We study the problem of short term wind speed prediction,
which is a critical factor for effective wind power generation.
This is a challenging task due to the complex and stochastic behavior
of the wind environment. Observing various periods in the wind
speed time series present different patterns, we suggest a nonlinear
adaptive framework to model various hidden dynamic processes.
The model is essentially data driven, which leverages non-parametric
Heteroscdastic Gaussian Process to model relevant patterns for short
term prediction. We evaluate our model on two different real world
wind speed datasets from National Data Buoy Center. We compare
our results to state-of-arts algorithms to show improvement in terms
of both Root Mean Square Error (RMSE) and Mean Absolute Percentage
Xiaoqian Jiang, Bing Dong, Le Xie, and Latanya Sweeney. Adaptive Gaussian Process for Short-TermWind Speed Forecasting. The 19th European Conference on Artificial Intelligence (ECAI 2010). (PDF).