Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm

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A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines (2013, methodology): “It should be noted that the SSA-SVRmodel for the ISO New England data set built by using any of the tested feature selection algorithms performs better than the best reported model in [1], while only the ReliefF algorithm for the 24-hour North-American utility case performs worse than best reported ”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, background): “The Daubechies wavelet of order 4 (Db4) is proposed in the wind forecasting literature as the most suitable type of 190 mother wavelet, due to its ability of providing a balance between wavelength and smoothness [30].”
Short-term wind power forecasting in Portugal by neural networks and wavelet transform (2011, background): “The CWT ) , ( b a W of signal ) (x f with respect to a mother wavelet ) (x  is given by [21]:”