Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models
DOI:
https://doi.org/10.24160/1993-6982-2020-6-119-128Keywords:
time series, prediction model, electricity price prediction, autoregression, regression analysisAbstract
The solution of problems concerned with predicting a free market price for electricity through constructing different prediction models is considered. In so doing, a shift is made from an analysis of conventional regression and auto-regression models of the moving average to the proposed combined multifactor models, which also include the time trend and dummy variables. This shift is partly justified by the specific behavior of the electricity price in the free market, which is caused by a strictly cyclic change of its value, e.g., proceeding from such attributes as the heating season, day of week, etc. The techniques of constructing combined prediction models has been developed to the level of elaborating effective computational procedures based on the Statistica and OsiSoft PI-System software packages. The application of the autoregressive and combined regression prediction models to the Russian market has demonstrated their fairly good effectiveness with an acceptable level of accuracy. A comparison of the achieved levels of accuracy provided by the competing models has not shown any advantages of the shift to the use of combined regression multifactor models in terms of achieving better prediction accuracy; however, their application for analyzing the influence of different factors on the predicted variable may become a fundamental advantage in selecting the type of prediction model. Despite their being limited to an analysis of the Belgorod region market, the obtained results demonstrate the achieved prediction accuracy that is as least as good, and in the main is even better than the majority of the data presented in the review of the results for European electricity markets. The article substantiates the advisability of studying the combined regression models as a tool for analyzing the influence of individual factors as components influencing the electricity price formation for the predicted period, given that the accuracy level of the combined regression models corresponds to the currently achieved electricity price prediction accuracy levels.
References
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18. Knittel C., Roberts M. An Empirical Examination of Restructured Electricity Prices // Energy Economics. 2005. V. 27. Pp. 791—817.
19. Contreras J., Espinola R., Nogales F., Conejo A. ARIMA Models to Predict Next-day Electricity Prices // IEEE Trans. Power Systems. 2003. V. 18. No. 3. Pp. 1014—2020.
20. Diongue A., Guégan D., Vignal B. Forecasting Electricity Spot Market Prices with a k-factor GIGARCH Process // Appl. Energy. 2009. V. 86. Pp. 505—510.
21. Gianfreda A., Grossi L. Forecasting Italian Electricity Zonal Prices with Exogenous Variables // Energy Economics. 2012. V. 34. Pp. 2228—2239.
22. База тарифов на электроэнергию. [Электрон. ресурс] www.time2save.ru/calculaters/nereguliruemie-ceni-na-elektroenergiu (дата обращения 05.02.2020).
---
Для цитирования: Шихина А.В., Ягодкина Т.В. Повышение точности предсказания цены электроэнергии за счет применения комбинированных моделей прогноза // Вестник МЭИ. 2020. № 6. С. 119—128. DOI: 10.24160/1993-6982-2020-6-119-128.
#
1. Sovremennaya Rynochnaya Elektroenergetika Rossiyskoy Federatsii. M.: Izd-vo Sovet Rynka, 2017. (in Russian).
2. Smirnova E.O. Reshenie Zadachi Prognozirovaniya Tsen na Elektroenergiyu na Rynke «na Sutki Vpered» Ekonometricheskimi Metodami. [Elektron. Resurs], www.olymp.msu.ru/archive/Lomonosov_2012/1946/44283_e645.pdf. (Data Obrashcheniya 05.02.2020). (in Russian).
3. Shikhin V.A., Pavlyuk G.P. Postroenie Oblastey Zadannogo Kachestva Upravleniya na Osnove Statisti-Cheskogo Podkhoda: Metod Otsekayushchikh Modeley. Mekhatronika, Avtomatizatsiya, Upravlenie. 2010;10:39—45. (in Russian).
4. Drobysh I.I. Statisticheskie Modeli Prognozirovaniya Tsen na Optovykh Rynkakh Elektroenergii: Rossiyskiy i Zarubezhnyy Opyt. Vestnik Yuzhno-Ural'skogo Gos. Un-ta. Seriya «Ekonomika i Menedzhment». 2017;11;3:41—54. (in Russian).
5. Shikhin V.A., Kochengin A.E., Pavliuk G.P. Significant Events Detection and Identification through Electrical Grid Load Profile. Proc. Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE), Casablanca, 2018:848—857.
6. Karakatsani N., Bunn D. Forecasting Electricity Prices: The Impact of Fundamentals and Time-varying Coef-ficients. Intern. J. Forecasting. 2008;24:764—785.
7. Weron R., Misiorek A. Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series Models. Ibid. 744—763.
8. Conejo A., Contreras J., Espinola R., Plazas M. Forecasting Electricity Prices for a Day-ahead Pool-based Electric Energy Market. Intern. J. Forecasting. 2005;21:435—462.
9. Tan Z., Zhang J., Wang J., Xu J. Day-ahead Electricity Price Forecasting Using Wavelet Transform Com-bined with ARIMA and GARCH Models. Appl. Energy. 2010;87:3606—3610.
10. Kim C., Yu I., Song Y. Prediction of System Marginal Price of Electricity Using Wavelet Transform Analysis. Energy Conversion and Management. 2002;43:1839—1851.
11. Nogales F., Contreras J., Conejo A., Espinola R. Forecasting Next-day Electricity Prices by Time Series Models. IEEE Trans. Power Systems. 2002;17;2:342—348.
12. García-Martos S., Rodríguez J., Sánchez M. Modelling and Forecasting Fossil Fuels, ????????2 and Electricity Prices and their Volatilities. Appl. Energy. 2013;101: 363—375.
13. Higgs H., Worthington A. Stochastic Price Modeling of High Volatility, Mean-reverting, Spike-prone Commodities: The Australian Wholesale Spot Electricity Market. Energy Economics. 2008;30:3172—3185.
14. Koopman S., Ooms M., Carnero M. Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices. J. American Statistical Association. 2007;102;477:16—27.
15. Cuaresma J., Hlouskova J., Kossmeier S., Obersteiner M. Forecasting Electricity Spot-prices Using Linear Univariate Time-series Models. Appl. Energy. 2004;77:87—106.
16. Liu H., Shi J. Applying ARMA–GARCH Approaches to Forecasting Short-term Electricity Prices. Energy Economics. 2013;37:152—166.
17. Garcia R., Contreras J., Akkeren M., Garcia J. A GARCH Forecasting Model to Predict Day-ahead Electrici-ty Prices. IEEE Trans. Power Syst. 2005;20;2:867—874.
18. Knittel C., Roberts M. An Empirical Examination of Restructured Electricity Prices. Energy Economics. 2005;27:791—817.
19. Contreras J., Espinola R., Nogales F., Conejo A. ARIMA Models to Predict Next-day Electricity Prices. IEEE Trans. Power Systems. 2003;18;3:1014—2020.
20. Diongue A., Guégan D., Vignal B. Forecasting Electricity Spot Market Prices with a k-factor GIGARCH Process. Appl. Energy. 2009;86:505—510.
21. Gianfreda A., Grossi L. Forecasting Italian Electricity Zonal Prices with Exogenous Variables. Energy Economics. 2012;34:2228—2239.
22. Baza Tarifov na Elektroenergiyu. [Elektron. Resurs] www.time2save.ru/calculaters/nereguliruemie-ceni-na-elektroenergiu (Data Obrashcheniya 05.02.2020). (in Russian).
---
For citation: Shikhina A.V., Yagodkina T.V. Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models. Bulletin of MPEI. 2020;6:119—128. (in Russian). DOI: 10.24160/1993-6982-2020-6-119-128.
2. Смирнова Е.О. Решение задачи прогнозирования цен на электроэнергию на рынке «на сутки вперед» эконометрическими методами. [Электрон. ресурс], www.olymp.msu.ru/archive/Lomonosov_2012/1946/44283_e645.pdf. (дата обращения 05.02.2020).
3. Шихин В.А., Павлюк Г.П. Построение областей заданного качества управления на основе статистического подхода: метод отсекающих моделей // Мехатроника, автоматизация, управление. 2010. № 10. С. 39—45.
4. Дробыш И.И. Статистические модели прогнозирования цен на оптовых рынках электроэнергии: российский и зарубежный опыт // Вестник Южно-Уральского гос. ун-та. Серия «Экономика и менеджмент». 2017. Т. 11. № 3. С. 41—54.
5. Shikhin V.A., Kochengin A.E., Pavliuk G.P. Significant Events Detection and Identification through Electrical Grid Load Profile // Proc. Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE), Casablanca, 2018. Pp. 848—857.
6. Karakatsani N., Bunn D. Forecasting Electricity Prices: The Impact of Fundamentals and Time-varying Coefficients // Intern. J. Forecasting. 2008. V. 24. Pp. 764—785.
7. Weron R., Misiorek A. Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series Models // Ibid. 744—763.
8. Conejo A., Contreras J., Espinola R., Plazas M. Forecasting Electricity Prices for a Day-ahead Pool-based Electric Energy Market // Intern. J. Forecasting. 2005. V. 21. Pp. 435—462.
9. Tan Z., Zhang J., Wang J., Xu J. Day-ahead Electricity Price Forecasting Using Wavelet Transform Combined with ARIMA and GARCH Models // Appl. Energy. 2010. V. 87. Pp. 3606—3610.
10. Kim C., Yu I., Song Y. Prediction of System Marginal Price of Electricity Using Wavelet Transform Analysis // Energy Conversion and Management. 2002. V. 43. Pp. 1839—1851.
11. Nogales F., Contreras J., Conejo A., Espinola R. Forecasting Next-day Electricity Prices by Time Series Models // IEEE Trans. Power Systems. 2002. V. 17. No. 2. Pp. 342—348.
12. García-Martos С., Rodríguez J., Sánchez M. Modelling and Forecasting Fossil Fuels, ????????2 and Electricity Prices and their Volatilities // Appl. Energy. 2013. V. 101. Pp. 363—375.
13. Higgs H., Worthington A. Stochastic Price Modeling of High Volatility, Mean-reverting, Spike-prone Commodities: The Australian Wholesale Spot Electricity Market // Energy Economics. 2008. V. 30. Pp. 3172—3185.
14. Koopman S., Ooms M., Carnero M. Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices // J. American Statistical Association. 2007. V. 102. No. 477. Pp. 16—27.
15. Cuaresma J., Hlouskova J., Kossmeier S., Obersteiner M. Forecasting Electricity Spot-prices Using Linear Univariate Time-series Models // Appl. Energy. 2004. V. 77. Pp. 87—106.
16. Liu H., Shi J. Applying ARMA–GARCH Approaches to Forecasting Short-term Electricity Prices // Energy Economics. 2013. V. 37. Pp. 152—166.
17. Garcia R., Contreras J., Akkeren M., Garcia J. A GARCH Forecasting Model to Predict Day-ahead Electricity Prices // IEEE Trans. Power Syst. 2005. V. 20. No. 2. Pp. 867—874.
18. Knittel C., Roberts M. An Empirical Examination of Restructured Electricity Prices // Energy Economics. 2005. V. 27. Pp. 791—817.
19. Contreras J., Espinola R., Nogales F., Conejo A. ARIMA Models to Predict Next-day Electricity Prices // IEEE Trans. Power Systems. 2003. V. 18. No. 3. Pp. 1014—2020.
20. Diongue A., Guégan D., Vignal B. Forecasting Electricity Spot Market Prices with a k-factor GIGARCH Process // Appl. Energy. 2009. V. 86. Pp. 505—510.
21. Gianfreda A., Grossi L. Forecasting Italian Electricity Zonal Prices with Exogenous Variables // Energy Economics. 2012. V. 34. Pp. 2228—2239.
22. База тарифов на электроэнергию. [Электрон. ресурс] www.time2save.ru/calculaters/nereguliruemie-ceni-na-elektroenergiu (дата обращения 05.02.2020).
---
Для цитирования: Шихина А.В., Ягодкина Т.В. Повышение точности предсказания цены электроэнергии за счет применения комбинированных моделей прогноза // Вестник МЭИ. 2020. № 6. С. 119—128. DOI: 10.24160/1993-6982-2020-6-119-128.
#
1. Sovremennaya Rynochnaya Elektroenergetika Rossiyskoy Federatsii. M.: Izd-vo Sovet Rynka, 2017. (in Russian).
2. Smirnova E.O. Reshenie Zadachi Prognozirovaniya Tsen na Elektroenergiyu na Rynke «na Sutki Vpered» Ekonometricheskimi Metodami. [Elektron. Resurs], www.olymp.msu.ru/archive/Lomonosov_2012/1946/44283_e645.pdf. (Data Obrashcheniya 05.02.2020). (in Russian).
3. Shikhin V.A., Pavlyuk G.P. Postroenie Oblastey Zadannogo Kachestva Upravleniya na Osnove Statisti-Cheskogo Podkhoda: Metod Otsekayushchikh Modeley. Mekhatronika, Avtomatizatsiya, Upravlenie. 2010;10:39—45. (in Russian).
4. Drobysh I.I. Statisticheskie Modeli Prognozirovaniya Tsen na Optovykh Rynkakh Elektroenergii: Rossiyskiy i Zarubezhnyy Opyt. Vestnik Yuzhno-Ural'skogo Gos. Un-ta. Seriya «Ekonomika i Menedzhment». 2017;11;3:41—54. (in Russian).
5. Shikhin V.A., Kochengin A.E., Pavliuk G.P. Significant Events Detection and Identification through Electrical Grid Load Profile. Proc. Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE), Casablanca, 2018:848—857.
6. Karakatsani N., Bunn D. Forecasting Electricity Prices: The Impact of Fundamentals and Time-varying Coef-ficients. Intern. J. Forecasting. 2008;24:764—785.
7. Weron R., Misiorek A. Forecasting Spot Electricity Prices: A Comparison of Parametric and Semiparametric Time Series Models. Ibid. 744—763.
8. Conejo A., Contreras J., Espinola R., Plazas M. Forecasting Electricity Prices for a Day-ahead Pool-based Electric Energy Market. Intern. J. Forecasting. 2005;21:435—462.
9. Tan Z., Zhang J., Wang J., Xu J. Day-ahead Electricity Price Forecasting Using Wavelet Transform Com-bined with ARIMA and GARCH Models. Appl. Energy. 2010;87:3606—3610.
10. Kim C., Yu I., Song Y. Prediction of System Marginal Price of Electricity Using Wavelet Transform Analysis. Energy Conversion and Management. 2002;43:1839—1851.
11. Nogales F., Contreras J., Conejo A., Espinola R. Forecasting Next-day Electricity Prices by Time Series Models. IEEE Trans. Power Systems. 2002;17;2:342—348.
12. García-Martos S., Rodríguez J., Sánchez M. Modelling and Forecasting Fossil Fuels, ????????2 and Electricity Prices and their Volatilities. Appl. Energy. 2013;101: 363—375.
13. Higgs H., Worthington A. Stochastic Price Modeling of High Volatility, Mean-reverting, Spike-prone Commodities: The Australian Wholesale Spot Electricity Market. Energy Economics. 2008;30:3172—3185.
14. Koopman S., Ooms M., Carnero M. Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices. J. American Statistical Association. 2007;102;477:16—27.
15. Cuaresma J., Hlouskova J., Kossmeier S., Obersteiner M. Forecasting Electricity Spot-prices Using Linear Univariate Time-series Models. Appl. Energy. 2004;77:87—106.
16. Liu H., Shi J. Applying ARMA–GARCH Approaches to Forecasting Short-term Electricity Prices. Energy Economics. 2013;37:152—166.
17. Garcia R., Contreras J., Akkeren M., Garcia J. A GARCH Forecasting Model to Predict Day-ahead Electrici-ty Prices. IEEE Trans. Power Syst. 2005;20;2:867—874.
18. Knittel C., Roberts M. An Empirical Examination of Restructured Electricity Prices. Energy Economics. 2005;27:791—817.
19. Contreras J., Espinola R., Nogales F., Conejo A. ARIMA Models to Predict Next-day Electricity Prices. IEEE Trans. Power Systems. 2003;18;3:1014—2020.
20. Diongue A., Guégan D., Vignal B. Forecasting Electricity Spot Market Prices with a k-factor GIGARCH Process. Appl. Energy. 2009;86:505—510.
21. Gianfreda A., Grossi L. Forecasting Italian Electricity Zonal Prices with Exogenous Variables. Energy Economics. 2012;34:2228—2239.
22. Baza Tarifov na Elektroenergiyu. [Elektron. Resurs] www.time2save.ru/calculaters/nereguliruemie-ceni-na-elektroenergiu (Data Obrashcheniya 05.02.2020). (in Russian).
---
For citation: Shikhina A.V., Yagodkina T.V. Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models. Bulletin of MPEI. 2020;6:119—128. (in Russian). DOI: 10.24160/1993-6982-2020-6-119-128.
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2020-02-25
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System Analysis, Management and Information Processing (05.13.01)
