Active Privacy-Preserving, Distributed Edge–Cloud Orchestration–Empowered Smart Residential Mains Energy Disaggregation in Horizontal Federated Learning

Abstract

Combinations of technical advances in artificial intelligence of things (AIoT) are becoming increasingly fundamental constituents of smart houses, buildings, and factories in cities. In smart grids that ensure the resilient delivery of electrical energy to support cities, effective demand-side management (DSM) can alleviate ever-increasing electricity demand from customers in downstream grid sectors. Compared with the traditional intrusive load monitoring (ILM) approach used by energy management systems (EMSs), energy disaggregation, which is an EMS component instead of the ILM approach, can monitor relevant electrical appliances in a nonintrusive manner such that an effective DSM scheme can be achieved. In this study, a distributed horizontal federated learning (HFL)–based energy management framework that implements an active privacy-preserving and edge–cloud collaborative computing–based energy disaggregation algorithm for smart mains energy disaggregation to energy-efficient smart houses/buildings is proposed, and its preliminary implementation, in which active two-stage energy disaggregation considering edge–cloud collaborative computing for autonomous AI modeling is achieved under HFL preserving user data privacy, is demonstrated. In the proposed framework, edge computing that collaborates with the cloud to form edge–cloud computing can serve as converged computing from which load data gathered by distributed on-site edge devices for online load monitoring/smart energy disaggregation are globally consolidated through an artificial intelligence (AI) model in the cloud (cloud AI) and which the model that realizes global knowledge modeling is then deployed for global AI deployment at the edge (edge AI) via global knowledge sharing. In addition, edge–cloud collaboration based on HFL not only improves data privacy and data security but also enhances network traffic, as it exchanges AI model updates (model weights and biases) for global collaborative AI modeling. This is the promising achievement, instead of transmitting raw private real-time data to a centralized cloud server for traditional model training. Simulations are conducted and used to demonstrate the feasibility and effectiveness of the proposed framework for smart mains energy disaggregation as an illustrative application paradigm of the framework; the overall load classification rate can be improved by a maximum of approximately 11% as reported from simulation results.