Low-Power Appliance Recognition Using Recurrent Neural Networks

Abstract

Indoor energy consumption can be understood by breaking overall power consumption down into individual components and appliance activations. The clas- sification of components of energy usage is known as load disaggregation or ap- pliance recognition. Most of the previous efforts address the separation of devices with high energy demands. In many contexts though, such as an office, the devices to separate are numerous, heterogeneous, and have low consumptions. The disag- gregation problem becomes then more challenging and, at the same time, crucial for understanding the user context. In fact, from the disaggregation one can deduce the number of people in an office room, their activities, and current energy needs. In this paper, we review the characteristics of office appliances load disaggregation efforts. We then illustrate a proposal for a classification model based on Recur- rent Neural Network (RNN). RNN is used to infer device activation from aggre- gated energy consumptions. The approach shows promising results in recognizing 14 classes of 5 different devices being operated in our office, reaching 99.4% of Cohen’s Kappa measure.