Long-term wind speed and power forecasting using local recurrent neural network models

By Thanasis G. Barbounis, J. Theocharis, Minas Alexiadis, Petros Dokopoulos. 2006

564 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. K. Bhaskar
2. Cláudio Monteiro
3. Vladimiro Miranda
4. Charles J. Geyer
5. Jianhui Wang
6. Audun Botterud
7. S. Singh
8. Guenter Conzelmann

In Computer Vision and Pattern Recognition

1. Robert P. Broadwater
2. Jaesung Jung
3. K. Bhaskar
4. S. Singh
5. Eklas Hossain
6. Mahdi Khodayar
7. Jianhui Wang

In Electrical and Electronic Engineering

1. Robert P. Broadwater
2. Jaesung Jung
3. Shuaixun Chen
4. Fuad Un-Noor
5. Cláudio Monteiro
6. Vladimiro Miranda
7. Jianhui Wang
8. Audun Botterud

Citations in the corpus

• AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network (2012, background): “[5] proposed recurrent neural networks (RNNs) to forecast wind speed and power up to 72 h ahead.”
• Wind power forecasting : state-of-the-art 2009. (2009, background): “Neural Networks [139], [144], [147], [153], [164], [165] Support Vector Machines [139], [144], [147] Regression Trees with Bagging [139] Random Forests [139], [147] Adaptive Neural Fuzzy System [142], [143] Mixture of Experts [144] Nearest Neighbor Search [144] Autoregressive with Exogenous Input (A”
• A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets (2011, methodology): “models at forecast horizons shorter than a few hours and has relevance as the simplest and the cheapest forecast method at those horizons [ 9 ]–[13].”
• Current status and future advances for wind speed and power forecasting (2014, methodology): “In [95], a method for long-term wind speed and power forecasting is proposed by using the NWPs provided at nearby sites of the wind park.”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “This forecasting is complex, because from controlling wind turbine to integrating wind power into energy system, time duration for prediction changes from milliseconds or seconds to minutes or hours [143], [144].”
• Sizing of Energy Storage for Microgrids (2012, background): “Consequently, extensive research has been directed toward the development of good and reliable wind power forecasts in recent years and many different forecasting approaches have been developed [11]–[16].”