Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing

Non-intrusive load monitoring (NILM) can infer the status of the appliances in operation and their energy consumption by analyzing the energy data collected from monitoring devices. With the rapid increase of electric loads in amount and type, the traditional way of uploading all energy data to cloud is facing enormous challenges. It becomes increasingly significant to construct distinguishable load signatures and build robust classification models for NILM. In this paper, we propose a load signature construction method for load recognition task in home scenarios. The load signature is based on the Gramian angular field encoding theory, which is convenient to construct and significantly reduces the data transmission volume of the network. Moreover, edge computing architecture can reasonably utilize computing resources and relieve the pressure of the server. The experimental results on NILM datasets demonstrate that the proposed method obtains superior performance in the recognition of household appliances under insufficient resources.