Avoiding Domain Drift and Constant Predictions with Diffusion Enhanced Vector-Quantized Autoencoders for Temperature Predictions

Accurate temperature prediction in rotary cement kilns is crucial for process stability and equipment longevity. However, the repeated application of the predicted values creates an error accumulation over the length of the forecast, causing a domain drift of the predictions. This issue is exacerbated for image prediction, as more degrees of freedom lead to a higher sensitivity to small errors as local structures are lost. Using a vector-quantized autoencoder can mitigate the problem as it can map predictions back to the source domain, but it leads to almost constant predictions. Thus, we propose the usage of an additional diffusion model to avoid the local minimum of constant predictions. Our method maintains reliable in-domain predictions, preventing localized temperature peaks and ensuring stable kiln operation. Our experiments show, that the proposed vector-quantized diffusion model (VQ-Diff) can forecast much longer time sequences than reference methods with high accuracy, by being limited to the generation of in-domain images.