A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market
The paper contains selected results of research related to the nature and the implementation of the neural model supported by the evolutionary algorithm inspired by quantum calculations for determination of prices at the Polish Power Exchange. Numeric data quoted at the Day Ahead Market in the perio...
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EDP Sciences
2019-01-01
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Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2019/05/itmconf_zkwe2019_01006.pdf |
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doaj-9bc43dd761c449b1b5e4bfcd2e8c8f882021-02-02T08:15:42ZengEDP SciencesITM Web of Conferences2271-20972019-01-01280100610.1051/itmconf/20192801006itmconf_zkwe2019_01006A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead MarketTchórzewski JerzyRuciński DariuszThe paper contains selected results of research related to the nature and the implementation of the neural model supported by the evolutionary algorithm inspired by quantum calculations for determination of prices at the Polish Power Exchange. Numeric data quoted at the Day Ahead Market in the period of 1st January 2015 to 30th June 2015 were used to train the artificial neural network in the model of the system. Attention was paid to quantization method, dequantization method and the method of quantum calculations. Significant improvement of the neural model supported by the quantum-inspired evolutionary algorithm was obtained compared with the model without quantum inspiration.https://www.itm-conferences.org/articles/itmconf/pdf/2019/05/itmconf_zkwe2019_01006.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tchórzewski Jerzy Ruciński Dariusz |
spellingShingle |
Tchórzewski Jerzy Ruciński Dariusz A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market ITM Web of Conferences |
author_facet |
Tchórzewski Jerzy Ruciński Dariusz |
author_sort |
Tchórzewski Jerzy |
title |
A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market |
title_short |
A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market |
title_full |
A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market |
title_fullStr |
A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market |
title_full_unstemmed |
A neural model supported by the Evolutionary Algorithm inspired by quantum calculations to determine prices at the Polish Power Exchange Day Ahead Market |
title_sort |
neural model supported by the evolutionary algorithm inspired by quantum calculations to determine prices at the polish power exchange day ahead market |
publisher |
EDP Sciences |
series |
ITM Web of Conferences |
issn |
2271-2097 |
publishDate |
2019-01-01 |
description |
The paper contains selected results of research related to the nature and the implementation of the neural model supported by the evolutionary algorithm inspired by quantum calculations for determination of prices at the Polish Power Exchange. Numeric data quoted at the Day Ahead Market in the period of 1st January 2015 to 30th June 2015 were used to train the artificial neural network in the model of the system. Attention was paid to quantization method, dequantization method and the method of quantum calculations. Significant improvement of the neural model supported by the quantum-inspired evolutionary algorithm was obtained compared with the model without quantum inspiration. |
url |
https://www.itm-conferences.org/articles/itmconf/pdf/2019/05/itmconf_zkwe2019_01006.pdf |
work_keys_str_mv |
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