A method for modeling a system that includes a disturbance rejection model configured for modeling an operation of the system so to generate a predicted value for a system output. The disturbance rejection model having a network for mapping system inputs to the system output, and input-output pairings, each representing a unique pairing of one of the system inputs with the system output. The method may include the steps of: calculating a confidence metric for a selected input-output pairing of the disturbance rejection model; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing. The confidence metric may indicate a probability that a predicted sign of a gain in the system output made by the disturbance rejection model is correct when the system input of the selected input-output pairing is varied.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for modeling a system, the method including a disturbance rejection model configured for modeling an operation of the system so to generate a predicted value for a system output at a future time, the disturbance rejection model further comprising: a network for mapping system inputs to the system output; and input-output pairings, each of which represent a unique pairing of one of the system inputs with the system output, the method comprising the steps of: calculating a confidence metric for a selected one of the input-output pairings of the disturbance rejection model; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing; wherein the confidence metric is configured to indicate a probability that a predicted sign of a gain in the system output at the future time made by the disturbance rejection model is correct when the system input of the selected one of the input-output pairings is varied. 2. The method of claim 1, wherein the predicted sign of the gain in the system output is defined as: a predicted sign of a value change for the system output between the time (t−1) and the time (t); wherein the predicted sign comprises: a positive value when the value change results in the predicted value for the system output at the time (t) increasing from the actual value of the system output (dt−1) at the time (t−1); and a negative value when the value change results in the predicted value for the system output at the time (t) decreasing from the actual value of the system output (dt−1) at the time (t−1); wherein: the disturbance rejection model is trained according to a disturbance rejection training algorithm and a training dataset, the training dataset comprising a time series dataset of actual values for the system inputs and the system output as determined from measurements taken of the operation of the system during a historical operating period, the historical operating period occurring prior to the future time; and the training dataset comprises T samples, the T samples taken at times=[1, 2, . . . , t, . . . , T] wherein a time (t), a time (t−1), a time (t−2) denote generalized samples in the training dataset where the time (t−2) occurs just prior to the time (t−1) and the time (t−1) occurs just prior to the time (t). 3. The method of claim 2, wherein the disturbance rejection model comprises a disturbance rejection configuration in which the predicted value made by the disturbance rejection model for the system output at the future time is based upon is based upon a predicted value made by the network for the system output at the future time and an error multiplied by a feedback coefficient; and wherein the error comprises a difference between: a predicted value made by the network of the controlled variable at the previous time; and an actual value of the system output at the previous time, wherein the actual value is based upon a measurement taken by a sensor disposed in the system for measuring an operating parameter related to the system output. 4. The method of claim 3, wherein the network comprises a neural network that includes multiple layers having nodes, the multiple layers including at least an input layer, an output layer, one or more hidden layers, and forward weight matrixes; and wherein: the input layer comprises a plurality of the nodes, the plurality of the nodes corresponding respectively to the system inputs, wherein each of the plurality of the nodes is configured to receive an input signal relating to a value for a particular one of the system inputs; the output layer comprises at least one of the nodes, the at least one of the nodes corresponding to the system output; the one or more hidden layers are disposed between the input layer and the output layer, each of the one or more hidden layers comprising a plurality of the nodes; and the forward weight matrices comprise connectors that connect the nodes of successive layers of the multiple layers of the neural network and a weight value for each of the connectors; wherein a weight vector defines the weight values for the connectors of the forward weight matrices. 5. The method of claim 4, wherein the confidence metric is based on a probabilistic distribution calculated for selected samples chosen from the T samples of the training dataset; wherein the system inputs at the time (t−1) and the time (t−2) are described by input vectors, which are denoted, respectively, as an input vector (xt−1) and an input vector (xt−2); wherein the system output at the time (t−1) is denoted as a system output (dt−1); and wherein the step of calculating the probabilistic distribution for the predicted value of the system output at the time (t) comprises calculating a mean (μ) and a variance (σ2) that define a normal distribution (). 6. The method of claim 5, wherein the mean (μ) for the probability distribution for the predicted value of the system output at the time (t) comprises the predicted value made by the disturbance rejection model for the system output at the time (t), as given by: μ(xt−1,xt−2,dt−1,k*,wMAP)=y(xt−1,xt−2,dt−1,k*,wMAP) where: the yt comprises the predicted value made by the disturbance rejection model for the system output at the time (t); the k* denotes a trained value for the feedback coefficient (k); and the wMAP denotes a trained value of the weight vector; wherein each of the trained values, the k* and the wMAP, are determined from the disturbance rejection model once trained via the disturbance rejection training algorithm pursuant to the training dataset. 7. The method of claim 6, wherein the variance (σ2) for the probability distribution for the predicted value of the system output at the time (t) is defined using the Bayesian evidence framework as: σ2(xt−1,xt−2,dt−1,k*,wMAP)=β−1+gtTA−1gt wherein: the β−1 comprises an inverse of a scalar hyperparameter denoted as β; the A is a second derivative of a negative log posterior distribution as given by: A=Diag(a)I+βH; where: the I comprises an identity matrix; and the H comprises a Hessian matrix, the Hessian matrix being computed using a R-propagation algorithm; and the gt comprises a vector gt that is calculated using at least one of the following equations: g t = ∂ y  ( x t - 1 , x t - 2 , k , w ) ∂ w  k = k * , w = w MAP ;   and   g t = ( ∂ NN  ( x t - 1 , w ) ∂ w - k  ∂ NN  ( x t - 2 , w ) ∂ w )  k = k * , w = w MAP . 8. The method of claim 7, wherein, given the probabilistic distribution of the predicted value of the system output at the time (t), the confidence metric comprises a probability calculated via a fraction, wherein: the numerator of the fraction comprises a sign for: the system output (dt−1) at the time (t−1) subtracted from the probabilistic distribution of the system output made by the trained disturbance rejection model at the time (t); and the denominator of the fraction comprises a sign for: the system output (dt−1) at the time (t−1) subtracted from the predicted value of the system output made by the trained disturbance rejection model at the time (t) and the system output (dt−1) at the time (t−1). 9. The method of claim 7, wherein given the probabilistic distribution of the predicted value of the system output at the time (t), which is designated as τt, the confidence metric comprises a probability (p) that the sign of the predicted change for the system output is correct at the time (t); wherein the probability (p) is given by: p  ( sign  ( τ t - d t - 1 ) sign  ( y  ( x t - 1 , x t - 2 , d t - 1 , k * , w MAP ) - d t - 1 ) > 0 ) . 10. The method of claim 9, wherein the mean (μ) of the probabilistic distribution (τt) is equal to: μ=y(xt−1,xt−2,dt−1,k*,wMAP); wherein, given the normal distribution of the probabilistic distribution (τt), a Gauss Cumulative Distribution (Φ) is utilized to compute the probability that the sign of the predicted change for the system output is correct at the time (t), which is given by: p  ( sign  ( τ t - d t - 1 ) sign  ( y  ( x t - 1 , x t - 2 , d t - 1 , k * , w MAP ) - d t - 1 ) > 0 ) = Φ  (  y  ( x t - 1 , x t - 2 , k * , w MAP ) - d t - 1  ) . 11. The method of claim 10, wherein, given the Gaussian Cumulative Distribution (Φ), the probability (p) comprises: p  ( sign  ( τ t - d t - 1 ) sign  ( y  ( x t - 1 , x t - 2 , d t - 1 , k * , w MAP ) - d t - 1 ) > 0 ) = 1 2 + 1 2  erf  (  y  ( x t - 1 , x t - 2 , k * , w MAP ) - d t - 1  2  σ 2  ( x t - 1 , x t - 2 , k * , w MAP ) ) where the erƒ( ) comprises a Gauss error function. 12. The method of claim 11, wherein the confidence metric, designated below as η, is computed as: η = 100  ∑ t = 2 T  C t , t  Φ  (  y  ( x t - 1 , x t - 2 , w MAP ) - d t - 1  ) ∑ t = 2 T  C t , t where Ct,t is a tth element of a diagonal of a C matrix, the C matrix comprising a T by T matrix configured to represent a coupling between time series elements of the training dataset. 13. The method of claim 9, wherein adjacent samples within the T samples of the training dataset are defined as paired samples that correspond to consecutively occurring times within the times=[1, 2, . . . , t, . . . , T]; wherein, pertaining to the training dataset, a move is defined as a change to a system input setting that occurs between the consecutively occurring times of one of the adjacent samples; and wherein the training dataset is configured such that each of the adjacent samples defined within the training dataset includes no more than one of the moves. 14. The method of claim 13, wherein the confidence metric is used to calculate a confidence score for the selected one of the input-output pairings; wherein the calculation of the confidence score comprises: determining a subset of samples from the T samples of the training dataset, the subset of samples comprises only those adjacent samples in which the move involves the selected one of the input-output pairings; calculating the confidence metric for each of the samples within the subset of samples; calculating an average value for the confidence metric for the subset of samples; and designating the average value as the confidence score for the selected one of the input-output pairings. 15. The method of claim 14, wherein the confidence score determined for the selected one of the input-output pairings is compared to a predetermined threshold; wherein the recommended modification for the disturbance rejection model is based on the comparison of the confidence score to the predetermined threshold; and wherein the recommended modification relates to whether additional data should be obtained for the training dataset. 16. The method of claim 15, wherein the predetermined threshold comprises a minimum threshold; and wherein the recommended modification for the disturbance rejection model comprises at least one of the following: when the comparison shows that the confidence score fails to satisfy the minimum threshold, the recommended modification comprises one regarding a high need for obtaining the additional data for training dataset; when the comparison shows that the confidence score satisfies the minimum threshold, the recommended modification comprises one regarding a low need for obtaining the additional data for training dataset. 17. The method of claim 16, wherein the recommended modification for the disturbance rejection model comprises defining the additional data as data corresponding to the system input of the selected one of the input-output pairings. 18. The method of claim 14, wherein the calculation of the confidence score is repeated for at least one other of the input-output pairings such that the confidence score is determined for two or more of the input-output pairings; wherein the confidence scores determined for the two or more of the input-output pairings are each compared against a predetermined threshold; wherein the recommended modification for the disturbance rejection model comprises one related to a need to obtain additional data for the training dataset based upon how each of the confidence scores compared to the predetermined threshold. 19. The method of claim 14, wherein the calculation of the confidence score is repeated for at least one other of the input-output pairings such that the confidence score is determined for two or more of the input-output pairings; wherein the confidence scores determined for the two or more of the input-output pairings are compared against each other so to determine at least a first input-output pairing that has a confidence score that is preferable compared to a second input-output pairing; wherein the recommended modification for the disturbance rejection model comprises one in which the need to obtain the additional data for the system input of the second input-output pairing is prioritized over the need to gather additional data for the system input of the first input-output pairing. 20. The method of claim 9, wherein adjacent samples within the T samples of the training dataset are defined as paired samples that correspond to consecutively occurring times within the times=[1, 2, . . . t, . . . , T]; wherein, pertaining to the training dataset, a move is defined as a change to a system input setting that occurs between the consecutively occurring times of one of the adjacent samples; and wherein the training dataset is configured such that a majority of the adjacent samples defined within the training dataset has more than one of the moves. 21. The method of claim 20, wherein the confidence metric is used to calculate a confidence score for the selected one of the input-output pairings; wherein the calculation of the confidence score comprises: modifying the training dataset to include only the moves made in the training dataset that involve the system input that corresponds to the selected one of the input-output pairings while holding constant values related to the moves made in the training dataset that involve the system inputs relating to other ones of the input-output pairings; and calculating the confidence score for the selected one of the input-output pairings as an average of the confidence metric calculated for each of the moves within the modified training dataset. 22. The method of claim 21, wherein the calculation of the confidence score is repeated for at least one other of the input-output pairings such that the confidence score is determined for two or more of the input-output pairings; wherein the confidence scores determined for the two or more of the input-output pairings are compared against each other so to determine at least a first input-output pairing that has a confidence score that is preferable compared to a second input-output pairing; and wherein the recommended modification for the disturbance rejection model comprises one in which a need to gather additional data for the system input of the second input-output pairing is prioritized over a need to gather additional data for the system input of the first input-output pairing. 23. A system comprising: a power generating unit; a control system operably connected to the power generating unit for controlling an operation thereof, the control system comprising: a hardware processor; and a machine readable storage medium on which is stored: a disturbance rejection model; and instructions that cause the hardware processor to execute a process related to control of the power generating unit; wherein the disturbance rejection model models the operation of the power generating unit so to generate a predicted value for an output of the power generating unit at a future time, the disturbance rejection model comprising: a network for mapping inputs of the power generating unit to the output; and input-output pairings, each of which represents a unique pairing of one of the inputs with the output; wherein the process comprises: calculating a confidence metric for a selected one of the input-output pairings of the disturbance rejection model, wherein the confidence metric comprises a probability that a predicted sign of a gain in the output at the future time made by the disturbance rejection model is correct when the input of the selected one of the input-output pairings is varied; and recommending a modification be made to the disturbance rejection model based upon the confidence metric calculated for the selected one of the input-output pairing. 24. The system of claim 23, wherein the predicted sign of the gain in the output is defined as: a predicted sign of a value change for the output between the time (t−1) and the time (t); wherein the predicted sign comprises: a positive value when the value change results in the predicted value for the output at the time (t) increasing from the actual value of the output (dt−1) at the time (t−1); and a negative value when the value change results in the predicted value for the output at the time (t) decreasing from the actual value of the output (dt−1) at the time (t−1); wherein: the disturbance rejection model is trained according to a disturbance rejection training algorithm and a training dataset, the training dataset comprising a time series dataset of actual values for the inputs and the output as determined from measurements taken of the operation of the power generating unit during a historical operating period, the historical operating period occurring prior to the future time; and the training dataset comprises T samples, the T samples taken at times=[1, 2, . . . , t, . . . , T] wherein a time (t), a time (t−1), a time (t−2) denote generalized samples in the training dataset where the time (t−2) occurs just prior to the time (t−1) and the time (t−1) occurs just prior to the time (t). 25. The system of claim 24, wherein the disturbance rejection model comprises a disturbance rejection configuration in which the predicted value made by the disturbance rejection model for the output at the future time is based upon is based upon a predicted value made by the network for the output at the future time and an error multiplied by a feedback coefficient; and wherein the error comprises a difference between: a predicted value made by the network of the controlled variable at the previous time; and an actual value of the output at the previous time, wherein the actual value is based upon a measurement taken by a sensor disposed in the power generating unit for measuring an operating parameter related to the output; wherein the network comprises a neural network that includes multiple layers having nodes, the multiple layers including at least an input layer, an output layer, one or more hidden layers, and forward weight matrixes; and wherein: the input layer comprises a plurality of the nodes, the plurality of the nodes corresponding respectively to the inputs, wherein each of the plurality of the nodes is configured to receive an input signal relating to a value for a particular one of the inputs; the output layer comprises at least one of the nodes, the at least one of the nodes corresponding to the output; the one or more hidden layers are disposed between the input layer and the output layer, each of the one or more hidden layers comprising a plurality of the nodes; and the forward weight matrices comprise connectors that connect the nodes of successive layers of the multiple layers of the neural network and a weight value for each of the connectors; wherein a weight vector defines the weight values for the connectors of the forward weight matrices. 26. The system of claim 25, wherein the confidence metric is based on a probabilistic distribution calculated for selected samples chosen from the T samples of the training dataset; wherein the inputs at the time (t−1) and the time (t−2) are described by input vectors, which are denoted, respectively, as an input vector (xt−1) and an input vector (xt−2); wherein the output at the time (t−1) is denoted as a output (dt−1); and wherein the step of calculating the probabilistic distribution for the predicted value of the output at the time (t) comprises calculating a mean (μ) and a variance (σ2) that define a normal distribution (). 27. The system of claim 26, wherein the mean (μ) for the probability distribution for the predicted value of the output at the time (t) comprises the predicted value made by the disturbance rejection model for the output at the time (t), as given by: μ(xt−1,xt−2,dt−1,k*,wMAP)=y(xt−1,xt−2,dt−1,k*,wMAP) where: the yt comprises the predicted value made by the disturbance rejection model for the output at the time (t); the k* denotes a trained value for the feedback coefficient (k); and the wMAP denotes a trained value of the weight vector; wherein each of the trained values, the k* and the wMAP, are determined from the disturbance rejection model once trained via the disturbance rejection training algorithm pursuant to the training dataset. 28. The system of claim 27, wherein the variance (σ2) for the probability distribution for the predicted value of the output at the time (t) is defined using the Bayesian evidence framework as: σ2(xt−1,xt−2,dt−1,k*,wMAP)=ρ−1+gtTA−1gt wherein: the β−1 comprises an inverse of a scalar hyperparameter denoted as β; the A is a second derivative of a negative log posterior distribution as given by: A=Diag(α)I+βH; where: the I comprises an identity matrix; and the H comprises a Hessian matrix, the Hessian matrix being computed using a R-propagation algorithm; and the gt comprises a vector gt that is calculated using at least one of the following equations: g t = ∂ y  ( x t - 1 , x t - 2 , k , w ) ∂ w  k = k * , w = w MAP ;   and   g t = ( ∂ NN  ( x t - 1 , w ) ∂ w - k  ∂ NN  ( x t - 2 , w ) ∂ w )  k = k * , w = w MAP . 29. The system of claim 28, wherein adjacent samples within the T samples of the training dataset are defined as paired samples that correspond to consecutively occurring times within the times=[1, 2, . . . , t, . . . , T]; wherein, pertaining to the training dataset, a move is defined as a change to a system input setting that occurs between the consecutively occurring times of one of the adjacent samples; wherein the confidence metric is used to calculate a confidence score for a selected one of the input-output pairings; wherein the calculation of the confidence score comprises: determining a subset of samples from the T samples of the training dataset, the subset of samples comprises only those adjacent samples in which the move involves the selected one of the input-output pairings; calculating the confidence metric for each of the samples within the subset of samples; calculating an average value for the confidence metric for the subset of samples; and designating the average value as the confidence score for the selected one of the input-output pairings. 30. The system of claim 29, wherein the confidence score determined for the selected one of the input-output pairings is compared to a predetermined threshold; wherein the recommended modification for the disturbance rejection model is based on the comparison of the confidence score to the predetermined threshold; and wherein the recommended modification relates to whether additional data should be obtained for the training dataset. 31. The system of claim 30, wherein the predetermined threshold comprises a minimum threshold; and wherein the recommended modification for the disturbance rejection model comprises at least one of the following: when the comparison shows that the confidence score fails to satisfy the minimum threshold, the recommended modification comprises one regarding a high need for obtaining the additional data for training dataset; when the comparison shows that the confidence score satisfies the minimum threshold, the recommended modification comprises one regarding a low need for obtaining the additional data for training dataset. 32. The system of claim 29, wherein the calculation of the confidence score is repeated for at least one other of the input-output pairings such that the confidence score is determined for two or more of the input-output pairings; wherein the confidence scores determined for the two or more of the input-output pairings are compared against each other so to determine at least a first input-output pairing that has a confidence score that is preferable compared to a second input-output pairing; wherein the recommended modification for the disturbance rejection model comprises one in which the need to obtain the additional data for the input of the second input-output pairing is prioritized over the need to gather additional data for the input of the first input-output pairing.