Knowledge-Guided Learning of Temporal Dynamics and its Application to Gas Turbines

Pawel Bielski, Aleksandr Eismont, Jakob Bach, Florian Leiser, Dustin Kottonau, Klemens Böhm

Abstract: Modeling dynamical systems is a fundamental task in scientific and engineering fields, often accomplished by applying theory-based models with mathematical equations. Yet, in cases where these equations cannot be established or parameterized properly, theory-based models are not applicable. Instead, a viable alternative is to learn the system dynamics directly from data, for example with deep learning models. However, traditional deep learning models often produce physically inconsistent results and struggle to generalize to unseen data, especially when training data is limited. One solution to this shortcoming is knowledge-guided deep learning, leveraging prior knowledge about the expected behavior of a dynamical system. In this work, we identify and formalize permissible system states, a novel type of prior knowledge that is often available for systems in the context of temporal dynamics modeling. This prior knowledge describes dynamic states that the system is allowed to take during its operation. We propose a knowledge-guided multi-state constraint to encode this type of prior knowledge through a loss function, making it applicable to any deep learning model. This approach allows to create an accurate data-driven model with minimal effort and data requirements. We validate the effectiveness of our method by applying it to model the temporal behavior of a gas turbine in response to an input control signal. Our results indicate that the proposed method reduces the prediction error by up to 40%. In addition to reducing the dependency on extensive training data, our method mitigates training randomness and enhances the consistency of predictions with the expected behavior.

External IDs:dblp:conf/eenergy/BielskiEBLKB24