Modeling Solar Irradiance in Tajikistan with XGBoost Algorithm

Jeongdu Noh; Taeyoo Na; Seong-Seung Kang

doi:10.9720/kseg.2023.3.403

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2023 Vol.33, Issue 3 Preview Page Next Page

Research Article

Modeling Solar Irradiance in Tajikistan with XGBoost Algorithm XGBoost를 이용한 타지키스탄 일사량 예측 모델

30 September 2023. pp. 403-411

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Abstract

The possibility of utilizing radiant solar energy as a renewable energy resource in Tajikistan was investigated by assessing solar irradiance using XGBoost algorithm. Through training, validation, and testing, the seasonality of solar irradiance was clear in both actual and predicted values. Calculation of hourly values of solar irradiance on 1 July 2016, 2017, 2018, and 2019 indicated maximum actual and predicted values of 1,005 and 1,009 W/m², 939 and 997 W/m², 1,022 and 1,012 W/m², 1,055 and 1,019 W/m², respectively, with actual and predicted values being within 0.4~5.8%. XGBoost is thus a useful tool in predicting solar irradiance in Tajikistan and evaluating the possibility of utilizing radiant solar energy.

Keywords

Tajikistan

XGBoost

solar energy

solar irradiance

renewable energy

본 연구는 XGBoost를 이용하여 타지키스탄의 일사량을 예측하여 타지키스탄의 재생에너지 자원으로서 복사 태양에너지의 활용 가능성을 평가하기 위함이다. 첫째, 타지키스탄의 일사량을 훈련모델, 검증모델, 시험모델을 통해 예측한 결과, 시간과 계절에 따른 일사량의 계절성이 실제값과 예측값 모두에서 뚜렷하게 구분되는 것을 확인하였다. 둘째, 타지키스탄의 2016, 2017, 2018, 2019년 등 각 연도의 7월 1일 시간당 일사량의 실제값과 예측값을 계산한 결과, 2016년 일사량의 최대 실제값과 예측값은 약 1,005 W/m²과 1,009 W/m², 2017년에는 939 W/m²과 997 W/m², 2018년에는 1,022 W/m²과 1,012 W/m², 2019년에는 1,055 W/m²과 1,019 W/m²으로 나타났으며, 실제값과 예측값의 오차가 약 0.4~5.8%로 매우 비슷한 결과를 보였다. 결과적으로 타지키스탄의 일사량을 예측하여 복사 태양에너지의 활용 가능성을 평가하는 데 있어 XGBoost가 매우 유용한 도구로 활용될 수 있을 것으로 판단된다.

키워드

타지키스탄

XGBoost

태양에너지

일사량

재생에너지

References

An, K.M., 2021, Developing a prediction model for firm innovation and performance using statistical matching and machine learning ensemble techniques, Doctoral Dissertation, Dongguk University, 98-105 (in Korean with English abstract).

Chen, T., Guestrin, C., 2016, XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17 August 2016, San Francisco, CA, USA, 785-794. 10.1145/2939672.2939785

IEA (International Energy Agency), 2022, Tajikistan 2022: Energy sector review, 134p.

Joint Ministries, 2022, Tajikistan country partnership strategy.

Kudusov, M.A., Madvaliev, U., Elistratov, V.V., 2021, Evaluation of the efficiency of already existing network solar photovoltaic plants operating 24/7/365 in low-voltage power supply systems of social facilities in the city of Dushanbe, Applied Solar Energy, 57(4), 323-332. 10.3103/S0003701X21040071

Na, T., Noh, J., Kim, H., Kang, S.S., 2023, Analysis of climate, weather, solar radiation and solar energy in major cities of Tajikistan, The Journal of Engineering Geology, Submitted (in Korean with English abstract). 10.9720/kseg.2023.3.389

Pfenninger, S., Staffell, I., 2016, Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data, Energy, 114, 1251-1265. 10.1016/j.energy.2016.08.060

Renewables.ninja, 2023, Meteorological data for Tajikistan, Retrieved from https://renewables.ninja/.

Yoon, H.R., 2020, A empirical study on the financial stability prediction model of South Korea's public enterprises with machine learning techniques, Doctoral Dissertation, Hansung University, 36-43 (in Korean with English abstract).

Information

Publisher :Korean Society of Engineering Geology
Publisher(Ko) :대한지질공학회
Journal Title :The Journal of Engineering Geology
Journal Title(Ko) :지질공학
Volume : 33
No :3
Pages :403-411
Received Date : 2023-07-17
Revised Date : 2023-08-20
Accepted Date : 2023-08-31
DOI :https://doi.org/10.9720/kseg.2023.3.403

The Journal of Engineering Geology 지질공학 ISSN:1226-5268(Print) 2287-7169(Online)

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