Author:
Zheng Guoqing,Hu Yuming,Xiao Zhenlong,Ding Xinghao
Publisher
Springer Nature Singapore
Reference25 articles.
1. Angelis, G.F., Timplalexis, C., Krinidis, S., Ioannidis, D., Tzovaras, D.: NILM applications: literature review of learning approaches, recent developments and challenges. Energy Buildings 261, 111951 (2022)
2. Athanasoulias, S., Sykiotis, S., Kaselimi, M., Protopapadakis, E., Ipiotis, N.: A first approach using graph neural networks on non-intrusive-load-monitoring. In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 601–607 (2022)
3. Dai, E., Chen, J.: Graph-augmented normalizing flows for anomaly detection of multiple time series. arXiv preprint arXiv:2202.07857 (2022)
4. Decock, J., Kaddah, R., Read, J., et al.: Conv-NILM-net, a causal and multi-appliance model for energy source separation. arXiv preprint arXiv:2208.02173 (2022)
5. de Diego-Otón, L., Fuentes-Jimenez, D., Hernández, Á., Nieto, R.: Recurrent LSTM architecture for appliance identification in non-intrusive load monitoring. In: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6. IEEE (2021)