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Variational Regression; Multi-Target Energy Disaggregation
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DOI：10.3390/s23042051.png)
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摘要
En 中文
Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder's parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.
Keyword:
non-intrusive load monitoring
energy disaggregation
NILM
deep learning
variational inference
multi-target regression
KL divergence
convolution neural networks
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3.5
论文数:
7.2W
被引数:
20.9W
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Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning通过多标签一致性深度字典学习和深度变换学习从智能电表测量中同时检测多个电器OAAI
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