Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems.

ABSTRACT: The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are inte

SUBMITTER: Baldassarre A 

PROVIDER: S-EPMC10951540 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature