On the importance of the long-term seasonal component in day-ahead electricity price forecasting

By Bartosz Uniejewski, Grzegorz Marcjasz, Rafal Weron. Energy Economics, 2016

92 citations recorded by the source corpus.

In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring - contrary to a common belief - an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/acd94fd7f310b61adbad20d38fbf736b32267878

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In Economics and Econometrics

In Electrical and Electronic Engineering

Citations in the corpus

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, background): “Most notably, models using socalled variance stabilizing transformations [9, 57, 70, 71] and long-term seasonal components [72–75] have been proposed and shown to result in statistically significant improvements.”