • the division of the time interval into sub-intervals, called soiling seasons, based on the evolution of the fouling rate over the time interval, each soiling season corresponding to a different category of soiling than the previous soiling season, • the division of each soiling season into time periods based on the evolution of the soiling rate over the soiling season, the time periods being defined from a list of time periods including at least the following time periods: • a fouling period defined as a succession of days with fouling levels increasing from one day to the next by more than a predetermined value, • a stable period defined as a succession of days with fouling levels differing from each other by no more than a predetermined value, • a cleaning period defined as a day for which the rate the fouling is lower than the previous day by at least one value

2. predetermined, • for each soiling season, the determination of values ​​for variables characteristic of each time period, - for at least one soiling season, a phase of random generation of soiling profiles as a function of the variables characteristic of each time period of said soiling season, each soiling profile being representative of a possible temporal evolution of the soiling rate of the photovoltaic modules for a future season corresponding to the at least one soiling season considered. Method according to claim 1, wherein the step of dividing the reference time interval into soiling seasons comprises:

3. • calculating an average soiling rate for each month based on historical data, and • identifying a new soiling season whenever the average soiling rate of a month differs from that of the previous month by more than a predetermined value, the month otherwise belonging to the same soiling season as the previous month. A method according to claim 1 or 2, wherein the step of dividing each soiling season into time periods includes identifying the start and end points of said periods.

4. time periods. A method according to any one of claims 1 to 3, wherein the list of time periods further includes a slightly unstable period defined as a succession of days in which fouling rates vary from day to day without this variation exceeding a predetermined value, the last day of this period being identified when the difference in fouling rate between the last day and the first day exceeds this predetermined value.

5. A method according to any one of claims 1 to 4, wherein the random generation phase of soiling profiles is carried out on the basis of Monte Carlo simulations.

6. A method according to any one of claims 1 to 5, wherein the characteristic variables of each soiling season are chosen at least from among the following variables: a daily soiling ratio, a cleaning efficiency, the duration of the time period, and the frequency of the time period over the soiling season.

7. A method according to claim 6, wherein the random generation phase of soiling profiles comprises, for each soiling season considered, the following steps: a. For a given day (J) of the soiling season, the statistical definition of whether or not the given day (J) belongs to a cleaning period based on the frequency values ​​determined for the temporal periods of the soiling season, b. when the day (J) in question belongs to a cleaning period: i. the statistical definition of a cleaning efficiency (Eff) for the day (J) considered as a function of the values ​​determined for the cleaning efficiency over the soiling season, ii. the calculation of a soiling rate (SRj) for the day (J) considered as a function of the soiling rate (SRj-1) of the previous day (Jl) and the defined cleaning efficiency (Eff), iii. repeating the previous steps, replacing the day (J) considered with the following day (J+1), c. when the day (J) in question does not belong to a cleaning period: i. the statistical definition of the time period to which the day (J) in question belongs among the different time periods of a cleaning period, the statistical definition being carried out according to the determined frequency values

8. for the time periods of the soiling season, ii. the statistical definition of a daily soiling ratio (S.Rate) and a duration (Dur) for the time period to which the day (J) in question belongs, based on the values ​​determined for the daily soiling ratio and the duration of said time period, iii. the calculation of a soiling rate (SRj) for the day (J) considered as a function of the soiling rate (SRj-1) of the previous day (Jl) and the defined daily soiling ratio (S.Rate), iv. the repetition of the calculation of a soiling rate for the following days by replacing the day (J) considered with the following day (J+l) as long as the following day (J+l) belongs to the duration defined for the time period, v. when the following day does not belong to the duration defined for the time period, the repetition of the previous steps replacing the day (J) considered with the last following day. A method according to any one of claims 1 to 7, wherein the method comprises an update phase including the following steps: a. obtaining additional data relating to environmental forecasts, b. the modification of soiling seasons and / or the temporal periods of soiling seasons based on additional data, and c. For each soiling season, the determination of characteristic variables for each time period of the soiling season, The update phase is followed, for at least one fouling season, by a phase of random generation of fouling profiles based on the characteristic variables of each time period of said fouling season, each fouling profile being representative of a possible temporal evolution of the fouling rate of photovoltaic modules for a future season corresponding to at least one season of soiling considered.

9. A method according to claim 8, wherein the additional data includes at least weather forecasts and / or data relating to circumstantial elements affecting the fouling of the photovoltaic modules of the photovoltaic installation.

10. A method according to claim 8 or 9, wherein the supplementary data includes at least weather forecasts, during the update phase, the modification step comprising: i. comparing the frequency of rainfall forecast in the weather forecast with the frequency of cleaning periods in the soiling seasons excluding cleaning interventions, and ii. modifying the soiling seasons and / or the temporal periods of the soiling seasons according to the result of the comparison.

11. A method according to any one of claims 1 to 10, wherein the historical data are obtained from measurements taken by sensors on the photovoltaic power plant.

12. A method according to any one of claims 1 to 11, wherein the random generation phase is implemented for each soiling season, the method comprising, for each soiling season, a phase of selecting a soiling profile from among the possible soiling profiles according to a predefined acceptable risk level.

13. A method according to claim 12, wherein the method includes a phase of predicting the production of the photovoltaic power plant based on the soiling profiles selected for each soiling season, and historical production data of the photovoltaic power plant.

14. A computer program product comprising program instructions recorded on a computer-readable medium, for the execution of a generation process according to any of the 19 claims 1 to 13 when the computer program is run on a computer.

15. Readable information carrier on which a computer program product according to claim 14 is stored.

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