Day-ahead prediction using time series partitioning with Auto-Regressive model

Show simple item record Kiplangat, Dennis Cheruiyot Drisya, G. V. Kumar, K. Satheesh 2016-11-22T05:36:16Z 2016-11-22T05:36:16Z 2016-08
dc.identifier.issn 0975-3397
dc.description.abstract Wind speed forecasting has received a lot of attention in the recent past from researchers due to its enormous benefits in the generation of wind power and distribution. The biggest challenge still remains to be accurate prediction of wind speeds for efficient operation of a wind farm. Wind speed forecasts can be greatly improved by understanding its underlying dynamics. In this paper, we propose a method of time series partitioning where the original 10 minutes wind speed data is converted into a twodimensional array of order (N x 144) where N denotes the number of days with 144 the daily 10-min observations. Upon successful time series partitioning, a point forecast is computed for each of the 144 datasets extracted from the 10 minutes wind speed observations using an Auto-Regressive (AR) process which is then combined together to give the (N+1)st day forecast. The results of the computations show significant improvement in the prediction accuracy when AR model is coupled with time series partitioning. en_US
dc.language.iso en en_US
dc.publisher International Journal on Computer Science and Engineering (IJCSE) en_US
dc.relation.ispartofseries Volume 8;Issue 8
dc.subject Wind speed forecasting; Auto-regressive models; Time series partitioning. en_US
dc.title Day-ahead prediction using time series partitioning with Auto-Regressive model en_US
dc.type Article en_US

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