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A Hybrid System Based on LSTM for Short-Term Power Load Forecasting
| Content Provider | MDPI |
|---|---|
| Author | Jin, Yu Guo, Honggang Wang, Jianzhou Song, Aiyi |
| Copyright Year | 2020 |
| Description | As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential. |
| Starting Page | 6241 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en13236241 |
| Journal | Energies |
| Issue Number | 23 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-26 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Power Load Forecasting Hybrid Analysis-forecast System Data Preprocessing Strategy Deep Learning Structure Optimization Algorithm |
| Content Type | Text |
| Resource Type | Article |
