Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for the computer-assisted modeling of a technical system, comprising: modeling one or more output vectors on the basis of one or more input vectors by a learning process of a neural network (NN) based on training data comprising known input vectors and output vectors, a respective output vector comprising one or more operating variables of the technical system and a respective input vector comprising one or more input variables which influence the operating variable(s); wherein the neural network (NN) is a feed-forward network having a plurality of layers (I, H1, H2, H3, O) which are connected to one another and comprise an input layer (I), a plurality of hidden layers (H1, H2, H3) and an output layer (O), the input layer (I) containing a number of input neurons for describing the input vector(s), and a respective hidden layer (H1, H2, H3) containing a number of hidden neurons, and the output layer (O) containing a number of output neurons for describing the output vector(s); and wherein the output layer (O) comprises a plurality of output clusters (O1, O2, O3) each comprising one or more output neurons, said plurality of output clusters corresponding to the plurality of hidden layers (H1, H2, H3), and each output cluster (O1, O2, O3) describing the same output vector and being connected to another hidden layer (H1, H2, H3). 2. The method as claimed in claim 1, wherein the input layer (I) of the neural network (NN) is connected to each of the hidden layers (H1, H2, H3). 3. The method as claimed in claim 1, wherein a respective output vector comprises one or more operating variables for a plurality of successive future times within a future period. 4. The method as claimed in claim 1, wherein a respective input vector comprises one or more predicted input variables for a future time of successive future times within a future period. 5. The method as claimed in claim 1, wherein the technical system is an electrical energy generation installation. 6. The method as claimed in claim 5, wherein the energy generation installation comprises a wind power installation and/or a solar energy installation selected form a group consisting of a solar thermal installation and a photovoltaic installation. 7. The method as claimed in claim 5, wherein a respective output vector comprises, as operating variables, amounts of energy (ES) generated by the energy generation installation for a plurality of successive future times, an amount of energy (ES) being the amount of energy generated between two successive future times. 8. The method as claimed claim 5, wherein a respective input vector comprises, as input variables, one or more predicted environmental conditions for a future time from a plurality of future times. 9. The method as claimed in claim 8, wherein the predicted environmental condition(s) are weather data comprising one or more of the following variables: one or more ambient temperatures; one or more humidity values; one or more wind speeds and/or wind directions; one or more values relating to the cloud cover of the sky; one or more solar radiation values. 10. The method as claimed in claim 1, wherein the input layer (I) also comprises one or more input neurons for describing one or more further input vectors, the further input vector(s) comprising one or more of the operating variables of the technical system which are determined using an analytical model. 11. The method as claimed in claim 1, wherein 10 or more hidden layers are provided and/or each hidden layer comprises between 20 and 30 hidden neurons. 12. The method as claimed in claim 1, wherein during the learning process of the neural network, the difference between the output vector described by the output cluster (O1, O2, O3) and the output vector according to the training data is minimized, as the target variable, for each output cluster (O1, O2, O3). 13. The method as claimed in claim 1, wherein the learning process of the neural network takes place on the basis of error back-propagation. 14. A method for predicting one or more operating parameters of a technical system, wherein one or more input variables are supplied, via the input layer (I), to a neural network (NN) which learns using a method as claimed in claim 1, whereupon the neural network (NN) determines a corresponding output vector having one or more operating parameters of the technical system for at least one output cluster (O1, O2, O3) of the output layer (O). 15. The method as claimed in claim 14, wherein the corresponding output vectors are determined for a plurality of output clusters and for all output clusters (O1, O2, O3), the operating variables of which output vectors are then averaged. 16. A computer program product having a program code which is stored on a machine-readable data storage medium and is intended to carry out a method as claimed in claim 1 when the program code is executed on a computer. 17. The method as claimed in claim 1, wherein the electrical energy generation installation is a regenerative electrical energy generation installation. 18. The method as claimed in claim 3, wherein the future period comprises one or more days and the times have an interval of one hour. 19. The method as claimed in claim 4, wherein the future period comprises one or more days and the times have an interval of one hour. 20. The method as claimed in claim 8, wherein the predicted environmental conditions are weather data.