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    Wyświetlanie 1-11 z 11
    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:
    Quantum inspiration to build a neural model based on the Day-Ahead Market of the Polish Power Exchange
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2052430.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Neural Modeling
    day-ahead market
    Polish power exchange
    mean square error
    determination index
    quantum inspired neural model
    Opis:
    The article is an attempt of the methodological approach to the proposed quantum-inspired method of neural modeling of prices quoted on the Day-Ahead Market operating at TGE S.A. In the proposed quantum-inspired neural model it was assumed, inter alia, that it is composed of 12 parallel Perceptron ANNs with one hidden layer. Moreover, it was assumed that weights and biases as processing elements are described by density matrices, and the values flowing through the Artificial Neural Network of Signals are represented by qubits. Calculations checking the correctness of the adopted method and model were carried out with the use of linear algebra and vector-matrix calculus in MATLAB and Simulink environments. The obtained research results were compared to the results obtained from the neural model with the use of a comparative model.
    Źródło:
    Studia Informatica : systems and information technology; 2021, 1-2(25); 23-37
    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:
    The impact of the size of the training set on the predictive abilities of neural models on the example of the Day-Ahead Market System of TGE S.A.
    Autorzy:
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2175162.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    Day Ahead Market
    MATLAB environment
    Simulink environment
    neural modeling
    prediction time
    electricity prices
    Opis:
    The main object of the research was to examine the acceptable time horizon that could be predicted by previously learned models of the Day-Ahead Market (DAM) TGE S.A. system. The article contains the results of research on the predicting ability of different ANN models of the DAM TGE S.A. The research was conducted based on data covering the operation of the Polish stock exchange in the period from 2002 to 2019 (the first half of the year). The research was carried out based on the learned ANN models of the DAM system. Data were taken for examination covering the time from 2002 to 2019 (1st half of the year) and was divided into a different period, i.e., a month, a quarter, and a half-year., year, etc. The MSE, MAE, MAPE, and R2 were adopted as the criteria for assessing the ability of individual models to predict electricity prices. The research was carried out by successively expanding forecasting periods in a rolling manner. For example, for a half-year, prediction time intervals were increased from one week to month, two months, quarter, half-year, etc. results for a model representing a given period. A lot of interesting research results were obtained.
    Źródło:
    Studia Informatica : systems and information technology; 2022, 1(26); 5--22
    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:
    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
    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
    Tytuł:
    Model symulacyjny systemu Towarowej Giełdy Energii Elektrycznej z wykorzystaniem wspomaganej ewolucyjnie oraz inspirowanej kwantowo Sztucznej Sieci Neuronowej
    Simulation model of the Polish Power Exchange System using evolutionally assisted and quantum-inspired Artificial Neural Network
    Autorzy:
    Tchórzewski, Jerzy
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/376414.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    badania komparatystyczne
    badania symulacyjne
    obliczenia kwantowe
    sztuczne sieci neuronowe
    towarowa giełda energii elektrycznej
    Opis:
    Utworzono wspomaganą ewolucyjnie oraz inspirowaną kwantowo Sztuczną Sieć Neuronową, którą zaimplementowano w Simulinku na bazie danych Rynku Dnia Następnego Towarowej Giełdy Energii Elektrycznej. Dane wejściowe, wagi i biasy poddano kwantyzacji. Kwantowe obliczenia quasi-równoległe przeprowadzono na bazie 100 wygenerowanych kwantowych liczb mieszanych za pomocą metody kwantyzacji na bazie stanów czystych |0> i |1>, a uzyskane w wyniku obliczeń kwantowe liczby mieszane poddano dekwantyzacji za pomocą Sztucznej Sieci Neuronowej (SSN). Model symulacyjny składający się ze wspomaganej ewolucyjnie oraz kwantowo inspirowanej Sztucznej Sieci Neuronowej, oprócz badań symulacyjnych, umożliwia przeprowadzanie badań komparatystycznych uzyskiwanych sygnałów z danymi rzeczywistymi oraz z danymi wyjściowymi z perceptronowej Sztucznej Sieci Neuronowej. Wyniki badań wskazują na wysoką dokładność przeprowadzanego eksperymentu.
    An evolutionary-assisted and quantum-inspired Artificial Neural Network was created, which was implemented in Simulink on the Day-Ahead Market of the Polish Power Exchange. Input data, weights and bias were quantized. Quantum quasi-parallel calculations were carried out on the basis of 100 generated quantum mixed numbers using the quantization method based on pure states |0> and |1>, and the resulting quantum mixed numbers were dequantized using another Artificial Neural Network. The implemented simulation model consists of evolutionarily assisted and quantum-inspired Artificial Neural Network, which in addition to simulation studies allows conducting comparative studies of obtained signals with real data and with output data from the perceptron Artificial Neural Network. The test results indicate the high accuracy of the experiment.
    Źródło:
    Poznan University of Technology Academic Journals. Electrical Engineering; 2020, 104; 55-64
    1897-0737
    Pojawia się w:
    Poznan University of Technology Academic Journals. Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Quantum-inspired method of neural modeling of the day-ahead market of the Polish electricity exchange
    Autorzy:
    Tchórzewski, Jerzy
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/2183468.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    artificial neural networks
    day-ahead market
    dequantization with ANN
    neural modeling
    quantum inspired method
    quantum computing
    Polish Electricity Exchange
    system quantization
    Opis:
    The paper presents selected elements of a modelling methodology involving quantization, quantum calculations and dequantization on the example of the neural model of the Day-Ahead Market of the Polish Electricity Exchange. Based on the fundamental assumptions of quantum computing, a new method has been proposed here of converting the real numbers in decimal notation into quantum mixed numbers using the probability modules of quantum mixed number and the principle of superposition, along with a new method of quantum calculations using linear algebra and vectormatrix calculus, and the Artificial Neural Network was taught accordingly. Dequantization of quantum mixed numbers to real numbers in decimal notation using the new method of dequantization has been proposed as well. The operation of the methods introduced was shown on numerical examples.
    Źródło:
    Control and Cybernetics; 2021, 50, 3; 383--399
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Algorytm Ewolucyjny inspirowany informatyką kwantową do poprawy parametrów modelu neuralnego wyznaczania cen na Towarowej Giełdzie Energii Elektrycznej notowanych na RDN
    Evolutionary Algorithm inspired by quantum information technology to improve the parametrers of the neural piice setting model on the Polish Power Exchange traded on the DAM
    Autorzy:
    Tchórzewski, Jerzy
    Ruciński, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/377068.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    Algorytm Ewolucyjny
    dekwantyzacja
    kwantowa liczba mieszana
    kwantyzacja
    obliczenia kwantowe
    Rynek Dnia Następnego
    Sztuczna Sieć Neuronowa
    Towarowa Giełda Energii Elektrycznej
    Opis:
    Artykuł zawiera wybrane wyniki badań dotyczące istoty i implementacji Algorytmu Ewolucyjnego inspirowanego obliczeniami kwantowymi do poprawy parametrów modelu neuralnego wyznaczającego ceny na Towarowej Giełdzie Energii Elektrycznej. Do uczenia Sztucznej Sieci Neuronowej modelu systemu wykorzystano dane liczbowe notowane na Rynku Dnia Następnego w okresie od 01 stycznia 2015 r. do 30 czerwca 2015 r. Szczególną uwagę zwrócono na sposób systemowego tworzenie Populacji Początkowej oraz na sposób systemowego tworzenie funkcji krzepkości (funkcji przystosowania), a na tej bazie na metodę kwantyzacji, dekwantyzacji i obliczeń kwantowych przeprowadzonych z wykorzystaniem pojęcia kwantowej liczby mieszanej i rachunku wektorowo-macierzowego. Uzyskano znaczącą poprawę modelu neuralnego wspomaganego algorytmem ewolucyjnym inspirowanym kwantowo w stosunku do modelu neuralnego wspomaganego algorytmem ewolucyjnym bez inspiracji kwantowej.
    The paper contains selected research results on the nature and implementation of the Evolutionary Algorithm inspired by quantum computation to improve the parameters of the neural model determining prices at the Polish Power Exchange. To learn the Artificial Neural Network system model, the figures quoted on the Commodity Electricity Market of the Day-Ahead Market were used in the period from January 1, 2018 to June 30, 2018. Particular attention was paid to the systemic creation of the Initial Population and the systemic creation of the function of solidification (function adaptation), and on this basis, the quantization, dequantization and quantum computation methods carried out using the quantum concept of a mixed number. Significant improvement of the neural model supported by quantum-inspired evolutionary algorithm in relation to the model without quantum inspiration was obtained.
    Źródło:
    Poznan University of Technology Academic Journals. Electrical Engineering; 2019, 100; 121-132
    1897-0737
    Pojawia się w:
    Poznan University of Technology Academic Journals. Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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