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Behind-the-Meter Load and PV Disaggregation via Deep Spatiotemporal Graph Generative Sparse Coding With Capsule Network

  • Published:2023 Issue: Volume: Page:1-15
  • ISSN:2162-237X
  • Container-title:IEEE Transactions on Neural Networks and Learning Systems
  • language:
  • Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Saffari Mohsen1,Khodayar Mahdi1ORCID,Khodayar Mohammad E.2ORCID,Shahidehpour Mohammad3ORCID

Affiliation:

1. Department of Computer Science, The University of Tulsa, Tulsa, OK, USA

2. Department of Electrical and Computer Engineering, Southern Methodist University, Dallas, TX, USA

3. Department of Electrical and Computer Engineering (ECE), Illinois Institute of Technology, Chicago, IL, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Cited by 5 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. Sparse Attention Graph Gated Recurrent Unit for Spatiotemporal Behind-The-Meter Load and PV Disaggregation;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

2. RLIDT: A Novel Reinforcement Learning-Infused Deep Transformer Model for Multivariate Electricity Load Forecasting;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

3. Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

4. Spatiotemporal Deep Learning for Power System Applications: A Survey;IEEE Access;2024

5. Hybrid Gromov–Wasserstein Embedding for Capsule Learning;IEEE Transactions on Neural Networks and Learning Systems;2024
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