Comparative Study of the Identification Methods of the Management System of the Day-Ahead Market of Polish Energy Market S.A.

• Autorzy: Marlęga Radosław

Identyfikatory

DOI

10.34739/si.2021.25.04

• Języki publikacji: EN

Abstrakty

EN

Nowadays, identification and neural methods are used more and more often in modeling IT forecasting systems in addition to analytical methods. Six characteristic models used to forecast the Day- Ahead Market system functioning as a transaction management system at the Polish Power Exchange (POLPX) and the Nord Pool Spot market have been selected for comparative analysis. The research was preceded by a detailed discussion of modern criteria used to assess the quality of model fitting to the system, namely: effectiveness, efficiency, and robustness. In the literature, there are two main groups of system modeling methods, namely time series modeling methods and identification modeling methods, including neural modeling methods. Modeling usually results in such models as parametric models and artificial neural networks learned neural models of the Day-Ahead Market, as well as time series models, among others. In the comparative analysis, special attention was paid to the accuracy of the obtained models concerning the system. It has been pointed out that the studied solutions used to measure the accuracy of modeling criteria such as accuracy of fit or efficiency, and did not use the modeling efficiency, which is very important in IT forecasting systems for such large markets as the Day-Ahead Market of POLPX. The search for the best market models, including identification models of the Day- Ahead Market operation that can be used in electricity price forecasting is a very important issue both from the point of view of algorithmic solutions and economical solutions.

Słowa kluczowe

EN

artificial neural network; business intelligence; day ahead market; Identification methods; information system in management; parametrical model; Polish Energy Market

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2021
• Tom: Vol. 1-2(25)
• Strony: 67--86
• Opis fizyczny: Bibliogr. 31 poz., tab., wykr.

Twórcy

autor

Marlęga Radosław

• PhD Student at Computer Science Institute, Siedlce University of Natural Sciences and Humanities, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland

Bibliografia

• 1. Aggarwal S.K., Saini L.M., Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation, International Journal of Electrical Power & Energy Systems, No. 31/2009, pp. 13-22.
• 2. Barczak A., Barczak M.: Projektowanie i implementacja bazy dokumentów (English: Design and implementation of a document database), Wydawnictwo Uczelniane, UPH, Siedlce 2020, pp. 126
• 3. Box G. E. P., Jenkins G. M.: Analiza szeregów czasowych. Prognozowanie i sterowanie (English: Time series analysis. Forecasting and control). PWN, Warszawa 1983.
• 4. Conejo A. J., Plazas M. A. , [et all]: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transaction on Power System, No. 20(2)/2005, pp.1035-1042.
• 5. Dash S.K., Dash K.P.: Short-term mixed electricity demand and price forecasting using adaptive autoregressive moving average and functional link neural network, Journal of Modern Power Systems and Clean Energy, No. 7(5)/2019, pp. 1241-1255.
• 6. Ejdys, Halicka K., Godlewska J.: Prognozowanie cen energii elektrycznej na giełdzie energii (English: Forecasting electricity prices on the power exchange). Zeszyty Naukowe Politechniki Śląskiej, Seria: Organizacja i Zarządzanie, Z. 77, Nr 1927, ss. 1-10.
• 7. Halicka K.: Skuteczność prognozowania w zarządzaniu transakcjami na giełdzie energii (English: Effectiveness of forecasting in managing transactions on the power exchange). Rozprawa doktorska pod kierunkiem (English: doctoral dissertation under the supervision of) prof. dr hab. inż. Joanicjusza Nazarko, Wydział Zarządzania UW, Warszawa 2007.
• 8. Jiang L.L., Hu G.: Day-Ahead Price Forcasting for Electricity Market using Long-Short Term Memory Recurent Neural Network. 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapure, Nov. 19-21, IEEE Digital Library, pp. 949-954.
• 9. Kowal W.: Skuteczność i efektywność - zróżnicowane aspekty interpretacji (English: Effectiveness and efficiency - various aspects of interpretation). Kwartalnik Naukowy pt. Organizacja i Kierowanie. Nr 4(157), SGH, Warszawa 2013.
• 10. Labib N., Wadid E.: Comparative study of Intelligent Systems for Management of GIT Cancers, MATEC Web of Conferences 125, 02063 (2017, CSCC 2017, pp. 1-6, 2017.
• 11. Mandal P., Senyju T. [et all]: A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method, IEEE Transactions on Power Systems, 22(4)/2007, pp. 2058 - 2065.
• 12. Marlęga R.: State-space model and implementation Polish Power Exchange in MATLAB and Simulnk environments. Information Systems in Management. Vol. 6, No. 4/2017, pp. 294-308.
• 13. Marlęga R., Tchórzewski J.: Identification modeling of Polish electric power exchange, Information Systems in Management, No. 2, Vol. 5/2016, pp. 195-204.
• 14. Mandal P., Haque A.U., Meng J., Srivastava A.K., Martinez R.: A Novel Hybrid Approach Using Wavelet, Firefly Algorithm, and Fuzzy ARTMAP for Day-Ahead Electricity Price Forecasting, IEEE Transactions on Power Systems, Vol. 28. No. 2, pp. 1041-1051, May 2013.
• 15. Merayo D., Rodriguez-Prieto A., Camacho A.M.: Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.0, Procedia Manufacturing No. 41/2019, pp. 42-49.
• 16. Moghaddam R.K., Yazdan N. M.: A Comparative Analisis of Artificial Intelligence - Based Methods for Fault Diagnosis of Mechanical Systems, Mechanics and Mechanical Engineering, No. 23/2019, Sciendo, pp. 113-124.
• 17. Mynarski S.: Modelowanie rynku w ujęciu systemowym (English: System modeling of the market). PWN, Warszawa 1982, stron182.
• 18. Popławski T., Weżgowiec M.: Krótkoterminowe prognozy cen na Towarowej Giełdzie Energii z wykorzystaniem modelu trendu pełzającego (English: Short-term price forecasts on the Polish Power Exchange using the crawling trend model). Przegląd Elektrotechniczny, R. 91, Nr 12/2015, ss. 267-270.
• 19. Ruciński D.: 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. Studia Informatica. Systems and Information Technology. No. 1-2 (23)2019, pp. 77-93.
• 20. Ruciński D.: Neural modeling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors. Studia Informatica. Systems and Information Technology. No. 1-2 (24)2020, pp. 25-36.
• 21. Staniszewski R.: Sterowanie procesem eksploatacji (English: Control of the operation process). WNT, Warszawa 1988, stron 475.
• 22. Tchórzewski J.: Cybernetyka życia i rozwoju systemów (English: Cybernetics of life and systems development). WSRP, Siedlce 1992, stron 408.
• 23. Tchórzewski J.: Rozwój system elektroenergetycznego w ujęciu teorii sterowania i systemów (English: Development of the power system in terms of control theory and systems). OW PWr, Wrocław 2013, stron 190.
• 24. Tchórzewski J.: Metody sztucznej inteligencji i informatyki kwantowej w ujęciu teorii sterowania i systemów (English: Methods of artificial intelligence and quantum computing in terms of control theory and system). Wydawnictwo Naukowe UPH, Siedlce 2021, stron 343.
• 25. Tchórzewski J. and Marlęga R.: The Day-Ahead Market System Simulation Model in the MATLAB and Simulink Environment, 2021 Progress in Applied Electrical Engineering (PAEE), 2021, pp. 1-8.
• 26. Tchórzewski J., Marlęga R.: Modeling and simulation of the control- and the systems-inspired of the Polish Electricity Exchange, 2017 Progress in Applied Electrical Engineering (PAEE), Kościelisko, IEEE Digital Library, pp. 1-6, 2017.
• 27. Tchórzewski J., Marlęga R., The Management System of the Polish Electricity Exchange from the Viewpoint of the Control and Systems Theory,16th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, IEEE Xplore Digital Library, 2019, pp. 1-5.
• 28. Tchórzewski J., Ruciński D.: Modeling and simulation inspired by quantum methods of the Polish Electricity Stock Exchange, Progress in Applied Electrical Engineering, Kościelisko, 2017, pp. 1-6.
• 29. Trusz M., Tserakh U.: GARCH(1,1) models with stable residuals. Studia Informatica. Systems and Information Technology. No. 1-2(22)2017, pp. 47-57.
• 30. Voronin S.: Price spike forecasting in a competitive day-ahead energy market. Acta Universitatis, Lappeenrantaensis 530, pp. 9-177.
• 31. www.tge.pl - the website of Towarowa Giełda Energii S.A. [access: 2016-2021