Name
Title | ||
|---|---|---|
Highly accurate energy consumption forecasting model based on parallel LSTM neural networks |
Abstract | ||
|---|---|---|
The main challenges of the energy consumption forecasting problem are the concerns for reliability, stability, efficiency and accuracy of the forecasting methods. The existing forecasting models suffer from the volatility of the energy consumption data. It is desired for AI models that predict irregular sudden changes and capture long-term dependencies in the data. In this study, a novel hybrid AI empowered forecasting model that combines singular spectrum analysis (SSA) and parallel long short term memory (PLSTM) neural networks is proposed. The decomposition with the SSA enhanced the performance of the PLSTM network. According to the experimental results, the proposed model outperforms the state-of-the-art models at different time intervals in terms of both prediction accuracy and computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.aei.2021.101442 | Advanced Engineering Informatics |
Keywords | DocType | Volume |
Long short term memory,Energy consumption,Time series data analysis,Forecasting,Singular spectrum analysis | Journal | 51 |
ISSN | Citations | PageRank |
1474-0346 | 1 | 0.36 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Jin | 1 | 6 | 4.24 |
| Fan Yang | 2 | 1 | 0.36 |
| Yuchang Mo | 3 | 1 | 0.36 |
| Yongkang Zeng | 4 | 2 | 0.70 |
| Xiaokang Zhou | 5 | 1 | 0.36 |
| Ke Yan | 6 | 3 | 1.74 |
| Xiang Ma | 7 | 1 | 0.36 |