Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm

By Da Liu, D. Niu, Hui Wang, L. Fan. 2014

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Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…and thermal power consumption Monthly Liu et al. (2013a) GA, PSO DWT Wind speed Sampling Liu et al. (2013b) ANN DWT, Packet Wind speed Sampling Liu et al. (2014) SVM DWT Wind speed Sampling Evrendilek (2012) ANN DWT Heat fluxes, evapotranspiration Sampling Wang et al. (2013) ANN, ARIMA DWT…”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, methodology): “Liu et al. (2014) propose a hybrid model which combines inputs selected by deep quantitative analysis, wavelet transform, genetic algorithm and support vector machines.”
Tackling Climate Change with Machine Learning (2019, methodology): “[44] Da Liu, Dongxiao Niu, Hui Wang, and Leilei Fan.”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “[15] 55 also propose a model based on WT and SVM, in which the parameters of SVM are optimized using Genetic Algorithm (GA).”