A review of combined approaches for prediction of short-term wind speed and power

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Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, neutral): “For a more complete survey of wind speed forecasting methods see (Zhu & Genton, 2012) and (Tascikaraoglu & Uzunoglu, 2014), among others.”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, methodology): “The literature on contributed models for wind power and wind speed forecasts is vast, Jung and Broadwater (2014) as well as Tascikaraoglu and Uzunoglu (2014) provide an up-to-date overview.”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, methodology): “Moreover, Tascikaraoglu and Uzunoglu (2014) point out that they are not convenient for short-term forecasting and require too much time.”
Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “For a more complete survey of wind speed forecasting methods see [11] and [12] among others.”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “Among 45 various techniques, WT-based methods can be pointed out as the prevailing approach in wind forecasting due to its easy implementation and adaptive ability of time-frequency analysis [10].”