Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 4

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Day-Ahead Market

Sortuj według:

Ogranicz wyniki do:

Artificial Neural Network based on mathematical models used in quantum computing

100%

Ruciński D.

tom Vol. 2(27)

27--48

The article is a proposition of a new approach to building a neural model based on the system of Day-Ahead Market operating at TGE S.A. The reason for the proposed method is an attempt to find a better model for the DAM system. The proposed methodology is based on using mathematical models used in quantum computing. All calculations performed on learning the Artificial Neuron Network are based on operations described in Hilbert space. The main idea of calculations is to replace the data from the decimal system into the quantum state in Hilbert space and perform learning operations for a neural model of the DAM system in a special manner which relay on the teaching model for each position of the quantum register for all data. The obtained results were compared to the “classical” neural model with the use of a comparative model.

Hourly identification and simulation of the TGE S.A. Day-Ahead Market system

86%

Marlęga R. , Tchórzewski J. R.

tom Vol. 51, No. 4

523--555

The paper presents selected research results concerning the identification and simulation of the TGE S.A. Day-Ahead Market (DAM) system of the day for electricity delivered and sold, listed for the following hours: 5:01-6:00, 11:01-12:00, 17:01-18:00 and 23:01-24:00 in 2019, which were obtained in the MATLAB and Simulink environment using the System Identification Toolbox. As a result of identification, four respective discrete parametric arx models were obtained, which were then subject to quality assessment. Then, a simulation model was built in the Simulink environment, which was used for simulation tests and for assessing the sensitivity of the model created using the data from 2019 as the basis and the data from 2020 for verification. The obtained results confirm the correctness of both the performed discrete parametric identification and the possibility of testing the quality of the model and its sensitivity with the use of the DAM system model in the MATLAB and Simulink environment.

Krótkookresowe prognozy ceny energii elektrycznej na Rynku Dnia Następnego

86%

Popławski T. , Weżgowiec M.

tom Nr 5 (104)

10--14

Artykuł porusza problematykę prognozowania krótkookresowego ceny energii elektrycznej na Rynku Dnia Następnego. Badania przeprowadzone zostały na rzeczywistych danych, dla których skonstruowano szereg czasowy. Wykonano analizę statystyczną i przeprowadzono testy pozwalające na wybór odpowiedniej metody i modelu predykcyjnego. Zaproponowano kilka modeli prognostycznych dla przewidywania ceny w horyzoncie dobowym. Dokonano analizy oceny prezentowanych modeli.

Article raises the problem of short-term forecasting of the electricity price on the Day-Ahead-Market. Researches were conducted on real data, for which the time series was constructed. Statistical analysis and tests were performed which allow to choose the proper method and predictive model. It was proposed several forecasting models to predict price in the twenty-four hours horizon. It was performed analysis and evaluation used tor presented models.

The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company

72%

Ruciński D.

tom Vol. 1-2(23)

77--93

The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.