Hierarchical AI protects home energy data while helping grid operators
HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems
Artificial Intelligence
Summary
Managing electricity is tricky because renewable power is unpredictable and people use energy in different ways. The authors designed a system called HXAI that explains energy use patterns without revealing private details about what happens inside individual homes. This approach keeps detailed appliance information local and only shares safe, combined insights with grid managers. It helps balance power demand and supply while respecting household privacy.
What this means in practice
- •For energy grid operators: Use HXAI to manage electricity demand with detailed but privacy-safe household consumption insights.
- •For smart home solution providers: Integrate HXAI to offer privacy-preserving energy usage explanations that assist grid-level coordination without exposing appliance data.$Commercial implications: Enables product features that provide privacy-compliant insights to grid operators, enhancing smart energy services.
Authors
Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Yan Zhang
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.