Powering Up With Space-lime Wind Forecasting

The technology to harvest electricity from wind energy is now advanced enough to make enure cities powered by it a reality High-quality, short-term forecasts of wind speed are vital to making this a more reliable energy source Gneiting et at (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal in The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution Is sharp. that is, highly concentrated around Its center However, this model is split into nonunique regimes based on the wind direction at an offsite location This paper both generalizes and Improves upon this model by treating wind direction as a circular variable and including it in the model It is robust in many experiments. such as predicting wind at other locations We compare this with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output This proposed loss measure yields more insight into the true value of each model's predictions