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

2026-10-01

AI Summary

This study addresses the inherent conflict between grid demand management and household electricity privacy in distributed energy systems by proposing a hierarchical explainable AI (XAI) framework. Methodologically, the approach synergizes local fine-grained explanation generation with regional aggregation analysis, integrating differential privacy techniques and a flexible budget management mechanism to constrain cumulative privacy exposure. The core contributions include the first hierarchical privacy-preserving mechanism and the establishment of a novel paradigm for differentially private XAI that prioritizes preserving semantic structure over minimizing numerical error. Experimental results demonstrate that this framework provides actionable insights for regional load management without uploading appliance-level data, thereby simultaneously achieving robust privacy protection and model interpretability.

Abstract

Balancing electricity demand and supply is increasingly difficult due to the inherent intermittency of renewable power generation and the stochastic power consumption. Grid operators require fine-grained, decision-relevant insights into household energy consumption to manage peak loads and design responsive tariffs, but increased transparency at this level raises significant privacy concerns. Traditional methods for explainable AI (XAI) can reveal sensitive information, while standard privacy techniques often reduce the usefulness of explanations. To address this issue, we introduce HXAI, a hierarchical framework that preserves privacy while enabling reasonable explainable analysis for grid-level demand management. HXAI consists of two main components: (1) a local model that generates fine-grained explanations within a secure, private environment, and (2) a zonal model that aggregates these explanations to support grid-level analysis while enforcing privacy through flexible privacy-budget management. We explicitly limit cumulative privacy exposure under repeated operator queries and show that the proposed framework preserves decision-relevant information without compromising household privacy. Experiments on both simulated and real-world energy datasets demonstrate that HXAI provides useful insights for zonal load management while ensuring that appliance-level consumption remains local and is never transmitted to grid operators. Our results show that preserving the semantic structure of explanations, rather than minimizing numerical error, is the key to XAI under differential privacy. This framework provides a way to achieve both privacy and explainability in energy management.

Problem

• Explainable AI
• Privacy-preserving
• Distributed energy systems
• Differential privacy
• Demand management

Innovation

• Hierarchical Explainable AI
• Differential Privacy
• Privacy Budget Management
• Distributed Energy Systems
• Semantic Structure Preservation