Name
Title | ||
|---|---|---|
Collaborative deep learning framework on IoT data with bidirectional NLSTM neural networks for energy consumption forecasting |
Abstract | ||
|---|---|---|
•This work proposes a parallel and distributed deep learning framework for IoT data analytics.•The proposed method is implemented and deployed with a real-world problem of energy consumption forecasting.•Comprehensive comparative study has been conducted to show the outperformance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jpdc.2022.01.012 | Journal of Parallel and Distributed Computing |
Keywords | DocType | Volume |
Energy consumption,Time series forecasting,LSTM,Stationary wavelet transform | Journal | 163 |
ISSN | Citations | PageRank |
0743-7315 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Yan | 1 | 3 | 1.74 |
| Xiaokang Zhou | 2 | 1 | 0.69 |
| Jinjun Chen | 3 | 1 | 0.36 |