A Federated Learning Model With Short Sequence To Point Mechanism For Smart Home Energy Disaggregation

Abstract

Residential households contribute significantly to the overall energy consumption in developed countries. To reduce their energy consumption, they need solutions that help them track the use of their appliances at home. Non-Intrusive Load Monitoring (NILM) with short Sequence-to-Point (Seq2Point) is a deep learning method used to track and recognize what appliances are used in the houses and their respective energy consumption. To apply NILM with short Seq2Point in the real world, a large amount of data from households must be collected and transferred to centralized servers for further analysis. Such a process does not always preserve consumers' security and privacy. To address this challenge, this paper proposes a combination of Federated Learning (FL) and NILM to make the entire system safe and trustworthy in order to avoid the risks of customers' privacy leakage. In FL, local models are developed on each consumer end and trained on local data instead of gathering data from all devices and sending it back to the central server. By using this method, data will be handled locally in a single residence while increasing the system's speed and security. The proposed model is evaluated using a real-life dataset (UK-Dale) of Appliance Level Electricity from the UK. The results show that our system provides better accuracy and preserves the privacy of consumers.