Abstract
Non-Intrusive Load Monitoring (NILM) provides a way to acquire detailed energy consumption and appliance operation status through a single sensor, which has been proven to save energy. Further, besides load disaggregation, advanced applications (e.g., demand response) need to recognize on/off events of appliances instantly. In order to shorten the time delay for users to acquire the event information, it is necessary to analyze extremely short period electrical signals. However, the features of those signals are easily submerged in complex background loads, especially in cross-user scenarios. Through experiments and observations, it can be found that the feature of background loads is almost stationary in a short time. On the basis of this result, this paper provides a novel model called the concatenate convolutional neural network to separate the feature of the target load from the load mixed with the background. For the cross-user test on the UK Domestic Appliance-Level Electricity dataset (UK-DALE), it turns out that the proposed model remarkably improves accuracy, robustness, and generalization of load recognition. In addition, it also provides significant improvements in energy disaggregation compared with the state-of-the-art.
Highlights
Energy consumption has always been a major concern in the world, which can be alleviated with accurate and efficient load monitoring methods
(2) The proposed approach improves the performance of energy disaggregation, especially on multi-state appliances and programmable appliances
For two different convolutional neural networks (CNNs) as embedding layers, Concatenate-CNN models perform better than two baselines in average F1-score, as well as recall and precision for most appliances
Summary
Energy consumption has always been a major concern in the world, which can be alleviated with accurate and efficient load monitoring methods. NILM contains two main objectives: energy disaggregation and load recognition. Appliances are detected from the uncompleted operation cycle, that is the transient-state process of on/off events. Some advanced applications in the smart grid need to acquire appliance operation status for remote household control [2], such as demand response. It represents that the power supply side uses induction mechanism (e.g., price changes over time) to improve end Energies 2019, 12, 1572; doi:10.3390/en12081572 www.mdpi.com/journal/energies. The difference learning module dφ converts the features of the mixed load and the “implicit background” to the target feature X2(on).
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