Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks

By Erasmo Cadenas, Wilfrido Rivera. 2009

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0fc5064d322e87a4bf3d3dbe9113d2c43731cfd6

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. Cláudio Monteiro
2. Vladimiro Miranda
3. Okyay Kaynak
4. Jianhui Wang
5. Jianhui Wang
6. Audun Botterud
7. Guenter Conzelmann
8. Ricardo J. Bessa

In Electrical and Electronic Engineering

1. Jianhui Wang
2. Mahdi Khodayar
3. Cláudio Monteiro
4. Vladimiro Miranda
5. Okyay Kaynak
6. Jianhui Wang
7. Audun Botterud
8. Guenter Conzelmann

Citations in the corpus

• Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, methodology): “Most recently proposed ANN-based approaches design and learn shallow neural architectures with few levels of abstraction, such as Feed-forward ANN [17][18], recurrent ANN [19], RBF network [20]-[21], ridgelet ANN [22] and adaptive wavelet ANN [23].”
• Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, unknown): “(2007), its recent generalization, GWPPT, by Croonenbroeck and Dahl (2014), or the spatial GWPPT by Croonenbroeck and Ambach (2015).”
• Wind power forecasting : state-of-the-art 2009. (2009, methodology): “Cadenas and Rivera [108] tested four NN configurations to hourly wind speed forecast.”
• Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting (2017, methodology): “An AE is an ANN with a hidden layer designed to learn a compressed distributed representation for a set of data.”