| bibtype |
J -
Journal Article
|
| ARLID |
0640435 |
| utime |
20251107114426.1 |
| mtime |
20251025235959.9 |
| SCOPUS |
105019109156 |
| WOS |
001602908700002 |
| DOI |
10.1016/j.eneco.2025.108988 |
| title
(primary) (eng) |
Learning the probability distributions of day-ahead electricity prices |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0250426 |
| ISSN |
0140-9883 |
| title
|
Energy Economics |
| volume_id |
152 |
| publisher |
|
|
| keyword |
Distributional forecasting |
| keyword |
Deep learning |
| keyword |
Probabilistic |
| keyword |
Electricity |
| keyword |
Energy time series |
| author
(primary) |
| ARLID |
cav_un_auth*0462008 |
| name1 |
Hanus |
| name2 |
Luboš |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0242028 |
| name1 |
Baruník |
| name2 |
Jozef |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-11555S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0472836 |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
AH |
| FORD0 |
50000 |
| FORD1 |
50200 |
| FORD2 |
50202 |
| reportyear |
2026 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0371248 |
| confidential |
S |
| article_num |
108988 |
| mrcbC91 |
C |
| mrcbT16-e |
ECONOMICS |
| mrcbT16-f |
13.2 |
| mrcbT16-g |
2.6 |
| mrcbT16-h |
3.8 |
| mrcbT16-i |
0.04641 |
| mrcbT16-j |
2.28 |
| mrcbT16-k |
53243 |
| mrcbT16-q |
230 |
| mrcbT16-s |
3.919 |
| mrcbT16-y |
64.57 |
| mrcbT16-x |
15.43 |
| mrcbT16-3 |
27784 |
| mrcbT16-4 |
Q1 |
| mrcbT16-5 |
12.300 |
| mrcbT16-6 |
747 |
| mrcbT16-7 |
Q1 |
| mrcbT16-C |
99.9 |
| mrcbT16-M |
4.59 |
| mrcbT16-N |
Q1 |
| mrcbT16-P |
99.9 |
| arlyear |
2025 |
| mrcbU14 |
105019109156 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001602908700002 WOS |
| mrcbU63 |
cav_un_epca*0250426 Energy Economics 152 1 2025 0140-9883 1873-6181 Elsevier |
|