ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
arXiv:2401.05381 · doi:10.14778/3632093.3632115
Abstract
Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the important challenges these suppliers face is how to utilize these data to detect the presence/absence of different appliances in the customers' households. This valuable information can help them provide personalized offers and recommendations to help customers towards the energy transition. Appliance detection can be cast as a time series classification problem. However, the large amount of data combined with the long and variable length of the consumption series pose challenges when training a classifier. In this paper, we propose ADF, a framework that uses subsequences of a client consumption series to detect the presence/absence of appliances. We also introduce TransApp, a Transformer-based time series classifier that is first pretrained in a self-supervised way to enhance its performance on appliance detection tasks. We test our approach on two real datasets, including a publicly available one. The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection. This paper appeared in VLDB 2024.
10 pages, 7 figures. This paper appeared in VLDB 2024
References in corpus (17)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Deep Residual Learning for Image Recognition
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Deep learning for time series classification: a review
- Gaussian Error Linear Units (GELUs)
- InceptionTime: Finding AlexNet for Time Series Classification
- CoAtNet: Marrying Convolution and Attention for All Data Sizes
- SAITS: Self-Attention-based Imputation for Time Series
- FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
- Network In Network
- Transformers in Time Series: A Survey
- Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
- dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
- A Transformer-based Framework for Multivariate Time Series Representation Learning
- Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning