Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods

By Zhang Yang, Li Ce, Li Lian. 2017

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Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, methodology): “3 2 [1, 3, 6, 12, 24, 48] 3 [1, 2, 4, 8, 16, 32] 4 [1, 3, 6, 12, 24]”
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, methodology): “Year Reference Journal Application 2018 Deng et al. [80] Journal of Renewable and Sustainable Energy Short-term load forecasting in microgrids 2016 Dou et al. [81] Electric Power Components and Systems prediction of renewable energy loads in microgrids 2016 Peng et al. [82] Energies Electric load fo”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, methodology): “An even more common problem is the fact that new models are very rarely compared in terms of their computational requirements [19,20, 22,24,32,37,41,42,51,100,102,103,105–108 , 111 1.”