PRobabilistic mid- and long-term prIce fORecasting In electriciTY markets (PRIORITY)
Grant no.: DFG-NCN 2021/43/I/HS4/02578
Funding agencies: German Research Foundation (DFG), Germany & National Science Centre (NCN), Poland
Funding scheme: OPUS LAP
Funding period: 1.09.2023-31.08.2026 (36 months)
Budget: 922 200 PLN + 416 000 EUR (ca. 2 807 300 PLN in total)
Title in Polish: Probabilistyczne prognozowanie cen na rynkach energii elektrycznej w horyzoncie średnio- i długoterminowym
Research team:
Principal Investigators (Kierownicy):
- Rafał Weron (Wrocław, PL)
- Florian Ziel (Essen, D)
Investigators (Wykonawcy):
- … (Essen, D)
- … (Essen, D)
- … (Wrocław, PL)
- … (Wrocław, PL)
Collaborators (Współpracownicy):
- Katarzyna Maciejowska (PWr, Wrocław, PL)
- Stefan Trück (MQ, Sydney, AUS)
- Bartosz Uniejewski (PWr, Wrocław, PL)
– Ph.D. / M.Sc. / B.Sc. student
Aims and scope:
Climate change is urging fundamental transformations in almost all sectors of the economy. To reach the EU target of climate neutrality by 2050, the electricity system needs huge investments in new storage capacities, renewable energy generation, demand response systems and transmission grid infrastructure. However, such investments require a rigorous (in statistical terms: reliable and sharp) quantification of the uncertain future, especially of electricity prices for horizons ranging from months to years. Due to sector coupling, e.g., via electric vehicles and power-to-heat systems, the role of mid- and long-term probabilistic – or distributional – electricity price forecasting (EPF) will become even more important in the coming decades. Yet, the relevant literature is very scarce. Only 8% of EPF studies concern horizons beyond the day-ahead and less than 1% provide the so much needed midand long-term distributional forecasts.
It is exactly the aim of the PRIORITY project to address these challenges and develop methods and computational tools for PRobabilistic mid- and long-term prIce fORecasting In electriciTY markets. The project offers an integrated approach composed of four interrelated and performed in parallel work packages (WPs) and consisting of two complementary tasks each.
Tasks:
- WP1: Machine learning forecasting models
- WP1.1: Developing probabilistic deep neural networks and testing their applicability for mid- and long-term electricity price forecasting
- WP1.2: Extending gradient boosting machines to cope with distributional electricity price forecasting beyond the day-ahead
- WP2: Statistical and econometric forecasting models
- WP2.1: Utilizing regularization to cope with high-dimensional models for probabilistic electricity price forecasting beyond the day-ahead
- WP2.2: Constructing mid- and long-term regime-switching models with interpretable market regimes
- WP3: Fundamental energy market forecasting models
- WP3.1: Developing mid- and long-term fundamental power market models with stochastic inputs
- WP3.2: Developing functional time series models for probabilistic forecasting of the supply and demand curves
- WP4: Combination, reconciling and disaggregation
- WP4.1: Developing probabilistic combination schemes for models with large temporal delays
- WP4.2: Developing reconciling and disaggregation methods for mid- to longterm forecasting horizons
Publications:
Peer-reviewed articles in JCR-listed journals
2023 (0+)
…
Peer-reviewed articles in non JCR-listed journals
- …
Edited volumes / special issues
- …
Book chapters
- …
Conference papers
- …
Forthcoming publications, submitted papers and work in progress
- …
