Modeling a compression plant using recurrent neural networks

Compression plants are characterized by the fact that they possess a large number of state variables that are coupled non-linearly with each other. The paper focuses on the exploration of recurrent neural networks for modeling the dynamic behavior of a laboratory setup of a compression plant. Using data collected from the setup, recurrent multilayer perceptron networks are trained. The networks are validated not only with test data measured under similar external conditions but also with those that are gathered when the measurements of the external temperature are beyond the range inspected during the collection of the training data. Despite a significant change in external conditions, the validation results showed a fairly good performance in a multi-step prediction of the temperature and relative humidity inside the refrigerator