Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device. (June 2023) - Luan, Wenpeng; Zhang, Ruiqi; Liu, Bo; Zhao, Bochao; Yu, Yixin
Highlights: A novel lightweight network constructed by two convolutional layers and dense layers, which approximately occupies 2.48 MB and requires 1.12 million floating point operations (MFLOPs). The sequence-to-sequence learning method is introduced into the proposed network to monitor the state and power of concerned appliances in real-time. More accurate appliance energy usage profile information is derived and provided for the edge users with continuous disaggregation results updates within defined time window. Experiments on ground truth data validate the efficiency of the proposed model and demonstrate its application potential on edge devices. Abstract: Non-intrusive load monitoring (NILM), extracting the appliances' usage profiles by decomposing a household's aggregate electricity consumption, has become increasingly important for home energy management. Studies show that users are more likely to adjust their usage behaviors if the real-time feedback on their energy consumption details is provided. Recent state-of-the-art deep learning models have delivered a good performance on load disaggregation; however, these models require tremendous computation and memory to tune huge numbers of parameters, which makes them difficult to be implemented on edge devices to derive real-time appliance usage information for users while keep user's privacy. In this paper, a lightweight sequence-to-sequence neural network model consisting of only two convolutional layers and dense layers is proposed, which can provide users with both real-time appliance power usage and improved energy consumption profiles. The online results are produced by picking up every end-point of output sequences of the network, whereas the past energy profiles or offline results of appliance can be obtained by averaging all points of the output sequence. By implementing the model in edge device, the user's privacy is maintained. The lightweight NILM algorithm is evaluated with low-frequency sampling data in two public available datasets: UKDALE and REFIT. Assessment results prove that the proposed model exhibits comparable or even better disaggregation performance with dramatically reduced model size and computation requirements, and thus demonstrates its application potential on edge devices.