2022
DOI: 10.3390/en15207482
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Feasibility Study on the Influence of Data Partition Strategies on Ensemble Deep Learning: The Case of Forecasting Power Generation in South Korea

Abstract: Ensemble deep learning methods have demonstrated significant improvements in forecasting the solar panel power generation using historical time-series data. Although many studies have used ensemble deep learning methods with various data partitioning strategies, most have only focused on improving the predictive methods by associating several different models or combining hyperparameters and interactions. In this study, we contend that we can enhance the precision of power generation forecasting by identifying… Show more

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Cited by 3 publications
(1 citation statement)
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“…Therefore, elements related to solar irradiance, such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI), have been used in many studies on forecasting the amount of solar PV power generation [1][2][3][4]. Factors affecting solar irradiance reaching the surface, such as sky condition, clearness index, visibility, cloud cover, and date, have also been investigated [5][6][7]. Furthermore, studies focusing on forecasting the amount of solar PV power generation are preceded by solar irradiance forecasting [8,9].…”
Section: Problem Statementmentioning
confidence: 99%
“…Therefore, elements related to solar irradiance, such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI), have been used in many studies on forecasting the amount of solar PV power generation [1][2][3][4]. Factors affecting solar irradiance reaching the surface, such as sky condition, clearness index, visibility, cloud cover, and date, have also been investigated [5][6][7]. Furthermore, studies focusing on forecasting the amount of solar PV power generation are preceded by solar irradiance forecasting [8,9].…”
Section: Problem Statementmentioning
confidence: 99%