eFHMM: Event-Based Factorial Hidden Markov Model for Real-Time Load Disaggregation
Author:
Affiliation:
1. Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/5165411/9865089/09761814.pdf?arnumber=9761814
Reference12 articles.
1. An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data
2. Approximate inference in additive factorial HMMs with application to energy disaggregation;kolter;Proc 15th Int Conf Artif Intell Stat,2012
3. Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models
4. Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring
5. Adaptive event detection for representative load signature extraction;yan;arXiv 2107 1128,2021
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1. FederatedNILM: A Distributed and Privacy-Preserving Framework for Non-Intrusive Load Monitoring Based on Federated Deep Learning;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18
2. An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data;2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);2023-01-16
3. A Study of the Effects of Appliance Energy Signatures on Different Neural Network Types in Nonintrusive Load Monitoring;IEEE Transactions on Instrumentation and Measurement;2023
4. Sequence-to-Point Learning Based on Spatio-Temporal Attention Fusion Network for Non-Intrusive Load Monitoring;2023
5. A Scoping Review of Energy Load Disaggregation;Progress in Artificial Intelligence;2023
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