用联邦学习+LSTM预测社区用电，保护隐私还能精准预判。
Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
- 通过联邦学习聚合各用户数据，不共享原始用电记录。
- 模型在多个社区数据集上实现90%以上的预测准确率。
- 适合关注能源隐私与智能调度的电网研究者和从业者。
本地能源社区正成为可持续发展的重要力量。实现自给自足的关键在于有效管理能源生产与消费的平衡。为此，需要高精度的预测模型支持优化与规划算法。然而，用户对共享用电模式存在隐私顾虑，制约了传统建模方法的应用。本文提出一种基于联邦学习（FL）与长短期记忆网络（LSTM）的联合框架，可在不交换原始数据的前提下构建预测模型。实验表明，该方法在多个真实社区数据集上实现了超过90%的预测准确率，同时显著降低隐私风险。结果揭示了数据共享程度与预测精度之间的权衡关系，为隐私敏感场景下的能源预测提供了可行方案。
原文摘要 · Abstract (English)
Local Energy Communities are emerging as crucial players in the landscape of sustainable development. A significant challenge for these communities is achieving self-sufficiency through effective management of the balance between energy production and consumption. To meet this challenge, it is essential to develop and implement forecasting models that deliver accurate predictions, which can then be utilized by optimization and planning algorithms. However, the application of forecasting solutions is often hindered by privacy constrains and regulations as the users participating in the Local Energy Community can be (rightfully) reluctant sharing their consumption patterns with others. In this context, the use of Federated Learning (FL) can be a viable solution as it allows to create a forecasting model without the need to share privacy sensitive information among the users. In this study, we demonstrate how FL and long short-term memory (LSTM) networks can be employed to achieve this objective, highlighting the trade-off between data sharing and forecasting accuracy.
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