Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach

Unlike sub-metering, which requires individual appliances to be equipped with their own meters, non-intrusive load monitoring (NILM) use algorithms to discover appliance individual consumption from the aggregated overall energy reading. Approaches that uses low frequency sampled data are more applicable in a real world smart meters that has typical sampling capability of <= 1Hz. In this paper, a systematic literature review on deep-learning-based approaches for NILM problem is conducted, aiming to analyse the four key aspects pertaining to deep learning adoption. This includes deep learning model adoption, features selection that are used to train the model, used data set and model accuracy. In our study, analyses the performance of four different deep learning approaches, namely, denoising autoencoder (DAE), recurrent long short-term memory (LSTM) , Recurrent gate recurrent unit (GRU), and sequence to point. Our experiments will be conducted using the two data sets, namely, REDD and UK-DALE. According to our analysis, the sequence to point model has achieved the best results with an average mean absolute error (MAE) of 14:98 watt when compared to other counterpart algorithms.

@article{Alkhulaifi2020,
  title     = {Investigation of Deep Learning-based Techniques for Load Disaggregation, Low-Frequency Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {1},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Abdolmaged Alkhulaifi and Abdulah J. Aljohani},
  doi       = {10.14569/IJACSA.2020.0110186},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110186}
}