Under the “dual carbon” goals,low-carbon production and green development have become a shared commitment among high energy-consuming enterprises. Centered around electricity,integrated energy systems (IES) enable the efficient incorporation of renewable energy,thereby. uggle to adapt to the escalating complexity of today's Energy Internet of Things (EIoT), necessitating a pivotal paradigm shift. In response, this work introduces a pioneering data-driven SA framework, termed digital twin-based situation awareness (DT-SA), aiming to bridge existing gaps between. Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenge the traditional model-based situation awareness (SA) in energy internet of thing (EIoT). This work explores digital twin of EIoT (EIoT-DT), and then provides a novel data-driven SA. TL;DR: Based on the combination of the latest data technologies and machine learning algorithms, DT-SA as mentioned in this paper transferred those stubborn situation awareness challenges to digital space, and then addressed them by building a domain-specific and data-friendly digital twin (DT). Situational awareness, in the context of this guide, is the understanding of one's environment and the ability to predict how it might change due to various factors. With the rapid growth of EIoT, which includes various energy resources like solar panels, wind turbines, and electric vehicles, traditional methods of.
