Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whilst retaining the privacy of the underlying data. We compare two approaches: FL, and a clustered variant, FL+HC against a non-private, centralised learning approach and a fully private, localised learning approach. Within these approaches, we measure model performance using RMSE and computational efficiency. In addition, we suggest the FL strategies are followed by a personalisation step and show that model performance can be improved by doing so. We show that FL+HC followed by personalisation can achieve a $\sim$5\% improvement in model performance with a $\sim$10x reduction in computation compared to localised learning. Finally we provide advice on private aggregation of predictions for building a private end-to-end load forecasting application.
翻译:在能源工业中,负载预测是一项基本任务,有助于平衡供需并保持电网的稳定负荷。随着供应向不那么可靠的可再生能源发电过渡,智能米将证明是便利这些预测任务的一个关键组成部分。然而,在担心其微粒消费数据被侵入的有隐私意识的消费者中,智能计量的采用率很低。在这项工作中,我们提议并探索一种基于联合学习(FLF)的方法,以分布式、协作的方式培训预测模型,同时保留基本数据的隐私。我们比较了两种方法:FL和一组变体,即FL+HC,以非私人集中化的学习方式和完全私有的本地化的学习方式。在这些方法中,我们用RMSE和计算效率衡量模型的性能。此外,我们建议FL战略之后有一个个性化步骤,并表明通过这样做可以改进模型的性能。我们证明FL+HC在个人化之后,在模型性能方面可以实现5美元元的改进,比本地化学习在计算方面减少10美元。最后,我们建议用私人的预测进行私人的负式预测。