The following publications are possibly variants of this publication:
- Bayesian method for multimode non-Gaussian process monitoringZhiqiang Ge, Zhihuan Song, Muguang Zhang, Ruowei Fu, Zhibo Zhu. cdc 2009: 4927-4932 [doi]
- Non-Intrusive Load Monitoring Method Considering the Time-Segmented State ProbabilityLina Liu, Fangshuo Li, Zhijiong Cheng, Yifei Zhou, Jie Shen, Ruichao Li, Siyu Xiong. access, 10:39627-39637, 2022. [doi]
- LightNILM: lightweight neural network methods for non-intrusive load monitoringZhenyu Lu, Yurong Cheng, Mingjun Zhong, Wenpeng Luan, Yuan Ye 0001, Guoren Wang. sensys 2022: 383-387 [doi]
- Non-Intrusive Load Monitoring Using a CNN-LSTM-RF Model Considering Label Correlation and Class-ImbalanceXiao Zhou, Shujian Li, Chengxi Liu, Haojun Zhu, Nan Dong, Tianying Xiao. access, 9:84306-84315, 2021. [doi]
- MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring Based on a Dual-CNN ModelJialing He, Jiamou Liu, Zijian Zhang, Yang Chen, Yiwei Liu, Bakh Khoussainov, Liehuang Zhu. AAAI 2023: 5078-5086 [doi]
- A non-intrusive load recognition method combining adaptive PSO algorithm and CNN modelZhichao Liu, Yachao Wang, Zhiyuan Ma, Mengnan Cao, Mingda Liu, Xiaochu Yang. jifs, 45(6):10921-10935, December 2023. [doi]
- Non-Intrusive Load Monitoring Using Identity Library Based on Structured Feature Graph and Group Decision ClassifierXin Wu, Yifan Guo, Meng Yan, Xiang Li, Lijuan Yao, Gangjun Gong. tsg, 14(3):1958-1973, May 2023. [doi]