Neuro-Fuzzy Modeling Techniques in Economics

Neuro-Fuzzy Modeling Techniques in Economics

Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine)

DOI:

10.33111/nfmte.2021.164

Анотація:
Abstract: Electricity generation forecasting is a common task that helps power generating companies plan capacity, minimize costs, and detect anomaly. Despite its importance, there are serious challenges associated with obtaining reliable and high-quality forecasts, especially when it comes to the newly created renewable electricity market.
A practical approach to predicting the generation of electricity from alternative sources in developing countries (on the example of Ukraine) based on the use of classical (ARIMA, TBATS) and modern (Prophet, NNAR) approaches is proposed.
The legal framework regulating the process of Ukraine's entry into the pan-European energy market and its functioning was analyzed: the weak points of the legislation on responsibility, the permissible accuracy of weather conditions data, and the lack of data on the monitoring infrastructure are indicated.
Among all the proposed alternatives, the Prophet model was the most accurate, since it allows you to simultaneously take into account several seasonalities (hourly, daily, weekly, monthly, and holidays). According to this, for an operational forecast (6 hours) the best model is the one that takes into account hourly seasonality, and for shortterm forecasts (24 and 48 hours) and medium-term forecast (72 hours) the most accurate models are those taking into account hourly, daily, weekly seasonality and weather conditions.
The obtained forecasts and model quality indicators approve the feasibility of applying the proposed approach and the constructed models that can be used in a wide range of economic problems.
Ключові слова:
Key words: alternative energy, forecasting, Prophet model, neural network autoregression
УДК:
UDC:

JEL: C45 C63 C88 Q42 Q47

To cite paper
In APA style
Miroshnychenko, I., Kravchenko, Т., & Drobyna, Y. (2021). Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine). Neuro-Fuzzy Modeling Techniques in Economics, 10, 164-198. http://doi.org/10.33111/nfmte.2021.164
In MON style
Мірошниченко І.В., Кравченко Т., Дробина Ю. Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine). Нейро-нечіткі технології моделювання в економіці. 2021. № 10. С. 164-198. http://doi.org/10.33111/nfmte.2021.164 (дата звернення: 16.09.2025).
With transliteration
Miroshnychenko, I., Kravchenko, Т., Drobyna, Y. (2021) Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine). Neuro-Fuzzy Modeling Techniques in Economics, no. 10. pp. 164-198. http://doi.org/10.33111/nfmte.2021.164 (accessed 16 Sep 2025).
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  1. Verkhovna Rada. (2017). Pro rynok elektrychnoi enerhii [On the electricity market] (Law of Ukraine 2019-VIII). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/2019-19 [in Ukrainian]
  2. Verkhovna Rada. (2003). Pro alternatyvni dzherela enerhii [On Alternative Energy Sources] (Law of Ukraine 555-IV). Retrieved February 7, 2021, from https://zakon.rada.gov.ua/laws/show/555-15 [in Ukrainian]
  3. Verkhovna Rada. (2005). Pro kombinovane vyrobnytstvo teplovoi ta elektrychnoi enerhii (koheneratsiiu) ta vykorystannia skydnoho enerhopotentsialu [On Combined Heat and Power (Cogeneration) and Waste Energy Potential] (Law of Ukraine 2509-IV). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/2509-15 [in Ukrainian]
  4. Verkhovna Rada. (2016). Pro Natsionalnu komisiiu, shcho zdiisniuie derzhavne rehuliuvannia u sferakh enerhetyky ta komunalnykh posluh [On National Commission for State Regulation of Energy and Utilities] (Law of Ukraine 1540-VIII). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/1540-19 [in Ukrainian]
  5. Verkhovna Rada. (2000). Pro pryrodni monopolii [On natural monopolies] (Law of Ukraine 1682-III). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/1682-14 [in Ukrainian]
  6. Verkhovna Rada. (2001). Pro zakhyst ekonomichnoi konkurentsii [On protection of economic competition] (Law of Ukraine 2210-III). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/2210-14 [in Ukrainian]
  7. Verkhovna Rada. (1991). Pro okhoronu navkolyshnoho pryrodnoho seredovyshcha [On environmental protection] (Law of Ukraine 1264-XII). Retrieved Fabruary 7, 2021, from https://zakon.rada.gov.ua/laws/show/1264-12 [in Ukrainian]
  8. Hong, T., Pinson, P., Wang, Y., Weron, R., Yang D., & Zareipour, H. (2020). Energy Forecasting: A Review and Outlook. IEEE Open Access Journal of Power and Energy, 7, 376-388. https://doi.org/10.1109/OAJPE.2020.3029979
  9. Harrou, F., & Sun, Y. (2020). Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems. IntechOpen. http://doi.org/10.5772/intechopen.85999
  10. Bangert, P. (2021). Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case Studies. Elsevier. https://doi.org/10.1016/C2019-0-00440-2
  11. Liu, H., & Chen, C. (2019). Data processing strategies in wind energy forecasting models and applications: A comprehensive review. Applied Energy, 249, 392–408. https://doi.org/10.1016/j.apenergy.2019.04.188
  12. Qian, Z., Pei, Y., Zareipour, H., & Chen, N. (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, 235, 939–953. https://doi.org/10.1016/j.apenergy.2018.10.080
  13. Blaga, R., Sabadus, A., Stefu, N., Dughir, C., Paulescu, M., & Badescu, V. (2019). A current perspective on the accuracy of incoming solar energy forecasting. Progress in Energy and Combustion Science, 70, 119–144. https://doi.org/10.1016/j.pecs.2018.10.003
  14. Liu, H., Chen, C., Lv, X., Wu, X., & Liu, M. (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195, 328–345. https://doi.org/10.1016/j.enconman.2019.05.020
  15. Mashlakov, A., Kuronen, T., Lensu, L., Kaarna, A., & Honkapuro, S. (2021). Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Applied Energy, 285, Article 116405. https://doi.org/10.1016/j.apenergy.2020.116405
  16. Rodríguez, F., Fleetwood, A., Galarza, A., & Fontán, L. (2018). Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control. Renewable Energy, 126, 855–864. https://doi.org/10.1016/j.renene.2018.03.070
  17. Zhou, H., Zhang, Y., Yang, L., Liu, Q., Yan, K., & Du, Y. (2019). Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism. IEEE Access, 7, 78063–78074. https://doi.org/10.1109/access.2019.2923006
  18. Ministry of Energy of Ukraine. (2017). Enerhetychna stratehiia Ukrainy na period do 2035 roku [Energy Strategy of Ukraine up to 2035] (Order of the Cabinet of Ministers of Ukraine of August 18, 2017 No. 605). http://mpe.kmu.gov.ua/minugol/control/uk/publish/article?art_id=245239564&cat_id=245239555 [in Ukrainian]
  19. National Power Company Ukrenergo. (2020). Vstanovlena potuzhnist enerhosystemy Ukrainy [Installed capacity of the power system of Ukraine]. Retrieved January 21, 2020, from https://ua.energy/vstanovlena-potuzhnist-energosystemy-ukrayiny/ [in Ukrainian]
  20. Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts. https://otexts.org/fpp2/
  21. De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771
  22. Taylor, S. J, & Letham, B. (2017). Forecasting at Scale. PeerJ Preprints, 5, Article e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2
  23. Hastie, T., & Tibshirani, R. (1987). Generalized Additive Models: Some Applications. Journal of the American Statistical Association, 82(398), 371–386. https://doi.org/10.2307/2289439
  24. Energy Map. (2016-2019). Hourly power balance of IPS of Ukraine [Data set]. Retrieved January 21, 2020, from https://map.ua-energy.org/en/resources/8998f2ed-379f-4fa9-9076-88782b32ee4f/
  25. Ukrainian Hydrometeorological Center. (2021). Weather archive [Data set]. Retrieved Fabruary 7, 2021, from https://meteo.gov.ua/en/33345/climate/climate_stations