Correlation and local feature based cloud motion estimation

Hao Huang, Shinjae Yoo, Dantong Yu, Dong Huang, Hong Qin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th International Workshop on Multimedia Data Mining, MDMKDD'12 - Held in Conjunction with SIGKDD'12
Pages1-9
Number of pages9
DOIs
StatePublished - Sep 14 2012
Externally publishedYes
Event12th International Workshop on Multimedia Data Mining, MDMKDD 2012 - Held in Conjunction with SIGKDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 12 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other12th International Workshop on Multimedia Data Mining, MDMKDD 2012 - Held in Conjunction with SIGKDD 2012
Country/TerritoryChina
CityBeijing
Period8/12/128/12/12

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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