Method for the computer-assisted monitoring of the operation of a technical system, particularly of an electrical energy-generating installation, wherein the technical system is characterized at corresponding operating times (t) by a state vector comprising a number of input variables (x(t)) and at least one output variable (y(t)) which is to be monitored, wherein: a) the at least one output variable (y(t)) is predicted for respective operating times (t) on the basis of input variables occurring in the operation of the technical system with a data-driven model which is trained by means of training data from known state vectors; b) one or more density estimators, which are trained by means of known input variables (x(t)) of the training data, are applied for respective operating times (t) to the number of input variables (x(t)) at the corresponding operating time (t), whereby a confidence measure (C(t)) is defined which is higher the greater the similarity of the input variables (x(t)) at the corresponding operating time (t) to known input variables (x(t)) from the training data; characterized in that c) for respective cycles from a plurality of consecutive operating times (t), a weighted deviation (E(t)), averaged over the number of state vectors in the respective cycle, between the at least one predicted output variable (y(t)) and the at least one output variable (y 0 (t)) occurring in the operation of the technical system is defined, wherein state vectors whose number of input variables have low confidence measures (C(t)) are weighted less in the average weighted deviation; d) a malfunction of the technical system is detected if all average weighted deviations (E(t)), for a number of consecutive cycles which is greater than a predefined numerical threshold (Cnt Th), comprising one or more criteria, fulfil the criterion that the amount of these deviations exceeds a predefined threshold value (E Th). a) the at least one output variable (y(t)) is predicted for respective operating times (t) on the basis of input variables occurring in the operation of the technical system with a data-driven model which is trained by means of training data from known state vectors; b) one or more density estimators, which are trained by means of known input variables (x(t)) of the training data, are applied for respective operating times (t) to the number of input variables (x(t)) at the corresponding operating time (t), whereby a confidence measure (C(t)) is defined which is higher the greater the similarity of the input variables (x(t)) at the corresponding operating time (t) to known input variables (x(t)) from the training data; c) for respective cycles from a plurality of consecutive operating times (t), a weighted deviation (E(t)), averaged over the number of state vectors in the respective cycle, between the at least one predicted output variable (y(t)) and the at least one output variable (y 0 (t)) occurring in the operation of the technical system is defined, wherein state vectors whose number of input variables have low confidence measures (C(t)) are weighted less in the average weighted deviation; d) a malfunction of the technical system is detected if all average weighted deviations (E(t)), for a number of consecutive cycles which is greater than a predefined numerical threshold (Cnt Th), comprising one or more criteria, fulfil the criterion that the amount of these deviations exceeds a predefined threshold value (E Th).