@inproceedings{bibcite_421,
  author = {Yiling Jia and Nipun Batra and Hongning Wang and Kamin Whitehouse},
  title = {A Tree-Structured Neural Network Model for Household Energy Breakdown},
  abstract = {Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents{\textquoteright} awareness of energy use and can help save up to 15\% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.},
  year = {2019},
  journal = {The World Wide Web Conference},
  chapter = {2872},
  pages = {7},
  month = {05},
  url = {https://par.nsf.gov/biblio/10106916},
  doi = {10.1145/3308558.3313405},
}