1 citations · 3 across the 5 of their papers we have counts for
5 papers
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
Xudong Wang, Guoming Tang, Junyu Xue +3
Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the…
Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring
Junyu Xue, Xudong Wang, Xiaoling He +3
Non-intrusive load monitoring (NILM) aims to disaggregate total electricity consumption into individual appliance usage, thus enabling more effective energy management. While deep…
ZSMerge: Zero-Shot KV Cache Compression for Memory-Efficient Long-Context LLMs
Xin Liu, Xudong Wang, Pei Liu +1
The linear growth of key-value (KV) cache memory and quadratic computational in attention mechanisms complexity pose significant bottlenecks for large language models (LLMs) in lon…
Towards Real-world Deployment of NILM Systems: Challenges and Practices
Junyu Xue, Yu Zhang, Xudong Wang +2
Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused o…
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring
Yu Zhang, Guoming Tang, Qianyi Huang +2
Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usa…