Short-term load forecasting (STLF) can support strategies for power retailers. Training based on aggregate load datasets can yield more precise STLF models. In real scenarios, however, retailers can only access the consumer information they serve. The aggregation of their data for centralized forecasting requires access to private local data, which may lead to potential security concerns. This paper proposes a method namely Privacy-Preserving ${k}$ -means Federated Learning (PPK-Fed), to facilitate STLF co-modeling among retailers. Federated Learning (FL) is founded on the assumption of data consistency, which does not necessarily exist in real datasets. PPK-Fed incorporates ${k}$ -means clustering together with convolutional neural networks into FL. PPK-Fed is proven to reduce the impact of potential data incongruence embedded in retailer local datasets. Besides, to address the sensitivity of the ${k}$ -means prototype to the potential anomalies in the real dataset, PPK-Fed fuses density-based anomaly detection into ${k}$ -means clustering under FL to improve robustness. For further model security, a secure multi-party computation (SMPC) scheme is designed in PPK-Fed. The model validity, privacy-preserving features, and robustness to anomalies have been verified using a real load dataset.

中文翻译：

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具有不一致负载数据分布的隐私保护短期负载预测的鲁棒联合建模

短期负荷预测（STLF）可以支持电力零售商的策略。基于聚合负载数据集的训练可以产生更精确的 STLF 模型。然而，在实际场景中，零售商只能访问他们所服务的消费者信息。聚合数据以进行集中预测需要访问私有本地数据，这可能会导致潜在的安全问题。本文提出了一种方法，即Privacy-Preserving ${k}$ - 表示联合学习 (PPK-Fed)，以促进零售商之间的 STLF 联合建模。联邦学习（FL）建立在数据一致性的假设之上，而真实数据集中不一定存在这种假设。 PPK-Fed 合并 ${k}$ - 表示与卷积神经网络一起聚类到 FL 中。事实证明，PPK-Fed 可以减少零售商本地数据集中嵌入的潜在数据不一致的影响。此外，为了解决敏感问题 ${k}$ -意味着真实数据集中潜在异常的原型，PPK-Fed 将基于密度的异常检测融合到 ${k}$ - 表示在 FL 下进行聚类以提高鲁棒性。为了进一步提高模型安全性，PPK-Fed 中设计了安全多方计算（SMPC）方案。使用真实负载数据集验证了模型的有效性、隐私保护功能和对异常的鲁棒性。