Multi‐step wind power forecast based on VMD‐LSTM
| Autor: | Rongchang Zhang, Xuesong Wang, Huitian Jing, Li Han, Achun Bao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Wind power
Artificial neural network Renewable Energy Sustainability and the Environment business.industry Computer science 020209 energy Load forecasting 020208 electrical & electronic engineering Recurrent neural nets 02 engineering and technology Error analysis 0202 electrical engineering electronic engineering information engineering Forecasting theory Variational mode decomposition business Algorithm Physics::Atmospheric and Oceanic Physics |
| Zdroj: | IET Renewable Power Generation. 13:1690-1700 |
| ISSN: | 1752-1424 1752-1416 |
| DOI: | 10.1049/iet-rpg.2018.5781 |
| Popis: | To improve the accuracy of multi-step wind power forecast, a variational mode decomposition-long short-term memory (VMD-LSTM) forecast method is proposed. Firstly, the variational mode decomposition method is adopted to decompose the wind power data into three constituent modes, named as the long-term component, the fluctuation component and the random component. Secondly, long short-term memory network is utilised to deeply learn the characteristics of the three constituent modes. Profit from its unique forget gate and memory gate structure, the association with long-term time series is learned to build a multi-step forecast model. Finally, the wind power data from ELIA and NERL are used to test. The error analysis shows that the proposed method has superior performance in the multi-step forecast and real-time forecast. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text