Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS
This work addresses energy efficiency for building residents by improving load monitoring, but it is incremental as it builds on existing imaging methods with specific enhancements.
The paper tackles the problem of limited data and transfer potential in energy management systems by proposing a dimensionality expansion and transfer learning approach for load monitoring, achieving an average weighted F1 score of 0.88 across datasets and up to 12 percentage points improvement in precision drop for unseen appliances.
Energy management systems (EMS) rely on (non)-intrusive load monitoring (N)ILM to monitor and manage appliances and help residents be more energy efficient and thus more frugal. The robustness as well as the transfer potential of the most promising machine learning solutions for (N)ILM is not yet fully understood as they are trained and evaluated on relatively limited data. In this paper, we propose a new approach for load monitoring in building EMS based on dimensionality expansion of time series and transfer learning. We perform an extensive evaluation on 5 different low-frequency datasets. The proposed feature dimensionality expansion using video-like transformation and resource-aware deep learning architecture achieves an average weighted F1 score of 0.88 across the datasets with 29 appliances and is computationally more efficient compared to the state-of-the-art imaging methods. Investigating the proposed method for cross-dataset intra-domain transfer learning, we find that 1) our method performs with an average weighted F1 score of 0.80 while requiring 3-times fewer epochs for model training compared to the non-transfer approach, 2) can achieve an F1 score of 0.75 with only 230 data samples, and 3) our transfer approach outperforms the state-of-the-art in precision drop by up to 12 percentage points for unseen appliances.