The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation

TL;DR

This paper demonstrates that injecting noise into training data significantly improves the accuracy and robustness of gray-box models for equipment emulation, especially in dynamic systems like heat exchangers.

Contribution

It introduces a noise-injection method to enhance gray-box model creation, addressing uncertainties from nonlinearity and unmodeled dynamics.

Findings

Root mean square error reduced from 0.68°C to 0.27°C.

Model accuracy improved by approximately 50-60%.

Method validated on real device data with live streaming.

Abstract

Gray-box models offer significant benefit over black-box approaches for equipment emulator development for equipment since their integration of physics provides more confidence in the model outside of the training domain. However, challenges such as model nonlinearity, unmodeled dynamics, and local minima introduce uncertainties into grey-box creation that contemporary approaches have failed to overcome, leading to their under-performance compared with black-box models. This paper seeks to address these uncertainties by injecting noise into the training dataset. This noise injection enriches the dataset and provides a measure of robustness against such uncertainties. A dynamic model for a water-to-water heat exchanger has been used as a demonstration case for this approach and tested using a pair of real devices with live data streaming. Compared to the unprocessed signal data, the application of noise injection resulted in a significant reduction in modeling error (root mean square error), decreasing from 0.68 to 0.27{\deg}C. This improvement amounts to a 60% enhancement when assessed on the training set, and improvements of 50% and 45% when validated against the test and validation sets, respectively.