Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Abstract

The development of techniques that allow the efficient identification of residential loads (nonintrusive load monitoring) is a key factor for the practical implementation of demand response programs. Recently, in terms of nonintrusive load monitoring, the use of deep learning has gained attention, mainly the models based on convolutional neural networks. However, the efficient training of these models is strongly dependent on the quantity and balance of the data, i.e., characteristics that are not normally found in nonintrusive load monitoring datasets. To deal with these challenges, this paper proposes an approach based on three stages, that are: (i) time series transformation into 2D images; (ii) feature extraction using deep transfer learning; and (iii) classification/labelling of loads. Moreover, it was analyzed and defined the better window size per load in relation to the f1-score reached by the classifiers. In this sense, it was considered five loads present in the Reference Energy Disaggregation Dataset, where the proposed approach was able to obtain an average f1-score of 83.2%. From the results analysis, it was demonstrated the greater capacity of the proposed approach to infer and generalize its responses.