摘要：由于可再生能源发电固有的间歇性以及用电端的随机性，电力供需平衡正变得愈发困难。电网运营商需要获取家庭用电量中具有决策价值的细粒度洞察，以调控高峰负荷并设计需求响应式电价；然而，在这一层面提高透明度会带来显著的隐私泄露隐患。传统的可解释人工智能（XAI）方法可能会暴露敏感信息，而常规的隐私保护手段往往会削弱模型解释的实用性。为了解决这一难题，我们提出了 HXAI——一种分层框架，既能严格保护隐私，又能为电网级需求响应管理提供高可用性的可解释性分析。HXAI 包含两大核心组件：(1) 本地模型：在本地安全隔离环境中生成细粒度的用电归因解释；(2) 区域模型：对这些解释进行聚合以支持电网级宏观分析，并通过灵活的隐私预算管理机制强制实施隐私防护。我们明确限制了应对运营商多次重复查询时的累积隐私暴露风险，并证明该框架能够在不损害家庭隐私的前提下完整保留决策相关信息。在模拟和真实世界能源数据集上的实验结果表明，HXAI 能为区域负荷管理提供极具价值的洞见，同时确保电器级别的细粒度用电数据仅保留在本地，绝不向电网运营商传输。我们的研究表明，在差分隐私机制下，保留解释的语义结构而非单纯最小化数值误差，才是实现高质量 XAI 的核心关键。该框架为在能源管理体系中兼顾隐私安全与可解释性开辟了新途径。

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.