Probabilistic mid- and long-term electricity price forecasting

By Florian Ziel, R. Steinert. Renewable & Sustainable Energy Reviews, 2017

153 citations recorded by the source corpus.

The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0712aed3cb8e602023309a780220a02cdde65df7

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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): “The more recent reviews either focused on short-term [6] and medium-/long-term [7] probabilistic EPF, were not that comprehensive in scope [62,63], or concerned electricity derivatives [61].”
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2022, background): “Notable examples include power plant maintenance scheduling (Hyn-dman and Fan 2009) and planning for infrastructure construction (Ziel and Steinert 2018), as well as early warning systems that help mitigate vulnerabilities due to extreme weather events (Basher 2006; Field et al. 2012).”