Hybrid Transformer-RNN Architecture for Household Occupancy Detection Using Low-Resolution Smart Meter Data
Author:
Affiliation:
1. Monash University,Faculty of IT,Department of Data Science and AI,Melbourne,VIC,Australia,3800
Funder
Australian Research Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10311571/10311610/10312340.pdf?arnumber=10312340
Reference17 articles.
1. Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data
2. Identifying the relationship between seasonal variation in residential load and socioeconomic characteristics
3. Long Short-Term Memory
4. Household occupancy monitoring using electricity meters
5. Occupancy Detection for General Households by Bidirectional LSTM with Attention
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Inter-seasons and Inter-households Domain Adaptation Based on DANNs and Pseudo Labeling for Non-Intrusive Occupancy Detection;Transactions of the Japanese Society for Artificial Intelligence;2024-09-01
2. Analyzing Consumer Behavior Insights from Smart Meter Data: A Comparative Study of Deep Learning and Traditional Machine Learning Approaches;2024 11th Iranian Conference on Renewable Energy and Distribution Generation (ICREDG);2024-03-06
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