An empirical comparison of alternate schemes for combining electricity spot price forecasts

By Jakub Nowotarski, Eran Raviv, Stefan Trück, Rafal Weron. 2013

144 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/fe7a2754e785ce392e18380738fa7a8b782c4ccd

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In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Bayesian forecasting in economics and finance: A modern review (2022, methodology): “Key operational decisions in electricity forecasting depend on quantities other than the predicted mean; see Nowotarski and Weron (2018) and references therein for discussion.”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, methodology): “measure used in [10], [38] for comparing models.”
On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, methodology): “, 2013; Lisi and Nan, 2014; Nowotarski et al., 2013; Weron and Zator, 2014, among others), we consider two well-performing model classes – wavelet smoothing and the Hodrick-Prescott (HP) filter. Recall, that any function or signal (here: the electricity log-price series, pt) can be built up as a seq”
Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, background): “” The forecasting literature suggests that as long as we are dealing with point forecasts, a simple average yields satisfactory results [27].”
The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts (2018, methodology): “used in Kiesel & Paraschiv (2017)) or wind power reported by the EEX transparency platform (see Pape et al. (2016); Aïd et al.”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, background): “…series: where 𝜀 Z 𝑑 is the 24-dimensional vector of prediction errors of model Z for day 𝑑 , ‖ Z 𝑑 ‖ 𝑝 = ( The univariate version of the test has the advantage of providing a deeper analysis as it indicates which forecast is significantly better for which hour of the day [6,55,57,65,133,134].”