Neural modelling of electricity prices quoted on the Day-Ahead Market of TGE S.A. shaped by environmental and economic factors

• Autorzy: Ruciński Dariusz

Identyfikatory

DOI

10.34739/si.2020.24.02

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Polish Power Exchange; day ahead market; DAM; artificial neural network; system modelling; MATLAB

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2020
• Tom: Vol. 1-2(24)
• Strony: 25--35
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Ruciński Dariusz

• 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. Amjady N., (2006), Day-ahead price forecasting of electricity markets by a new fuzzy neural network, IEEE Transactions on Power Systems, Vol. 21, No. 2, pp. 887-896.
• 2. Anderson, T. W. (2003), An Introduction to Multivariate Statistical Analysis, Wiley, New York.
• 3. Bai J., Ng S., (2002), Determining the Number of Factors in Approximate Factor Models Econometrica, Vol. 70, No. 1, pp. 191–221.
• 4. Beale M. H., [et al.] (1992-2020), Neural Network Toolbox™ User's Guide, by The MathWorks, Inc.
• 5. Conejo A. J., Plazas M. A., et all, (2005), Day-ahead electricity price forecasting using the wavelet transform and ARIMA models, IEEE Transactions on Power Systems, Vol. 20, No. 2, pp. 1035-1042, doi: 10.1109/TPWRS.2005.846054.
• 6. Contreras J., Espínola R. [et al.] (2003), ARIMA models to predict next-day electricity prices, IEEE Transactions on Power Systems, Vol. 18(3), pp. 1014-1020.
• 7. Kowalik K., Klimecka-Tatar D., (2018), The process approach to service quality management. Production Engineering Archives, 18, pp. 31-34.
• 8. Li G., Liu C., et all, (2007), Day-Ahead Electricity Price Forecasting in a Grid Environment, IEEE Transactions on Power Systems, Vol. 22, No. 1, pp. 266-274, doi: 10.1109/TPWRS.2006.887893.
• 9. Maciejowska, K., Weron, R. (2015), Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships. Comput Stat 30, pp. 805-819. https://doi.org/10.1007/s00180-014-0531-0.
• 10. Mahler V., Girard R, et all. (2019), Simulation of day-ahead electricity market prices using a statistically calibrated structural model. 16th European Energy Market Conference, IEEE PES, Sep 2019, Ljubljana, Slovenia.
• 11. Marszałek A., Burczyński T. (2021), Journal Smart Environ Green Computing 2021.
• 12. McHugh C., Coleman S., et all, (2019), Forecasting Day-ahead Electricity Prices with a SARIMAX Model, 2019 IEEE Symposium Series on Computational Intelligence, pp. 1523-1529.
• 13. Mingming G., Jianjing Let all, (2019), Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM, Energy, Vol. 187.
• 14. Ruciński D., Tchórzewski J. (2016). Neural modeling of the electric power stock market in usage of MATLAB and Simulink tools for the day ahead market data, Information System in Management, Vol. 5 (2), pp. 215-226.
• 15. Ruciński D., (2019), The Influence of the Artificial Neural Network Type on the Quality of Learning on the Day-Ahead Market Model at Polish Electricity Exchange Join-Stock Company, Studia Informatica. System and Information Technology, Vol. 1-2(23), pp. 77-93.
• 16. Rusiecki, A., L., (2006), Robust Learning Algorithm with the Variable Learning Rate, ICAISC 2006, Artificial Intelligence and Soft Computing, pp. 83-90.
• 17. Senjyu T., Takara H. [et al.] (2002), One-Hour-Ahead Load Forecasting Using Neural Network, IEEE Transaction on Power Systems, Vol. 17, No. 1, pp. 113-118.
• 18. Stankovic A. M., Saric A. T., and Milosevic M. (2003), Identification of Nonparametric Dynamic Power System Equivalents with Artificial Neural Networks, IEEE Transactions on Power Systems, Vol. 18, No. 4, pp. 1478-1486.
• 19. Tchórzewski J. (2021) Metody sztucznej inteligencji i informatyki kwantowej w ujęciu teorii sterowania i systemów, Wydawnictwo UPH, Siedlce.
• 20. Wang F., Zhang Z, et all. (2019), Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting, Energy Conversion and Management, Volume 181, pp. 443-462.