Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
È¿À²ÀûÀΠž籤 ¹ßÀü·® ¿¹ÃøÀ» À§ÇÑ Dynamic Piecewise Àϻ緮 ¿¹Ãø ¸ðµ¨ |
¿µ¹®Á¦¸ñ(English Title) |
A Dynamic Piecewise Prediction Model of Solar Insolation for Efficient Photovoltaic Systems |
ÀúÀÚ(Author) |
¾çµ¿Çå
¿©³ª¿µ
¸¶Æò¼ö
Dong Hun Yang
Na Young Yeo
Pyeongsoo Mah
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¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 11 PP. 0632 ~ 0640 (2017. 11) |
Çѱ۳»¿ë (Korean Abstract) |
Àϻ緮Àº ž籤 ¹ßÀü½Ã½ºÅÛÀÇ Àü·Â »ý»ê·®¿¡ °¡Àå Å« ¿µÇâÀ» ¹ÌÄ¡´Â ±â»ó¿ä¼ÒÀ̸ç, ´Ù¸¥ ±â»ó¿ä¼Òµé°ú ´Þ¸® ±â»óûÀÇ Àϱ⿹º¸¸¦ ÅëÇØ Á¦°ø¹ÞÀ» ¼ö ¾ø´Ù. µû¶ó¼ È¿À²ÀûÀΠž籤 ¹ßÀü½Ã½ºÅÛ ¿î¿ëÀ» À§ÇØ Àϻ緮 ¿¹Ãø¿¡ °üÇÑ ¿¬±¸´Â ÇʼöÀûÀÌ´Ù. º» ¿¬±¸´Â ±â»óÁ¤º¸ µ¥ÀÌÅÍ ±â¹ÝÀÇ Dynamic Piecewise Àϻ緮 ¿¹Ãø ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. Dynamic Piecewise Àϻ緮 ¿¹Ãø ¸ðµ¨Àº À¯»çÇÑ Å¾ç°íµµ¿Í À¯»çÇÑ ³¯¾¾ÀÇ µ¥ÀÌÅÍ Á¶°¢µé·Î ³ª´©¾î ÇнÀÇϱâ À§ÇØ, ¿¹ÃøÇÏ´Â ½ÃÁ¡ÀÇ Å¾ç°íµµ¿Í ¿î·®À» ±âÁØÀ¸·Î Àüü µ¥ÀÌÅ͸¦ µ¿ÀûÀ¸·Î ³ª´« ÈÄ ±â°èÇнÀ ¾Ë°í¸®ÁòÀÎ ´ÙÁß ¼±Çüȸ±Í ¾Ë°í¸®ÁòÀ¸·Î ÇнÀÇÏ¿© Àϻ緮À» ¿¹ÃøÇϴµ¥ »ç¿ëµÈ´Ù. º» ¿¬±¸ÀÇ ¼º´ÉÀ» °ËÁõÇϱâ À§ÇØ Á¦¾È ¸ðµ¨ÀÎ Dynamic Piecewise Àϻ緮 ¿¹Ãø ¸ðµ¨°ú ÀÌÀü ¿¬±¸¿¡¼ Á¦¾ÈÇÑ ¸ðµ¨, ±âÁ¸ÀÇ »ó°ü°ü°è½Ä ±â¹Ý Àϻ緮 ¿¹Ãø ¸ðµ¨¿¡ µ¿ÀÏÇÑ ±â»óÁ¤º¸ µ¥ÀÌÅÍ ¼ÂÀ» Àû¿ëÇÏ¿© ºñ±³ÇÏ¿´À¸¸ç, ºñ±³°á°ú º» ¿¬±¸¿¡¼ Á¦¾ÈÇÑ ¸ðµ¨ÀÌ °¡Àå Á¤È®ÇÑ Àϻ緮 ¿¹Ãø ¼º´ÉÀ» º¸¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.
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Å°¿öµå(Keyword) |
ž籤 ¹ßÀü
Àϻ緮 ¿¹Ãø
Piecewise ¾Ë°í¸®Áò
±â»óÁ¤º¸
ºòµ¥ÀÌÅÍ
photovoltaic systems
prediction of solar insolation
piecewise algorithm
weather information
big data
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ÆÄÀÏ÷ºÎ |
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