Denoising Diffusion Straightforward Models for Energy Conversion Monitoring Data Imputation

Monitoring of energy conversion process confronts great difficulties due to extreme value jumps or data packet loss under extreme operating conditions, consequently resulting in data missing. To tackle these issues, researchers propose diffusion-based time series imputation approaches. However, there are critical limitations of methods adopted by these models: first, dense reverse inference (DRI), where conventional diffusion methods suffer from time-consuming imputation procedures and thus the loss of effective information over a long transmission process; second, indirect prediction strategy (IPS), which causes inaccurate temporal data imputation results. To address these limitations, we propose denoising diffusion straightforward models (DDSMs) for missing data imputation in energy conversion process monitoring. Specifically, based on the conditional mechanism, we innovatively use the accelerated sampling strategy in temporal models to reduce the lengthy inference time caused by DRI and propose a well-designed straightforward training algorithm for a straightforward estimation to improve the imprecise inference results acquired by IPS. Extensive experiments on real-world dataset demonstrate the effectiveness and superiority of DDSM and our approach consistently outperforms previous temporal generative methods significantly.