Deep Domain Adaptation for Non-Intrusive Load Monitoring Based on a Knowledge Transfer Learning Network

• Published in: IEEE transactions on smart grid Vol. 13; no. 1; pp. 280 - 292
• Main Authors: Lin, Jun, Ma, Jin, Zhu, Jianguo, Liang, Huishi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Appliances; Customers; Data models; domain adaptation; Domains; Energy consumption; energy disaggregation; Energy management; Feature extraction; Hidden Markov models; Home appliances; Knowledge management; Load modeling; Monitoring; Non-intrusive load monitoring; Representations; temporal convolutional network; transfer learning
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2021.3115910

Understanding customers' energy consumption at the individual appliances level is crucial for the planning and implementation of demand response (DR) programs. The appliances' usage profiles can be disaggregated from whole-house energy consumption data using non-intrusive load monitoring (NILM) methods. The appliance load patterns of each customer are considerably different, which make it challenging to train a model with strong generalization ability. In this paper, a novel methodology using transfer knowledge between domains for NILM is proposed. A temporal convolutional network is developed to learn the dynamic features of individual appliance load. A domain adaption loss is used to quantify the domain distribution discrepancy between source and target domain representation. By jointly optimizing domain adaptation and energy disaggregation, an invariant representation across domains for the individual appliance states can be learned. Data experiments on ground truth data validate the accuracy and the robustness of the proposed model, and demonstrate its superior transferability and application potential under those scenarios of data shortage.