A method for forecasting cloudiness on a defined geographic location, characterised in that it consists at the least of the following steps: a) recovering cloudiness mapping data (value and geographic position) acquired by satellite and relating to a geographic study region containing the location for n times, with n being an integer strictly greater than 1 ({T1,..., Tn} series of times); b) comparing the geographic data recovered in step a) of a first time (Tp) and a second time (Tq) (p and q have values of between 1 and n), the first time (Tp) being subsequent to the second time (Tq); c) determining a shift in the positions of the cloudiness values between the second time (Tq) and the first time (Tp); d) delimiting a geographic region of interest containing at least one cloudiness value and relating to the shift determined in step (c); e) recovering cloudiness data (DAT_SELEC) relating to those contained in the geographic region of interest for at least one of the n times ({T1,..., Tn} series of times); f) estimating the cloudiness on the location at a time subsequent to the n times ({T1,..., Tn} series of times) by applying a generalised linear model (MOD_G) using the cloudiness data (DAT_SELECT) recovered in step e); and in that effective cloudiness data are also recovered for the n times ({T1,..., Tn} decreasing series, n > 1), and in step f), the generalised linear model (MOD_G) uses these data as additional inputs; in that the generalised linear model (MOD_G) applied in step f) is a linear regression the explanatory variables of which are the inputs of the model; and in that the generalised linear model (MOD_G) is adjusted to a training base consisting of a base (V_DAT) containing at least one effective cloudiness piece of data and of an input data base (C_DAT) necessary for applying step f) to estimate cloudiness at the times corresponding to the effective cloudiness data base (V_DAT). a) recovering cloudiness mapping data (value and geographic position) acquired by satellite and relating to a geographic study region containing the location for n times, with n being an integer strictly greater than 1 ({T1,..., Tn} series of times); b) comparing the geographic data recovered in step a) of a first time (Tp) and a second time (Tq) (p and q have values of between 1 and n), the first time (Tp) being subsequent to the second time (Tq); c) determining a shift in the positions of the cloudiness values between the second time (Tq) and the first time (Tp); d) delimiting a geographic region of interest containing at least one cloudiness value and relating to the shift determined in step (c); e) recovering cloudiness data (DAT_SELEC) relating to those contained in the geographic region of interest for at least one of the n times ({T1,..., Tn} series of times); f) estimating the cloudiness on the location at a time subsequent to the n times ({T1,..., Tn} series of times) by applying a generalised linear model (MOD_G) using the cloudiness data (DAT_SELECT) recovered in step e); and in that effective cloudiness data are also recovered for the n times ({T1,..., Tn} decreasing series, n > 1), and in step f), the generalised linear model (MOD_G) uses these data as additional inputs; in that the generalised linear model (MOD_G) applied in step f) is a linear regression the explanatory variables of which are the inputs of the model; and in that the generalised linear model (MOD_G) is adjusted to a training base consisting of a base (V_DAT) containing at least one effective cloudiness piece of data and of an input data base (C_DAT) necessary for applying step f) to estimate cloudiness at the times corresponding to the effective cloudiness data base (V_DAT). and in that effective cloudiness data are also recovered for the n times ({T1,..., Tn} decreasing series, n > 1), and in step f), the generalised linear model (MOD_G) uses these data as additional inputs; in that the generalised linear model (MOD_G) applied in step f) is a linear regression the explanatory variables of which are the inputs of the model; and in that the generalised linear model (MOD_G) is adjusted to a training base consisting of a base (V_DAT) containing at least one effective cloudiness piece of data and of an input data base (C_DAT) necessary for applying step f) to estimate cloudiness at the times corresponding to the effective cloudiness data base (V_DAT).