Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
arXiv:2605.21903v1 Announce Type: cross Abstract: The integration of machine learning with domain-specific physics is transforming the design, monitoring, and control of electricity systems, where data scarcity, limited interpretability, and the need to enforce physical laws constrain purely data-driven models. Physics-informed machine learning (PIML) addresses these limitations by embedding governing equations directly into the learning process, yielding accurate, efficient, and scalable solutions for Industry 4.0 applications. This article reviews hybrid PIML architectures for electricity sy
The increasing complexity and integration of renewable energy sources in electricity grids necessitate more sophisticated and adaptable control systems that purely data-driven models cannot reliably provide.
Hybrid physics-informed neural networks offer a pathway to address critical limitations in grid management, enabling more resilient, efficient, and scalable next-generation electricity systems.
The adoption of PIML architectures will transform grid design, monitoring, and control, moving away from solely data-driven or purely physics-based approaches towards more robust and interpretable solutions.
- · Energy utilities
- · Smart grid technology providers
- · AI/ML companies specializing in PIML
- · Renewable energy producers
- · Legacy grid infrastructure providers (without PIML adoption)
- · Purely data-driven AI solutions for energy (without physics integration)
- · Traditional grid operators resistant to new tech
Enhanced grid stability and efficiency with increased renewable integration.
Accelerated deployment of distributed energy resources due to improved management capabilities.
Reduced energy costs and increased energy independence for regions adopting these advanced systems.
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