Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

“…Like in [12,18,[25][26][27]30], the modeling is implemented separately across the hours, leading to 24 sets of parameters for each day the forecasting exercise is performed. As Ziel [18] notes, when we compare the forecasting performance of relatively simple models implemented separately across the hours and jointly for all hours (like in [9,[34][35][36]), the latter generally performs better for the first half of the day, whereas the former are better in the second half of the day.…”

“…• nine variants of three parsimonious autoregressive model structures with exogenous variables (ARX): one originally proposed by Misiorek et al [19] and later used in a number of EPF studies [13,18,[20][21][22][23][24][25][26][27], one which evolved from it during the successful participation of TEAM POLAND in the Global Energy Forecasting Competition 2014 (GEFCom2014; see [28][29][30]) and an extension of the former, which creates a stronger link with yesterday's prices and additionally considers a second exogenous variable (zonal load or wind power), • three two-year long, hourly resolution test periods from three distinct power markets (GEFCom2014, Nord Pool and the U.K.), • nine variants of five classes of selection and shrinkage procedures: single-step elimination of insignificant predictors (without or with constraints), stepwise regression (with forward selection or backward elimination), ridge regression, lasso and three elastic nets (with α = 0.25, 0.5 or 0.75), • model validation in terms of the robust weekly-weighted mean absolute error (WMAE; see [1]) and the Diebold-Mariano (DM; see [31]) test and draw statistically-significant conclusions of high practical value. The remainder of the paper is structured as follows.…”

Forecasting medium-term electricity demand in a South African electric power supply system

“…Some of these r packages are 'forecastHybrid' (Shaub and Ellis, 2017) and 'opera' (Gaillard and Goude, 2016). The 'opera' r package was used in this study.…”

“…Literature focuses mainly on forecasting short-term electricity demand (Gaillard et al, 2016;Fasiolo et al, 2017). Additive quantile regression models for forecasting both short-term probabilistic load and electricity prices for the Global Energy Forecasting Competition of 2014 (GEFCom2014) were discussed in Gaillard et al (2016). The proposed new methodology of Gaillard et al (2016) ranked first in both tracks of the competition.…”

“…Additive quantile regression models for forecasting both short-term probabilistic load and electricity prices for the Global Energy Forecasting Competition of 2014 (GEFCom2014) were discussed in Gaillard et al (2016). The proposed new methodology of Gaillard et al (2016) ranked first in both tracks of the competition. This methodology was extended by Fasiolo et al (2017), where the development of fast calibrated additive quantile regression models for short-term load forecasting was done.…”