1 citations · 2 across the 5 of their papers we have counts for
6 papers
Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring
Xudong Wang, Jiacheng Cui, Junyu Xue +2
Non-intrusive load monitoring (NILM) estimates appliance-level consumption from a whole-home meter, but appliance-specific models and fixed output inventories make coverage costly…
CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction
Kaiyuan Zhai, Jiacheng Cui, Zhehao Zhang +4
Cross-domain HVAC energy prediction is essential for scalable building energy management, particularly because collecting extensive labeled data for every new building is both cost…
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…
BandPilot: Toward Performance- and Contention-Aware GPU Dispatching in AI Clusters
Kunming Zhang, Hanlong Liao, Junyu Xue +2
Modern multi-tenant AI clusters are increasingly communication-bound, driven by high-volume and multi-round GPU-to-GPU collective communication. Consequently, the GPU dispatcher's…
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…
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…