Short-term wind power forecasting in Portugal by neural networks and wavelet transform

By João P. S. Catalão, Hugo M. I. Pousinho, Victor M. F. Mendes. 2011

306 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/8243535c6475df9876eaf8dc4494439793bf7f45

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Qinghua Hu
2. Shiguang Zhang
3. Pravin Pratap Varaiya
4. Kameshwar Poolla
5. Man Yu
6. A. Taşcıkaraoǧlu
7. Robert P. Broadwater
8. Borhan Molazem Sanandaji

In Electronic, Optical and Magnetic Materials

1. Robert P. Broadwater
2. Jaesung Jung

Citations in the corpus

• Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “For detailed explanations about the method and parameter selection, please see [12, 14].”
• Current status and future advances for wind speed and power forecasting (2014, methodology): “Several transformation techniques are used: wavelet decomposition [41, 42, 54, 55, 62], empirical mode”
• Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, methodology): “It could be modeled by various methods, including the persistence method, autoregressive models [13], [14], artificial neural networks (ANNs) [15], SVR machines [16]–[18], and the hybrid method [19].”
• An improved wavelet–ARIMA approach for forecasting metal prices (2014, unknown): “Much research has also concentrated on the combination of artificial neural networks with wavelets (e.g. Amina et al., 2012; Amjady and Keynia, 2008; Aussem and Murtagh, 1997; Bashir and El-Hawary, 2009; Catalão et al., 2011; Chen et al., 2010; Jammazi and Aloui, 2012).”