Learning the probability distributions of day-ahead electricity prices
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00640435" target="_blank" >RIV/67985556:_____/25:00640435 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11230/25:10510483
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0140988325008187?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0140988325008187?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eneco.2025.108988" target="_blank" >10.1016/j.eneco.2025.108988</a>
Alternative languages
Result language
angličtina
Original language name
Learning the probability distributions of day-ahead electricity prices
Original language description
We propose a novel machine learning approach for probabilistic forecasting of hourly day-ahead electricity prices. In contrast with the recent advances in data-rich probabilistic forecasting, which approximates distributions with few features (such as moments), our method is nonparametric and selects the distribution from all possible empirical distributions learned from the input data without the need for limiting assumptions. The model that we propose is a multioutput neural network that accounts for the temporal dynamics of the probabilities and controls for monotonicity using a penalty. Such a distributional neural network can precisely learn complex patterns from many relevant variables that affect electricity prices. We illustrate the capacity of the developed method on German hourly day-ahead electricity prices and predict their probability distribution via many variables, revealing new valuable information in the data.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
<a href="/en/project/GA24-11555S" target="_blank" >GA24-11555S: Taming the tail risks in financial markets with data-driven methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Economics
ISSN
0140-9883
e-ISSN
1873-6181
Volume of the periodical
152
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
108988
UT code for WoS article
001602908700002
EID of the result in the Scopus database
2-s2.0-105019109156