A method for controlling an electrical power supply station comprising at least one device for producing electrical energy from a natural renewable energy source and at least one device (120, 122) for storing the electrical energy produced, comprising: - predicting by a wavelet neural network of a first value (PL(N+h)) of an extrinsic parameter (Pe) at the station at a predefined future prediction time (N+h) from measurements of the extrinsic parameter (Pe); and - controlling at least one of the production and storage devices (110, 120, 122) on the basis of a command (V'); characterised in that it further comprises: - predicting a second value (PC(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) on the basis of measurements of the extrinsic parameter (Pe) all having an age less than a predefined threshold, independently of any measurement of the extrinsic parameter (Pe) having an age greater than this predefined threshold, the wavelet neural network having been trained on the basis of measurements of the extrinsic parameter (Pe) having an age greater than the predefined threshold; and - determining a third predicted value (P(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) from the first and second predicted values (PL(N+h), PC(N+h)), the command (V') being determined from the third predicted value (P(N+h)) of the extrinsic parameter (Pe); and - incrementally training the wavelet neural network from a comparison between the first predicted value (PL(N+h)) of the extrinsic parameter (Pe) and a measurement (Pe(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h). - predicting by a wavelet neural network of a first value (PL(N+h)) of an extrinsic parameter (Pe) at the station at a predefined future prediction time (N+h) from measurements of the extrinsic parameter (Pe); and - controlling at least one of the production and storage devices (110, 120, 122) on the basis of a command (V'); characterised in that it further comprises: - predicting a second value (PC(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) on the basis of measurements of the extrinsic parameter (Pe) all having an age less than a predefined threshold, independently of any measurement of the extrinsic parameter (Pe) having an age greater than this predefined threshold, the wavelet neural network having been trained on the basis of measurements of the extrinsic parameter (Pe) having an age greater than the predefined threshold; and - determining a third predicted value (P(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) from the first and second predicted values (PL(N+h), PC(N+h)), the command (V') being determined from the third predicted value (P(N+h)) of the extrinsic parameter (Pe); and - incrementally training the wavelet neural network from a comparison between the first predicted value (PL(N+h)) of the extrinsic parameter (Pe) and a measurement (Pe(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h). - predicting a second value (PC(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) on the basis of measurements of the extrinsic parameter (Pe) all having an age less than a predefined threshold, independently of any measurement of the extrinsic parameter (Pe) having an age greater than this predefined threshold, the wavelet neural network having been trained on the basis of measurements of the extrinsic parameter (Pe) having an age greater than the predefined threshold; and - determining a third predicted value (P(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h) from the first and second predicted values (PL(N+h), PC(N+h)), the command (V') being determined from the third predicted value (P(N+h)) of the extrinsic parameter (Pe); and - incrementally training the wavelet neural network from a comparison between the first predicted value (PL(N+h)) of the extrinsic parameter (Pe) and a measurement (Pe(N+h)) of the extrinsic parameter (Pe) at the prediction time (N+h).