DocumentCode :
259686
Title :
Incremental SVD for Insight into Wind Generation
Author :
Kamath, Chandrika ; Ya Ju Fan
Author_Institution :
Center for Appl. Sci. Comput., Lawrence Livermore Nat. Lab., Livermore, CA, USA
fYear :
2014
fDate :
3-6 Dec. 2014
Firstpage :
441
Lastpage :
446
Abstract :
In this paper, we formulate the problem of predicting wind generation as one of streaming data analysis. We want to understand if it is possible to use the weather data in a time window just before the current time to gain insight into how the wind generation might behave in a time interval just after the current time. Specifically, we use a singular value decomposition of the weather data, and how that the number of singular values and the largest singular value can be used to predict the magnitude of the change in the generation in the near future. The analysis uses an incremental algorithm based on a sliding window for reduced computational costs.
Keywords :
geophysics computing; singular value decomposition; wind power; change magnitude prediction; computational cost reduction; incremental SVD algorithm; singular value decomposition; sliding window; streaming data analysis; time interval; time window; weather data; wind generation prediction; Approximation methods; Matrix decomposition; Singular value decomposition; Vectors; Wind speed;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2014 13th International Conference on
Conference_Location :
Detroit, MI
Type :
conf
DOI :
10.1109/ICMLA.2014.77
Filename :
7033156
Link To Document :
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