Non-Intrusive Load Monitoring (NILM) is a blind source separation problem which aims to estimate the electricity usage of individual appliances by decomposing a household's aggregate electricity consumption. Recent state-of-the-art neural network models have delivered a good performance on load disaggregation, however these models have tremendous model size with huge numbers of parameters to tune and require large amounts of data for training, thus are computation and memory demanding, which makes it difficult to be practically implemented. To address this problem, we study the mechanism of depthwise separable convolution and propose a modified convolutional neural network model, which results in a lightweight NILM algorithm with the aim to be deployed on edge devices with limited computation power as mobile phones or smart meters. The lightweight NILM algorithm is evaluated with public available dataset: UKDALE. Assessment results prove that the proposed model delivers acceptable disaggregation performance with dramatically reduced model size and computation requirements.
|Title of host publication||Proceedings - 2021 IEEE Sustainable Power and Energy Conference|
|Subtitle of host publication||Energy Transition for Carbon Neutrality, iSPEC 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2021|
|Event||2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 - Nanjing, China|
Duration: 22 Dec 2021 → 24 Dec 2021
|Name||Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021|
|Conference||2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021|
|Period||22/12/21 → 24/12/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work is supported in part by the National Natural Science Foundation of China (Key Program), NSFC-SGCC (U2066207).
- Depthwise separable convolution
- Lightweight neural network
- Non-intrusive load monitoring