TY - GEN
T1 - Correlation and local feature based cloud motion estimation
AU - Huang, Hao
AU - Yoo, Shinjae
AU - Yu, Dantong
AU - Huang, Dong
AU - Qin, Hong
PY - 2012
Y1 - 2012
N2 - Short-term changes in atmospheric transmissivity caused by clouds can engender more severe fluctuations in photovoltaic (PV) outputs than those from traditional power plants. As PV energy continues to penetrate the U. S. National Energy Grid, such volatility increasingly lowers its reliability, efficiency, and value-added contribution. Therefore a model that can accurately predict the cloud motion and its affect on PV system's production is in a pressing demands. It can be used to mitigate the undesired behavior beforehand. In this paper we explore the use of Total Sky Images and the cloud estimation techniques based on such images. To further improve estimation quality of motion vector, we propose a novel hybrid algorithm taking the advantages of both correlation based and local feature based approaches. Our proposed hybrid approach significantly reduces the cloud motion prediction error rate by 25% on average, which can help to predict short term solar energy frustration in our later work.
AB - Short-term changes in atmospheric transmissivity caused by clouds can engender more severe fluctuations in photovoltaic (PV) outputs than those from traditional power plants. As PV energy continues to penetrate the U. S. National Energy Grid, such volatility increasingly lowers its reliability, efficiency, and value-added contribution. Therefore a model that can accurately predict the cloud motion and its affect on PV system's production is in a pressing demands. It can be used to mitigate the undesired behavior beforehand. In this paper we explore the use of Total Sky Images and the cloud estimation techniques based on such images. To further improve estimation quality of motion vector, we propose a novel hybrid algorithm taking the advantages of both correlation based and local feature based approaches. Our proposed hybrid approach significantly reduces the cloud motion prediction error rate by 25% on average, which can help to predict short term solar energy frustration in our later work.
UR - http://www.scopus.com/inward/record.url?scp=84866007419&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866007419&partnerID=8YFLogxK
U2 - 10.1145/2343862.2343863
DO - 10.1145/2343862.2343863
M3 - Conference contribution
AN - SCOPUS:84866007419
SN - 9781450315562
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1
EP - 9
BT - Proceedings of the 12th International Workshop on Multimedia Data Mining, MDMKDD'12 - Held in Conjunction with SIGKDD'12
T2 - 12th International Workshop on Multimedia Data Mining, MDMKDD 2012 - Held in Conjunction with SIGKDD 2012
Y2 - 12 August 2012 through 12 August 2012
ER -