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.

翻译：本地能源社区正成为可持续发展领域的关键参与者。这些社区面临的一个重大挑战是通过有效管理能源生产与消费之间的平衡来实现自给自足。为应对这一挑战，开发并实施能够提供准确预测的预测模型至关重要，这些预测随后可被优化与规划算法所利用。然而，预测解决方案的应用常受限于隐私约束与法规，因为参与本地能源社区的用户（有理由地）不愿与他人共享其消费模式。在此背景下，联邦学习（FL）的应用可成为一种可行的解决方案，它能够在无需用户间共享隐私敏感信息的情况下构建预测模型。本研究展示了如何利用联邦学习与长短期记忆（LSTM）网络实现这一目标，并揭示了数据共享与预测精度之间的权衡关系。