Work detail

Forecasting Electricity Pricing in Central and Eastern Europe

Author: Mgr. Kristýna Křížová
Year: 2021 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 117
Awards and prizes:
Link: https://ckis.cuni.cz:443/F/?func=direct&doc_number=002449022&local_base=CKS01&format=999
Abstract: Within forecasting electricity pricing, we analyse whether adding various variables improves the predictions, and if shorter time intervals between observations enhance accuracy of the forecasting. Next, we focus on proper selection of
lagged observations, which has not been thoroughly covered in the past literature. In addition, many papers studied electricity prices in larger markets (e.g.
United States, Australia, Nord Pool, etc.) on datasets limited in scope, with
2-3 years timespan. To address these gaps in literature, we obtain one daily
and one hourly dataset, both spanning 6 years (January 1, 2015 – December 31,
2020), from four Central and Eastern European countries – the Czech Republic, the Slovak Republic, Hungary, and Romania. These contain information
on the electricity prices, and information on our observed added variables –
temperature and cross-border electricity flows. For the forecasting, we use two
different methods – Autoregression (AR) and Seemingly Unrelated Regression
(SUR). The thorough selection of lagged observations, which we accustom to
the closing time of the auction-based electricity market system, serves further
studies as a guidance on how to avoid possible errors and inconsistencies in
their predictions. In our analyses, both AR and SUR models show that hourly
data offer more precise results and that the temperature, in general, improves
the forecast accuracy more than the cross-border flows

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