Multi-step Ahead Wind Power Forecasting Based on Recurrent Neural Networks
| Autor: | Wei Hu, Rui Yu, Maolin Tang, Baisi Liu, Yiwei Fu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Wind power
business.industry Computer science 020209 energy Deep learning Wind power forecasting 02 engineering and technology Numerical weather prediction computer.software_genre Wind speed Electric power system Recurrent neural network 0202 electrical engineering electronic engineering information engineering Autoregressive integrated moving average Artificial intelligence Data mining business computer |
| Zdroj: | 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). |
| Popis: | The accuracy of wind power forecasting has a very important influence on the safe and stable operation of power system. However, wind power prediction is very difficult, especially under the environment of massive data. This paper presents a novel multi-step ahead wind power prediction model based on recurrent neural network (RNN) with long short-term memory (LSTM) unit or gated recurrent unit (GRU) to improve the accuracy. Firstly, an overall forecasting framework for wind power with diverse forms of optional hybrid models is proposed. Moreover, an innovative LSTM/GRU-based forecasting model is developed with a wind speed correction process using numerical weather prediction (NWP) data. Through the application of GRU network, the correction process can provide corrected wind speed at the predicted moment as an input of the forecasting model. Finally, the experimental results demonstrate the superiority of the proposed RNN approaches as compared to the ARIMA method and SVM method while the main distinctions between the LSTM and GRU network are also illustrated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|