Learning the probability distributions of day-ahead electricity prices

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11230/25:10510483

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning the probability distributions of day-ahead electricity prices

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning the probability distributions of day-ahead electricity prices

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA24-11555S" target="_blank" >GA24-11555S: Zkrocení tržních rizik vyplývajících z málo pravděpodobných událostí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Economics

ISSN

0140-9883

e-ISSN

1873-6181

Svazek periodika

152

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

108988

Kód UT WoS článku

001602908700002

EID výsledku v databázi Scopus

2-s2.0-105019109156