HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems

Privacy and transparency can coexist in critical infrastructure: HXAI demonstrates that grid operators can obtain decision-relevant insights for managing electricity demand without accessing sensitive household-level data. Appliance-level consumption information remains local and is never transmitted to operators, addressing a key tension between utility needs and consumer privacy.

Hierarchical approaches protect privacy while maintaining usefulness: By separating analysis into local (household) and zonal (aggregated) levels, the framework prevents traditional explainable AI methods from revealing sensitive information. This two-tier structure shows that privacy protection doesn't require sacrificing the quality of explanations needed for grid management.

Privacy budgets can limit cumulative exposure over time: The framework explicitly manages how much privacy is exposed through repeated queries from grid operators, preventing the gradual erosion of privacy that can occur when systems are queried many times. This addresses a practical concern in ongoing operational systems.

Real-world energy data validation confirms practical viability: Testing on both simulated and real-world datasets demonstrates that HXAI works in practice, not just in theory. The approach successfully supports zonal load management decisions while maintaining household privacy protections, suggesting it could be deployed in actual smart grid systems.