ПРОГНОЗИРОВАНИЕ РАЗВИТИЯ СОЛНЕЧНОЙ ЭНЕРГЕТИКИ В РЕГИОНЕ

Title

Forecasting the solar energy development in the region

Автор(ы)

Б.В. Корнейчук

Author(s)

B.V. Korneychuk

DOI

10.5922/1994-5280-2020-3-2

Страницы/Pages

16-25

Статья

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Ключевые слова

энергетика региона, региональная экономика, энергетический переход, солнечная энергия, логистическая модель, прогнозирование.

Keywords

regional energy, regional economy, energy transition, solar energy, logistic model, forecasting.

Аннотация

Рассмотрена проблема прогнозирования мощности солнечной энергетики регионов и стран мира в контексте глобальной тенденции энергетического перехода. Актуальность проблемы обусловлена тем, что солнечная энергетика играет ключевую роль в процессе замещении традиционных источников энергии возобновляемыми источниками. Однако прогнозы развития солнечной энергетики обычно характеризуется значительными ошибками, что требует разработки надежных методов прогнозирования. Для решения данной задачи предложен мульти-трендовый подход к построению региональной функции роста мощности солнечной энергетики, основанный на моделировании роста мощности в виде линейной комбинации логистического, линейного и экспоненциального трендов с весовыми коэффициентами, обратно пропорциональными отклонениям трендов от фактических данных. На основе метода были составлены прогнозы мощности солнечной энергетики для ряда регионов и стран мира на период 2017–2019 гг., которые показали высокую надежность, разработан прогноз на период 2020–2023 гг. Показано, что причиной существенных ошибок прогнозов часто служит использование логистической кривой в качестве универсального инструмента анализа без учета особенностей региона. Так, причиной систематического занижения прогнозов для Китая служит игнорирование экспоненциального компонента роста мощности солнечной энергетики.

Abstract (summary)

The problem of forecasting the dynamics of the development of solar energy in the region in the context of the global trend of energy transition is considered. The urgency of the problem is due to the fact that forecasts of the development of solar energy are usually characterized by relatively large errors. To solve this problem, the author proposed a multi-trend approach to constructing a regional function for the growth of solar power capacity. The method is based on the description of the dynamics of power growth in the form of the average value of logistic, linear and exponential trends. The weighting factors are equal to values that are inversely proportional to the errors of the corresponding trends. Based on this method, forecasts of solar energy capacity were calculated for Africa, Asia, Europe, North America and South America for the period 2017–2019. The validity of the method is confirmed by the fact that these forecasts are characterized by a relatively low deviation from the actual data. The author has developed a forecast for these regions for the period 2020-2023. It is shown that the reason for the low reliability of most forecasts is the desire to use the logistic curve as a universal analysis tool. This approach absolutes the logistics trend and does not take into account the specifics of the region. However, for some regions, a linear or exponential trend can serve as the dominant growth trend in solar energy capacity. In particular, the reason for the systematic underestimation of forecasts for China was the ignorance of the exponential component of the growth of solar energy capacity.

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References

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16. Kwasnicki W. Logistic growth of the global economy and competitiveness of nations. Technological Forecasting & Social Change, 2013, vol. 80, pp. 50–76.

17. Madsen D., Hansen J. Outlook of solar energy in Europe based on economic growth characteris­tics. Renewable and Sustainable Energy Reviews, 2019, vol. 114. URL: https://doi.org/10.1016/j. rser.2019.109306 [Accessed 5.08.2020].

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22. Platzer M.D. US solar photovoltaic manufacturing: Industry trends, Ggobal competition, federal sup­port, 2012. Congressial Research Service. URL: www.crs.gov [Accessed 5.08.2020].

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24. Sohoni V., Gupta S., Nema R. A critical review on wind turbine power curve modeling techniques and their applications in wind based energy systems. Journal of Energy, 2016. URL: https://doi. org/10.1155/2016/8519785 [Accessed 5.08.2020].

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