Modeling Dynamic Processes With Deep Neural Networks: A Case Study With A Gas-Fired Absorption Heat Pump

Deriving mathematical models for the simulation of dynamic processes is costly and time-consuming. This paper examines the possibilities of deep neural networks (DNNs) as a means to facilitate and accelerate this step in development. DNNs are machine learning models that have become a state-of-the-art solution to a wide range of data analysis and pattern recognition tasks. Unlike mathematical modeling approaches, DNN approaches require little to no domain-specific knowledge. Given a sufficient amount of data, a model of the complex nonlinear input-to-output relations of a dynamic system can be learned autonomously. To validate this DNN based modeling approach, we use the example of a gas-fired absorption heat pump. The DNN is learned based on several measurement series recorded during a hardware-in-the-loop (HiL) simulation of the heat pump. A mathematical reference model of the heat pump that was tested in the same HiL environment is used for a comparison of a mathematical and a DNN based modeling approach. Our results show that DNNs can yield models that are comparable to the reference model. The presented methodology covers the data preprocessing, the learning of the models and their validation. It can be easily transferred to more complex dynamic processes.