Learning Task-Aware Energy Disaggregation: a Federated Approach
This work addresses energy disaggregation for residential users by enabling decentralized learning, though it appears incremental as it builds on existing federated and meta-learning techniques.
The paper tackles the challenge of training centralized non-intrusive load monitoring (NILM) models due to data privacy and heterogeneity issues by proposing a decentralized, task-adaptive learning scheme that combines meta and federated learning. Simulation results on benchmark datasets validate its efficiency in inferring appliance-level consumption across diverse homes and appliances.
We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on large amount of training data coming from a number of residential homes. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data, while load data coming from different regions or electricity users may exhibit heterogeneous usage patterns. Both practical concerns make training a single, centralized NILM model challenging. In this paper, we propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively. Simulation results on benchmark dataset validate proposed algorithm's performance on efficiently inferring appliance-level consumption for a variety of homes and appliances.