Short-term spatio-temporal prediction of wind speed and direction

By Jethro Dowell, Stephan Weiss, David Hill, David Infield. 2014

How researchers see this paper

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

1. Amir Ghaderi
2. Borhan Molazem Sanandaji
3. Faezeh Ghaderi
4. Kameshwar Poolla
5. Jianhui Wang
6. Pravin Pratap Varaiya
7. A. Taşcıkaraoǧlu
8. Mahdi Khodayar

Citations in the corpus

• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “(Dowell et al., 2013) employed a multi-channel adaptive filter to predict the wind speed and direction by taking advantages of spatial correlations at numerous geographical sites.”
• Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “Dowell et al. [16] employed a multi-channel adaptive filter to predict the wind speed and direction by taking advantages of spatial correlations at numerous geographical sites.”
• Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, methodology): “A multichannel adaptive filter is used to forecast wind speed and direction using spatial correlations at multiple sites in [29].”
• Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, background): “[25] present an adaptive filter for wind speed and direction forecasting on the basis of spatial correlations at neighbor areas.”