# Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS

arXiv: https://arxiv.org/abs/2204.02802 · HTML: https://mlsanity.com/paper/2204.02802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Transfer Learning for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1902.08835/) (2019)
- [Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies](https://mlsanity.com/paper/2104.00972/) (2021)
- [Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models](https://mlsanity.com/paper/1806.04189/) (2018)
- [Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?](https://mlsanity.com/paper/2201.04449/) (2022)

## Abstract (arXiv)

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.