Name
Title | ||
|---|---|---|
Poster Abstract: Residential Energy Management System Using Personalized Federated Deep Reinforcement learning |
Abstract | ||
|---|---|---|
The trend of Internet of Things is bringing in millions of new smart devices into homes to increase the quality of human life. However, this enormous number of new devices have also brings in an increasing energy consumption in standby for awaiting wire-less communication or status change. To reduce standby energy, existing approaches use real-time consumption data and machine learning techniques to identify standby energy, but aggregate data or intermediate model training updates in the cloud to collaboratively perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost. On the other hand, such a global collaborative model yields unsatisfactory en-ergy management performance as they fail to capture the diversity of each residence. In this paper, we propose a privacy-preserved, communication-efficient, personalized and cloud-service-free resi-dential energy management system (EMS) with personalized feder-ated deep reinforcement learning (PFDRL) framework to tackle the standby energy reduction in residential building. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/IPSN54338.2022.00071 | 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) |
Keywords | DocType | ISBN |
residential energy management system,personalized federated deep reinforcement learning,smart devices,human life,enormous number,increasing energy consumption,wire-less communication,real-time consumption data,aggregate data,intermediate model training updates,personal data leakage,significant communication bandwidth,extra cloud service monetary cost,global collaborative model yields,en-ergy management performance,communication-efficient, personalized,cloud-service-free,deep reinforcement learning framework,standby energy reduction | Conference | 978-1-6654-9625-4 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiechao Gao | 1 | 17 | 3.89 |
| Wenpeng Wang | 2 | 1 | 1.42 |
| Bradford Campbell | 3 | 3 | 4.21 |