arXiv:2308.07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy. In this work, we present a probabilistic guarantee for power flow learning and voltage risk estimation, derived through the framework of Gaussian Process (GP) regression. Specifically, we establish a bound on the expected estimation error that connects the GP's predictive variance to confidence in voltage risk estimates, ensuring stat
The increasing reliance on AI for critical infrastructure, coupled with the inherent risks of power grid stability, makes formal guarantees for ML performance increasingly urgent.
This research addresses a major impediment to AI adoption in safety-critical applications by introducing a framework for probabilistic guarantees, which could accelerate deployment in energy systems.
Machine learning models can now be deployed in power flow management with formal, quantifiable confidence in their predictions, mitigating risks for operators and regulators.
- · AI/ML developers for energy
- · Power grid operators
- · Renewable energy integration firms
- · Energy technology investors
- · Traditional SCADA/control system vendors
- · Energy utilities slow to adopt AI
Increased AI deployment in energy management and other critical infrastructure sectors due to established trust frameworks.
Enhanced grid stability and efficiency, potentially leading to more resilient and cost-effective energy distribution.
Accelerated transition to smart grids and distributed energy resources, reshaping the energy landscape and potentially alleviating energy bottlenecks.
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Read at arXiv cs.LG