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Forecasting electricity prices : a machine learning approach
ID
Castelli, Mauro
(
Author
),
ID
Groznik, Aleš
(
Author
),
ID
Popovič, Aleš
(
Author
)
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MD5: B0B117BACD79D5B361411E4D79745D37
URL - Source URL, Visit
https://www.mdpi.com/1999-4893/13/5/119
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Abstract
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique—namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements.
Language:
English
Keywords:
energetics
,
price
,
informatics
,
energy sector
,
electricity prices
,
forecasting
,
machine learning
,
geometric semantic
,
based programming
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
EF - School of Economics and Business
Publication status:
Published
Publication version:
Version of Record
Year:
2020
Number of pages:
16 str.
Numbering:
Vol. 13, iss. 5, art. 119
PID:
20.500.12556/RUL-116491
UDC:
659.2:004
ISSN on article:
1999-4893
DOI:
10.3390/a13050119
COBISS.SI-ID:
14509571
Publication date in RUL:
25.05.2020
Views:
1585
Downloads:
397
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Record is a part of a journal
Title:
Algorithms
Shortened title:
Algorithms
Publisher:
MDPI
ISSN:
1999-4893
COBISS.SI-ID:
517501977
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
08.05.2020
Secondary language
Language:
Slovenian
Keywords:
energetika
,
cena
,
informatika
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
J5-9329
Name:
Poslovna analitika in poslovni modeli v oskrbovalnih verigah
Funder:
FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:
DSAIPA/DS/0022/2018
Acronym:
GADgET
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