ÇѱÛÁ¦¸ñ(Korean Title) |
µ¥ÀÌÅÍ ½Ã°¢È ¹× Ž»öÀû µ¥ÀÌÅÍ ºÐ¼®À» ÅëÇÑ Å¾籤 ¿¡³ÊÁö ¿¹Ãø¿ë Ư¡º¤ÅÍ ÃßÃâ |
¿µ¹®Á¦¸ñ(English Title) |
Feature Vector Extraction for Solar Energy Prediction through Data Visualization and Exploratory Data Analysis |
ÀúÀÚ(Author) |
Á¤¿ø¼®
ÇÔ°æ¼±
¹Ú¹®±Ô
Á¤¿µÈ
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Wonseok Jung
Kyung-Sun Ham
Moon-Ghu Park
Young-Hwa Jeong
Jeongwook Seo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 21 NO. 02 PP. 0514 ~ 0517 (2017. 10) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In solar photovoltaic systems, power generation is greatly affected by the weather conditions, so it is essential to predict solar energy for stable load operation. Therefore, data on weather conditions are needed as inputs to machine learning algorithms for solar energy prediction. In this paper, we use 15 kinds of weather data such as the precipitation accumulated during the 3 hours of the surface, upward and downward longwave radiation average, upward and downward shortwave radiation average, the temperature during the past 3 hours at 2 m above from the ground and temperature from the ground surface as input data to the algorithm. We analyzed the statistical characteristics and correlations of weather data and extracted the downward and upward shortwave radiation averages as a major elements of a feature vector with high correlation of 70% or more with solar energy.
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Å°¿öµå(Keyword) |
Exploratory Data Analysis
Data Visualization
Solar Energy
Feature Vector
Prediction
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ÆÄÀÏ÷ºÎ |
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