Title :
Finding Motifs in Wind Generation Time Series Data
Author :
Kamath, C. ; Ya Ju Fan
Author_Institution :
Center for Appl. Sci. Comput., Lawrence Livermore Nat. Lab., Livermore, CA, USA
Abstract :
Wind energy is scheduled on the power grid using 0-6 hour ahead forecasts generated from computer simulations or historical data. When the forecasts are inaccurate, control room operators use their expertise, as well as the actual generation from previous days, to estimate the amount of energy to schedule. However, this is a challenge, and it would be useful for the operators to have additional information they can exploit to make better informed decisions. In this paper, we use techniques from time series analysis to determine if there are motifs, or frequently occurring diurnal patterns in wind generation data. Using data from wind farms in Tehachapi Pass and mid-Columbia Basin, we describe our findings and discuss how these motifs can be used to guide scheduling decisions.
Keywords :
power generation scheduling; power grids; time series; wind power plants; Tehachapi Pass; computer simulations; diurnal patterns; historical data; midColumbia Basin; motif finding; power grid; time 0 hour to 6 hour; time series analysis; wind energy; wind farms; wind generation time series data; Aggregates; Approximation methods; Clustering algorithms; Time series analysis; Wind forecasting; Wind power generation; Clustering; Motifs; Time Series Analysis; Wind Generation;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
DOI :
10.1109/ICMLA.2012.190
