  The share of wind power in fuel mixes worldwide has increased considerably.
The main ingredient when deriving wind power predictions are wind speed data;
the closer to the wind farms, the better they forecast the power supply. The
current paper proposes a hybrid model for predicting wind speeds at convenient
locations. It is then applied to Southern California power price area. We build
random fields with time series of gridded historical forecasts and actual wind
speed observations. We estimate with ordinary kriging the spatial variability
of the temporal parameters and derive predictions. The advantages of this work
are twofold: (1) an accurate daily wind speed forecast at any location in the
area and (2) a general method applicable to other markets.
