发表机构
University of Oslo（奥斯陆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布式能源系统中可解释AI的隐私泄露问题，提出分层框架HXAI，通过本地与区域模型结合隐私预算管理，在保护家庭隐私的同时提供电网级决策洞察，实验证明其有效性。
AI 中文摘要
由于可再生能源发电固有的间歇性和电力消费的随机性，平衡电力需求与供应变得越来越困难。电网运营商需要细粒度、与决策相关的家庭能源消耗洞察，以管理峰值负荷并设计响应式电价，但在此层面提高透明度会引发重大的隐私问题。传统的可解释人工智能（XAI）方法可能泄露敏感信息，而标准隐私技术往往降低解释的有用性。为解决这一问题，我们提出了HXAI，一个分层框架，在保护隐私的同时，为电网级需求管理实现合理的可解释分析。HXAI包含两个主要组件：（1）一个在安全、私有环境中生成细粒度解释的本地模型，以及（2）一个区域模型，该模型聚合这些解释以支持电网级分析，同时通过灵活的隐私预算管理来强制隐私保护。我们明确限制了在重复运营商查询下的累积隐私暴露，并表明所提出的框架在不损害家庭隐私的情况下保留了决策相关信息。在模拟和真实能源数据集上的实验表明，HXAI为区域负荷管理提供了有用的见解，同时确保设备级消耗保持本地化，且从不传输给电网运营商。我们的结果表明，保留解释的语义结构，而非最小化数值误差，是差分隐私下XAI的关键。该框架为能源管理中同时实现隐私和可解释性提供了一条途径。
英文摘要
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.
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