System modeling, control and optimization

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 is configured to model the operation of the power generating unit so to calculate 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, wherein the network comprises a weight vector and a feedback coefficient, and wherein the disturbance rejection model comprises a disturbance rejection configuration;
• wherein the process comprises:
• obtaining 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;
• and
• training the disturbance rejection model pursuant to the training dataset;
• wherein the training comprises calculating updated values for each of the weight vector and the feedback coefficient of the network by minimizing an error function that includes a first hyperparameter and a second hyperparameter;
• and
• wherein the first hyperparameter includes a vector for penalizing the weight vector and the second hyperparameter comprises a scalar.