.png){kind=icon}
.png){kind=icon}
Return
Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2020.2974347.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation is introduced. The proposed model, CoBiLSTM, harnesses the representational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for: non-causality and adaptivity to contextual factors (e.g., seasonality). A Bayesian-optimized framework is introduced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Furthermore, the proposed model is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method's superiority compared to the current state of the art.
Keywords:
Adaptation models
Biological system modeling
Context modeling
Home appliances
Hidden Markov models
Load modeling
Aggregates
Deep learning
NILM
non-intrusive load monitoring
energy disaggregation
recurrent neural networks
LSTM
bidirectional
Bayesian optimization
adaptability
sequence-to-sequence learning
context-aware
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
5.7K
Citations:
4.3W