Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine)
| dc.contributor.author | Miroshnychenko, Ihor | |
| dc.contributor.author | Мірошниченко, Ігор Вікторович | |
| dc.contributor.author | Мирошниченко, Игорь Викторович | |
| dc.contributor.author | Kravchenko, Тetiana | |
| dc.contributor.author | Кравченко (Лук’янець), Тетяна Володимирівна | |
| dc.contributor.author | Лук’янець, Тетяна Володимирівна | |
| dc.contributor.author | Drobyna, Yuliia | |
| dc.date.accessioned | 2024-04-01T10:48:54Z | |
| dc.date.available | 2024-04-01T10:48:54Z | |
| dc.date.issued | 2021 | |
| dc.description.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. | |
| dc.identifier.citation | Miroshnychenko I. Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine) / Ihor Miroshnychenko, Тetiana Kravchenko, Yuliia Drobyna // Нейро-нечіткі технології моделювання в економіці : наук.-анал. журн. / М-во освіти і науки України, ДВНЗ «Київ. нац. екон. ун-т ім. Вадима Гетьмана» ; [редкол.: А. В. Матвійчук (голов. ред.) та ін.]. – Київ : КНЕУ, 2021. – № 10. – С. 164–198. | |
| dc.identifier.doi | 10.33111/nfmte.2021.164 | |
| dc.identifier.issn | 2306-3289 | |
| dc.identifier.uri | https://ir.kneu.edu.ua/handle/2010/43389 | |
| dc.language.iso | en | |
| dc.publisher | ДВНЗ «Київський національний економічний університет імені Вадима Гетьмана» | |
| dc.subject | alternative energy | |
| dc.subject | forecasting | |
| dc.subject | Prophet model | |
| dc.subject | neural network autoregression | |
| dc.title | Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine) | |
| dc.type | Article |