A method for computer-assisted monitoring of an electrical energy-generating installation, in which output variables (y(t)) of the installation are prognosticated using a data-driven model (NN) based on corresponding input variables (x(t)). A confidence measurement (C(t)) is determined for respective input variables (x(t)), using one or more density estimators (DE), this measurement being higher, the greater the similarity of the input variables (x(t)) to known input variables from training data with which the data-driven model (NN) and the density estimator (DE) are taught. Based thereon, an average weighted deviation (E(t)) is determined between the prognosticated output variables (y(t)) and the output variables (y0(t)) actually occurring. If the average weighted deviation (y(t)) exceeds a predetermined threshold (ETh) successive times, an error in operation is detected and an alarm is issued.