Insolation prediction using air pollutants and meteorological variables

Hwang, Yeungeun; Kang, Da-Young; Na, Myung-Hwan; Yoon, Sanghoo

doi:10.7465/jkdi.2021.32.5.997

Cited by 2 publications

(2 citation statements)

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“…Hwang et al predicted the daily insolation by building rain forest, a generalization acceleration model, and extreme gradient boost (XG Boost) model using weather data and air pollutant data. In this research, the prediction performance of the optimum model is 0.979 of R-square [22]. Ekici constructed a least squares support vector machine (LS-SVM) model to predict the daily insolation using weather data.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Solar Insolation Using the Deep Learning Technique

Park

Hong

Yeon

et al. 2023

International Journal of Energy Research

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In this study, prediction performances of a regression model and deep learning-based predictive models were comparatively analyzed for the prediction of hourly insolation in regions located at the temperate climate and microthermal climate with high precipitation. Unlike linear regression models, artificial neural networks (ANN) and long short-term memory- (LSTM-) based models achieved reliable predictive performances with CV(RMSE) of 14.0% and 15.8%, respectively. This study proposed the direction of future research by improving the performance of predicting insolation at 1 hour after the current time-step, which has time-dependent characteristics, by utilizing insolation at 24 hours before the current time-step and insolation at the current time-step in addition to the forecasted weather data. In the proposed models, a large error occurred at sunrise and sunset times, suggesting the possibility of improving predictive performance by utilizing variables related to sunrise and sunset in the future. Along with Cheongju, the proposed model could properly predict the hourly insolation in other regions around the world. The results of predicting other regions derived slightly higher prediction errors than Cheongju. However, it is expected that it will be possible to predict the hourly insolation in other regions with better prediction performance if variables related to geographical location are additionally considered in the future.

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Section: Introductionmentioning

confidence: 99%

Predictive Model for Solar Insolation Using the Deep Learning Technique

Park

Hong

Yeon

et al. 2023

International Journal of Energy Research

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“…한반 도 강수량 군집에 관측지점 고도, 섬 지점, 극한 강수 관측값 과 같은 특성(feature)을 고려함에 따라 다른 군집 결과를 반환한 것은 적절한 특성을 고려하는 것이 중요하다는 것을 보여준다 (Lee and Kang, 2022). 이외 미세먼지와 관련하여 기계학습을 활용한 사례도 있었다 (Hwang et al, 2021;Kim et al, 2021) 1분 평균 풍향을 ( ∘ ≤  ≤  ∘ ), 1분 평균 풍속을  (ms -1 )로 할 때 1시간 합성 풍향 및 평균 풍속은 Eq. (1)과 같이 계산된다.…”

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Effect of Taebaek Mountains on the Spatial Distribution of PM10 and PM2.5 in Gangwon Province

Lee¹,

Kang²

2023

J. Korean Soc. Hazard Mitig

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The purpose of this study was to empirically confirm that the Taebaek Mountains are one of the primary causes of the spatial difference in the concentration of particulate matter between the west (Yeongseo) and east (Yeongdong) sides of the mountain range in Gangwon Province in the Korean peninsula. We investigated the effects of the Taebaek Mountains by analyzing data on PM10 and PM2.5 concentrations, wind direction, and wind speed, collected over seven years from 2015 to 2021 in the Yeongseo and Yeongdong regions. Our results demonstrated that wind direction and speed could be considered significant features in the aerial transport of PM10 and PM2.5. When the concentrations were higher than normal, the reduction effect of the Taebaek mountain range on PM10 was clear, while the effect on PM2.5 was not as obvious. When wind blows from west to east across the Taebaek Mountains, the wind speed may increase on the Yeongdong; in these cases, the concentrations of PM10 and PM2.5 were much lower than at the Yeongseo. Moreover, with these westerly winds, when the wind speed at Yeongseo exceeded 2 m/s, the distribution range of high concentrations of PM10 and PM2.5 was narrower at Yeongseo and Yeongdong than when the wind speed was weaker.

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Insolation prediction using air pollutants and meteorological variables

Cited by 2 publications

References 0 publications

Predictive Model for Solar Insolation Using the Deep Learning Technique

Predictive Model for Solar Insolation Using the Deep Learning Technique

Effect of Taebaek Mountains on the Spatial Distribution of PM10 and PM2.5 in Gangwon Province

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