Abstract
Online energy disaggregation is an advanced technology that can help both consumers and the utility to implement load components analysis, enhancing the reliability of demand-side management. However, the online system requires continuous access to personal electricity data to train an online machine learning model, which would infer personal information. In this paper, we introduce a privacy-preserving online energy disaggregation system, the online model and training data can be protected by adding Gaussian noise to the model during the training process.
| Original language | English |
|---|---|
| Title of host publication | The 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) |
| Publisher | IEEE |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-7281-7100-5 , 978-1-7281-7099-2 |
| ISBN (Print) | 978-1-7281-7101-2 |
| DOIs | |
| Publication status | Published - 10 Nov 2020 |
| Event | The 2020 IEEE PES Innovative Smart Grid Technologies Europe Conference - The Hague, Netherlands Duration: 25 Oct 2020 → … |
Conference
| Conference | The 2020 IEEE PES Innovative Smart Grid Technologies Europe Conference |
|---|---|
| Abbreviated title | ISGT-Europe |
| Country/Territory | Netherlands |
| City | The Hague |
| Period | 25/10/20 → … |