Abstract
Non Intrusive Load Monitoring is the field that encompasses energy disaggregation and appliance detection. In recent years, Deep Neural Networks have improved the classification performance, using the standard data representation that most datasets provide; that being low-frequency or high-frequency data. In this paper, we explore the NILM problem from the scope of transfer learning. We propose a way of changing the feature space with the use of an image representation of the low-frequency data from UK-Dale and REDD datasets and the pretrained Convolutional Neural Network VGG16. We then train some basic classifiers and use the metric F1 score to test the performance of this representation. Multiple tests are performed to test the adaptability of the models to unseen houses and different datasets. We find that the performance is on par and in some cases outperforms that of popular deep NN algorithms.
This work has been funded by the E\(\Sigma \Pi \)A (2014–2020) Erevno-Dimiourgo-Kainotomo 2018/EPAnEK Program ‘Energy Controlling Voice Enabled Intelligent Smart Home Ecosystem’, General Secretariat for Research and Technology, Ministry of Education, Research and Religious Affairs.
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Kyrkou, L., Nalmpantis, C., Vrakas, D. (2019). Imaging Time-Series for NILM. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_16
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