School of Computer Science, The University of Sydney, Camperdown, 2006, Australia.
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Sci Rep. 2023 Apr 21;13(1):6569. doi: 10.1038/s41598-023-33131-0.
Improving energy efficiency is a crucial aspect of building a sustainable smart city and, more broadly, relevant for improving environmental, economic, and social well-being. Non-intrusive load monitoring (NILM) is a computing technique that estimates energy consumption in real-time and helps raise energy awareness among users to facilitate energy management. Most NILM solutions are still a single machine approach and do not fit well in smart cities. This work proposes a model-agnostic hybrid federated learning framework to collaboratively train NILM models for city-wide energy-saving applications. The framework supports both centralised and decentralised training modes to provide a cluster-based, customisable and optimal learning solution for users. The proposed framework is evaluated on a real-world energy disaggregation dataset. The results show that all NILM models trained in our proposed framework outperform the locally trained ones in accuracy. The results also suggest that the NILM models trained in our framework are resistant to privacy leakage.
提高能源效率是建设可持续智能城市的关键方面，更广泛地说，也有助于改善环境、经济和社会福祉。非侵入式负载监测（NILM）是一种实时估计能源消耗的计算技术，有助于提高用户的能源意识，以促进能源管理。大多数 NILM 解决方案仍然是一种单机方法，不太适合智能城市。这项工作提出了一种与模型无关的混合联邦学习框架，以协作训练用于全市节能应用的 NILM 模型。该框架支持集中式和分散式训练模式，为用户提供基于集群的、可定制的和最佳的学习解决方案。所提出的框架在真实世界的能量分解数据集上进行了评估。结果表明，我们提出的框架中训练的所有 NILM 模型在准确性方面都优于本地训练的模型。结果还表明，我们框架中训练的 NILM 模型能够抵抗隐私泄露。
