Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

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Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, background): “Regime-switching models based on wind direction are studied by (Tastu et al., 2011) where they consider various statistical models, such as ARX models, to understand the effects of different variables on forecast error characteristics.”
Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “We consider a spatio-temporal model based on NN [40] and also an M-AR approach using Least Squares (LS) [22].”
Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System (2017, background): “The correlation between the forecasting error and the forecasted wind power has also been investigated [21].”
Current status and future advances for wind speed and power forecasting (2014, background): “The inclusion of wind direction with wind speed can reduce the forecasting error [96, 116, 118, 126].”
Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “We also employed a Least Squares (LS) M-AR spatio-temporal forecasting approach [18].”