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    Wyświetlanie 1-4 z 4
    Tytuł:
    Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052267.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Polish Power Exchange
    day ahead market
    DAM
    artificial neural network
    system modelling
    MATLAB
    Opis:
    The paper contains the results of research on the impact of the number of factors used to build the Day-Ahead Market model at Polish Power Exchange S.A. Five models with a different number of factors influencing the model were tested. To test the quality of models according to the adopted evaluation criteria, i.e., mean square error and the coefficient of determination for the weighted average prices sold in a given hour of the day, the influence of weather factors, socio-economic factors and energy demand were adopted. The results obtained from the analysis show a relatively high correctness of the simplest of the adopted models, which differs slightly from the best model.
    Źródło:
    Studia Informatica : systems and information technology; 2020, 1-2(24); 25-35
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    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
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/1819257.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Perceptron Artificial Neural Network
    Radial Artificial Neural Network
    Recursive Artificial Neural Network
    neural model quality
    Day-Ahead Market
    Polish Power Exchange
    Mean square error
    determination index
    Opis:
    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.
    Źródło:
    Studia Informatica : systems and information technology; 2019, 1-2(23); 77--93
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial Neural Network based on mathematical models used in quantum computing
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2201614.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    neural modeling
    Day-Ahead Market
    Polish Power Exchange
    Hilbert space
    quantum inspired neural network
    Opis:
    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.
    Źródło:
    Studia Informatica : systems and information technology; 2022, 2(27); 27--48
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural modeling of prices on the Day-Ahead Market at the Polish Power Exchange supported by an evolutionary algorithm and inspired by quantum computing
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/31342753.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    Polish Power Exchange
    Day Ahead Market
    modeling of energy market
    quantum inspired neural network
    Opis:
    The purpose of the work, presented in this article, was to obtain a price model for the Day-Ahead Market of the Polish Power Exchange (PPE). The resulting proposed models are based on Artificial Neural Networks (ANN), and the involved suggested improvement concerns the proper selection of both the type of network and the factors used in model construction. The article also proposes a new approach to the ANN with the implemented quantum learning model. The purpose of the research was to analyze factors, which exert influence on the quality of the model, like weather or economic factors, or the type of neural network used. The model determines the relationship between the price and the volume of electricity for a given hour of the day. The mean square error and the coefficient of determination were used to measure the quality of the obtained models. The results from the experiments performed indicate the possibility of developing improved models of the Day-Ahead Market.
    Źródło:
    Control and Cybernetics; 2022, 51, 4; 557-583
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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