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Wind speed forecasting has received a lot of attention in the recent pastfrom researchers due to its enormous benefits in the generation of wind powerand distribution. The biggest challenge still remains to be accurate predictionof wind speeds for efficient operation of a wind farm. Wind speed forecasts canbe greatly improved by understanding its underlying dynamics. In this paper, wepropose a method of time series partitioning where the original 10 minutes windspeed data is converted into a two-dimensional array of order (N x 144) where Ndenotes the number of days with 144 the daily 10-min observations. Uponsuccessful time series partitioning, a point forecast is computed for each ofthe 144 datasets extracted from the 10 minutes wind speed observations using anAuto-Regressive (AR) process which is then combined together to give the (N+1)st day forecast. The results of the computations show significant improvementin the prediction accuracy when AR model is coupled with time seriespartitioning.

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