- Tytuł:
- Prediction capabilities of the LSTM and Perceptron models based on the Day-Ahead Market on the Polish Power Exchange S.A.
- Autorzy:
- Ruciński, Dariusz
- Powiązania:
- https://bibliotekanauki.pl/articles/27323577.pdf
- Data publikacji:
- 2023
- Wydawca:
- Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
- Tematy:
-
shallow networks
deep networks
Day-Ahead Market
MATLAB
Simulink environment
neural modeling
prediction time
electricity prices - Opis:
- The main purpose of the research was to examine the properties of models for two kinds of neural networks, a deep learning models in which the Long Short-Term Memory was chosen and shallow neural model in which the Perceptron Neural Network was chosen. The subject of the examination was the Day-Ahead Market system of PPE S.A. The article presents the learning results of both networks and the results of the predictive abilities of the models. The research was conducted based on data published on the Polish Stock Exchange for the 2018 year. The MATLAB environment was chosen as a tool for providing the examinations. The determination index (R2) and the mean square error (MSE) was adopted as the network evaluation criterion for the learning ability and for the prediction ability of both networks.
- Źródło:
-
Studia Informatica : systems and information technology; 2023, 1(28); 69--82
1731-2264 - Pojawia się w:
- Studia Informatica : systems and information technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł