Abstract
Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.
Highlights
Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage
Using the pretrained model significantly reduced the test errors of the two models, indicating that the pattern learned by the model from REFIT can be applied to UK-DALE
The experimental results of this part support the conclusions about cross-domain transfer learning (CTL) in ref. [22]: due to differences in manufacturing standards, appliances from different countries (REFIT and REDD are from Britain and America, respectively) cannot be directly transferred; appliances in the same country can be directly transferred
Summary
Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This paper proposes a deep neural network model based on an attention mechanism This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. Based on the working conditions of the equipment, signal features can be classified into three types: steady state, transient state, and running mode Traditional NILM methods build equipment feature databases by manually extracting features, while deep neural networks can realize automatic learning from data, thereby avoiding the step of manually extracting features [6,7]. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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