Оценка выбросов CO2 в странах Центральной Азии с применением искусственного интеллекта

Балжан Азибек ^а , Андрей Билощицкий ^а , Мохаммад Алхуи Назари ^б , Нурхат Жакиев ^а*

^a Astana IT University, проспект Мангилик Ел, 55/11, Бизнес-центр ЭКСПО, блок С1, Астана, 010000, Казахстан
^b Тегеранский университет, площадь Энгелаб, улица Азар 16, Тегеран, 1417935840, Иран

Email: nzhakiyev@gmail.com

Балжан Азибек: balzhan.azibek@astanait.edu.kz; Андрей Билощицкий: a.b@astanait.edu.kz; Мохаммад Алхуи Назари: nazari.mohammad.a@ut.ac.ir

https://doi.org/10.29258/CAJSCR/2024-R1.v3-2/1-23.eng

30 сентября, 2024

Аннотация

Энергопотребление и доли различных видов энергии в общем объеме энергоснабжения играют решающую роль в объеме выбросов CO₂ в различных странах.  Помимо факторов, связанных с энергетикой, такие экономические показатели, как валовой внутренний продукт (ВВП), также могут влиять на выбросы.  В настоящем исследовании доли различных источников энергии в общем энергоснабжении и ВВП были использованы в качестве вводных данных для модельной оценки выбросов CO₂ в трех государствах Центральной Азии, а именно в Казахстане, Узбекистане и Туркменистане.  В дополнение к результатам моделирования в статье также описываются важные характеристики энергетических систем целевых стран.  На основе сравнения выбросов CO₂ на единицу ВВП исследование позволило выявить, что данный индекс в целевых странах превышает среднемировое значение, что обусловливает необходимость принятия срочных мер по сокращению выбросов.  Исходные данные для исследования были получены от Международного энергетического агентства (МЭА) и Всемирного банка.  В исследовании были применены следующие методы: групповой метод обработки данных (Group Method of Data Handling, GMDH) и метод многослойного перцептрона (Multilayer Perceptron, MLP).  Обе модели показали хорошие результаты в оценке выбросов, однако в соответствии с расчетными значениями для примененных критериев был сделан вывод о том, что использование GMDH дает более точные результаты по сравнению с MLP.  Средние абсолютные относительные отклонения моделей GMDH и MLP составили приблизительно 3,69% и 4,28% соответственно.  Значения R² для упомянутых моделей составили 0,9936 и 0,9929 соответственно.  В большинстве случаев для обеих моделей относительные отклонения между прогнозируемыми и фактическими выбросами CO₂ находились в диапазоне ± 5%.

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Доступно на английском

Для цитирования: Azibek, B., Biloshchytskyi, A., Nazari, M., Zhakiyev, M. (2024). CO2 emission estimation in Central Asian countries by use of artificial intelligent methods. Central Asian Journal of Sustainability and Climate Research. https://doi.org/10.29258/CAJSCR/2024-R1.v3-2/1-23.eng

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