Fundamentally, most modern cars and phones are programmed to learn from their environment – facial features, sounds, and even common driving routes. Due to the patterns of recognition, these systems can precisely predict and suggest favored options at the blink of the eye.
On these lines, if a system could be designed for the precision and responsiveness of critical national challenges, such as weather forecasting, disease diagnoses, and power grid reliability.
Smart Power Grid Simulator – a new software application – uses neural networks for efficiently solving power grid simulations. Such simulations are crucial for planning and optimizing electricity delivery. In fact, the initial test results of Smart Power Grid Simulator showed it solved power flow computation approximately three times faster than the traditional numerical model, that too without loss of precision.
Application first ever AI-based for Power Grid Simulations
Meanwhile, Smart Power Grid Simulator uses a new neural network technique called multi-task learning modeling, according to developers of the software application. And, the application is the first ever of AI for power grid.
“In the last several years, advancements in AI and high-performance computing has allowed to explore the method,” said one of the researchers behind the application.
The virtual presentation of the research is scheduled in November during Supercomputing 2020. The event is the world’s largest yearly gathering of professionals in the fields of networking, high-performance computing, storage, and analysis.
In terms of capability, Smart Power Grid Simulator is nearly three times speedier with fewer iterations and nearly the same accuracy, when compared with current power flow simulation instruments.
Currently, for the flow of power at optimum levels, power grid operators use offline computer models that examines data based on a host of scenarios.