  The widespread utilisation of grid-integrated wind electricity necessitates
accurate and reliable wind speed forecasting to ensure stable grid and quality
power. Machine learning algorithm based wind speed forecasting models are
getting increased attention in the literature owing to its superior ability to
learn by effectively capturing the changing patterns from the data. Most of the
reported wind forecasting models built on machine learning algorithms are
location specific and tested against data adjacent to the training data. In
this work, we develop the machine learning based wind speed forecasting models
and analyse their performance when applied to data from different cross-
locations up to a year ahead. Two distinct machine learning models based on
Support Vector Machine (SVM) and Random Forest (RF) algorithms have been
developed and tested separately for a relatively large geographical area. The
results of analysis of 1-hour forecasts obtained at various cross-locations and
points of time up to a year ahead show 80% of predictions within a Root Mean
Square Error (RMSE) of 1.5 m/s, 95% within 2.5 m/s and 98% within an RMSEof 3.5
m/s. The 75% of 2-hour predictions are within RMSE of 1.5 m/s, 16-hour
predictions within RMSE of 2.5 m / s and 48-hour predictions within RMSE of 3.5
m/s. When applied to thesame location of training data, the models generate
reliable forecasts for periods up to 22 hours, with the added advantage that
the models perform consistently throughout the year ahead horizon, independent
of the lead time from the training data. The output of the analysis is highly
promising to the wind energy industry in wind forecasting for locations where
historical wind speed data are not available for model building and training.
