Electrical Digital Twin Market Size, Share, Growth Analysis, By Twin Type(Digital Gas & Steam Power Plant, Digital Wind Farm, Digital Grid, Digital Hydropower Plant), By Usage Type(Product Digital Twin, Process Digital Twin, System Digital Twin), By Deployment Type(On-Premises, Cloud), By End-User(Utilities, Grid Infrastructure Operators), By Application(Asset Performance Management, Business & Operations Optimization), By Region - Industry Forecast 2024-2031

Electrical Digital Twin Market size was valued at USD 9.47 billion in 2019 and is poised to grow from USD 12.9 billion in 2023 to USD 121.48 billion by 2031, growing at a CAGR of 36.3% in the forecast period (2024-2031).

Electrical digital twin helps utilities to streamline data upkeep and interchange. A virtual model of an object or system that spans its lifespan updates from real-time data, and employs simulation, machine learning, and reasoning to aid in decision-making is known as a "digital twin. With the expenditures in development, research, and testing, it is anticipated that the electrical digital twin business will produce cash output in the upcoming years. But as the electrical digital twin business matures, it will likely only produce small profits for the major participants in the next years.

Future Electrical Digital Twin Market expansion is anticipated to be fueled by elements like the growing need in the healthcare industry for real-time aspects and the growing usage of digital twins for proactive maintenance to prevent unforeseen outages. Higher system complexity, however, poses significant obstacles and restrains the market's expansion. Additionally, it is anticipated that the electrical digital twin market participants would benefit greatly from the increased acceptance of cutting-edge technology for the deployment of digital twin applications.

Predictive maintenance is a common application for digital twins across numerous industries. In digital twin-based predictive maintenance, a real-time sensor is used to track and assess the condition of a material, operation, or system as well as its failure modes and criticality in comparison to previous data. This data has been analyzed, and one can use it to make predictions. A neural network detects irregular patterns in the physical asset's incoming sensor data and represents those patterns in predictive models. Such information can be used to predict failures; if a physical asset's configuration parameters are likely to cause it to fail, a virtual twin can spot the issue, assess its seriousness, notify technicians, and recommend a course of action.

US Electrical Digital Twin Market  is poised to grow at a sustainable CAGR for the next forecast year.